A Fast, Practical Algorithm for the Trapezoidation of Simple Polygons submitted to CISST 05 by:

Size: px
Start display at page:

Download "A Fast, Practical Algorithm for the Trapezoidation of Simple Polygons submitted to CISST 05 by:"

Transcription

1 A Fast, Practcal Algorthm for the Trapezodaton of Smple Polygons submtted to CISST by: Dr. Thomas F. Han (presenter) School of Computer & Informaton Scences Unversty of South Alabama Moble, Al 6688 Phone: -6-6 FAX: -6- E-mal: Dr. Davd D. Langan,III School of Computer & Informaton Scences Unversty of South Alabama Moble, Al 6688 Phone: -6-6 FAX: -6- E-mal:

2 A Fast, Practcal Algorthm for the Trapezodaton of Smple Polygons Thomas F. Han, Davd D. Langan, III Abstract A fast, practcal, determnstc algorthm for the horzontal trapezodaton of smple polygons s presented. The polygon s decomposed nto a mnmal collecton of trapezod sequences, such that two trapezods adjacent wthn a sequence always share a common horzontal border. Such trapezod sequences are a convenent data structure n a dsplay lst for a collecton of polygonal objects to be flled/rendered. A lnear traversal of the polygon outlne dentfes a subset of crtcal vertces, whch are then processed n sweep order. Horzontal as well as vertcal edges very common n practcal polygons are handled explctly. Complexty s lnear for most practcal polygons, wth non-lnear runnng tmes beng requred only for much less frequently occurrng geometres. A straghtforward extenson allows trapezodaton of smple polygons wth holes. Index Terms Computatonal geometry, renderng (computer graphcs), trapezodaton, polygon decomposton. I. INTRODUCTION Decomposng a smple polygon has been one of the most challengng problems n two-dmensonal computatonal geometry. It s a basc prmtve n computer graphcs and, generally seems the natural preprocessng step for most nontrval operatons on smple polygons [[],[]]. The goal of polygonal decomposton s to break a gven smple planar polygon nto a set of standard two-dmensonal shapes (most often nonoverlappng trangles or trapezods) wthout addng any new vertces. Decompostons nto trapezods and trangles are equvalent n terms of computatonal complexty, snce one problem can be reduced to the other n lnear tme [[]]. Decompostons are often used to reduce problems nvolvng complcated regons to problems, nvolvng prmtve shapes, that are generally easer to solve. Defnton: A smple polygon s the regon of a plane bounded by a fnte collecton of lne segments formng a smple closed curve. Let v, v,,v be n n ponts n the plane wth a cyclc orderng. Let e = ( v, v),, e = ( v, v+ ), en be n edges connectng the ponts (vertces). Then these edges bound a polygon ff [[]] Manuscrpt receved March,. T. F. Han, and D. D. Langan, are wth the School of Computer & Informaton Scences, Unversty of South Alabama, Moble, AL 6688 USA (phone: -6-6; e-mal: than@usouthal.edu, dlangan@usouthal.edu).. The ntersecton of each par of edges adjacent n the cyclc orderng s the sngle vertex shared between them: e e+ = v+, for all =,, n.. Nonadjacent edges do not ntersect: e ej = φ, for all j +. In partcular, the storng and renderng of flled polygons n computer graphcs often requres such a decomposton. In the case of trapezodaton, t s further convenent to organze the trapezods nto a mnmal collecton of trapezod sequences so that common edges of adjacent trapezods can be represented more compactly. In fact, each trapezod sequence covers a monotone polygon component of the general smple polygon. Snce monotone polygons are easy to resolve nto trapezods [[]], the general trapezodaton problem could be approached as one of decomposng a general polygon nto monotone polygons, whch are then further decomposed nto the consttuent trapezods. Ths s the approach taken here, wth the output beng a collecton of horzontal trapezods (parttoned nto a set of trapezod sequences.) Defnton: A trapezod sequence s an ordered lst of horzontal trapezods ( t, t,, tk ) such that bottom top bottom top {( y = y+ ) ( x x+ φ) =,, k }, bottom where y s the y-coordnate of the bottom of the bottom th trapezod, and x s the range of x-coordnates covered by the bottom edge of the th trapezod. That s, the bottom edge of a trapezod overlaps the top edge of the next trapezod. Defnton: A horzontal decomposton of a polygon nto trapezod sequences s a set of trapezod sequences such the unon of all trapezods n all trapezod sequences s the trapezodaton of the polygon. That s, the horzontal decomposton s a (generally non-unque) parttonng of trapezods nto a collecton of trapezod sequences. As an example of the need for a fast algorthm for the trapezodaton of polygons, consder the feld of prnter graphcs. A prnted page contans ether geometrc flled shapes or btmaps. All geometrcally defned shapes are typcally specfed by areas bounded by conc, Bézer, or straght-lne segments. Curves are then approxmated to a seres of straght-lne segments by a process called flattenng the curve. Thus, geometrc shapes to be flled are almost always descrbed by polygons. The polygons that ultmately wll consttute a prnted page are then converted nto trapezod sequences that are stored n a dsplay lst. Ths dsplay lst s a data structure, whch s a spatally compressed representaton of the page that

3 can be effcently stored, reused, and rendered. It s clear that the speed of the trapezodaton algorthm wll have an mpact on prnter page output rates. Currently, fllng polygons s typcally done scan-lne by scan-lne usng an actve edge table to keep track of scan-lne spans nteror to the polygon. Ths s an mage precson algorthm, and depends on the specfc resoluton of the devce, and requres a great deal of processng, specally n the case of hgh-resoluton dsplay devces where a polygon can span a large number of scan lnes. Object precson algorthms, such as the algorthm descrbed here, have the advantage of beng devce resoluton ndependent. Secton II of ths paper gves a bref revew of exstng approaches to ths problem whle secton III descrbes the algorthm. Secton IV gves a proof outlne, and secton V gves a complexty analyss. Secton VI llustrates the used mplementaton approach, whle Secton VII gves comparatve performance results. II. PREVIOUS WORK Quadratc trangulaton algorthms have been mplct n proofs snce Lennes paper [[]]. In 8, Garey, Johnson, Preparata, and Tarjan [[8]] trangulated a smple polygon by decomposng t nto monotone polygons and then decomposng each of these separately nto trangles usng an algorthm also descrbed n that paper. The decomposton nto monotone polygons uses the regularzaton procedure ntroduced by Lee and Preparata [[]]. Ths procedure has n complexty, where n s the number of vertces, and adds to the polygon non-ntersectng dagonals, whch do not cross the polygon boundary. A 8 algorthm by Chazelle [[]] s partcularly easy to mplement. It fnds a dagonal of the polygon P that dvdes t nto two polygons P and P, such that the number of vertces n P and P are each less than P +. The algorthm fnds such a dagonal n Ο ( P ) tme. A smple dvde-and-conquer algorthm based on ths technque yelds a total complexty of n. A 8 paper by Hertel and Mehlhorn [[]] combned the steps of the algorthm of Garey et al. [[8]] nto one sweep to yeld an Ο algorthm. They then mproved t to Ο( nlog r) ( nlog n), where r s the number of concave angles (nternal angles >8 ) wthn the nput polygon, by restrctng the sweep event ponts to only O(r) ponts of the polygon. The sweep lne also s no longer a smple straght lne. A crooked lne s vared, snce some ponts do not get processed untl the actual sweep lne s well past them. Ths algorthm works even f the smple nput polygon has polygonal holes. Lke that of Hertel and Melhorn [[]], Chazelle and Incerp s 8 algorthm [[]] depends on the geometrc complexty of the polygon. Any smple polygon can be decomposed nto alternatng sequences of clockwse and counterclockwse spralng chans: they defne snuosty, s, as the number of such chans. The complexty of ther algorthm s s. All attempts to start wth the monotonzaton or dagonal splttng path faled untl 8, when Fourner and Montuno [[]] showed the equvalence of trapezodal decomposton wth trangulaton for smple polygons, and as a result, many recent efforts have been concentrated on polygon trapezodaton. Ther determnstc sweep algorthm constructs vertcal trapezods defned by two non-adjacent vertces of the nput n. Once trapezodalzed, P s polygon, P, n tme broken nto monotone polygons and trangulated by jonng the trapezod defnng vertces. Clarkson, along wth Tarjan [[6]] n 8 devsed a randomzed algorthm based on the dvde-and-conquer strategy that fnds the vsblty partton of the polygons wth respect to a random subset of edges. The polygon s then recursvely subdvded about that partton nto smaller polygons usng * Jordan sortng. The algorthm runs n expected Ο ( nlog n) can also be used to check whether or not a gven polygon s smple. In, Krkpatrck, Klawe, and Tarjan [[]] presented an log n algorthm employng much smpler data structures. In addton, ther algorthm can be modfed to run n Ο * ( nlog n) ( log n) In, Sedel [[]] ntroduced a randomzed ncremental algorthm to compute the horzontal vsblty map (trapezo- * daton) of a smple polygon. It has a complexty On ( log n),. It f the coordnates of the ponts of the polygon are ntegers bounded by a fxed polynomal n n. The basc dea s the same as n Tarjan s 88 paper, but the trangle splttng s acheved wthout any Jordan sortng or other complcated data structures. For bounded nteger coordnates, ths algorthm bulds a data structure n lnear tme that can answer queres about horzontal neghbors n Ο. but s much smpler to mplement than that of Clarkson and Tarjan. In fact, he asserts that ts mplementaton smplcty s a property that very few, f any, of the algorthms mentoned can clam. Also n, Bernard Chazelle [[]] dscovered a lnear tme, determnstc algorthm that settled the queston about the ntrnsc computatonal complexty of trangulaton once and for all. However, the algorthm s accordng to Toussant [[]] unmplementable, and accordng to O Rourke [[]] contans detals [that] are formdable. Ths algorthm has, to the authors knowledge, never been mplemented. III. CURRENT TRAPEZOIDATION ALGORITHM The presented algorthm has a precondton that the polygon s smple, and (n the current descrpton) that the vertces are gven n clockwse order around the polygon startng at some arbtrary vertex. It wll also be assumed that unnecessary nlne vertces have been prevously removed by a smple, lnear, preprocessng stage.

4 Vertex coordnates are snapped to an arbtrarly fne (wthn the representaton) grd to allow drect coordnate comparson wthout havng to worry about round-offs. A. Prelmnares To help n the dscusson of the algorthm, some termnology s now defned: Defnton: A chan C = ( v, v,, vp ) s a planar straght-lne graph wth vertex set { v, v,, v p } and edge set {( v, v+ ) =,, p }. [[8]] Defnton: A chan C = ( v, v,, vp ) s sad to be monotone relatve to a gven lne l f a lne orthogonal to l ntersects C at exactly one pont. That s, the orthogonal projectons {( lv), lv ( ), l( v p )} of the vertces of C on l are ordered as lv ( ),( lv),,( lvp ). [[8]] We extend the orderng to be nonstrct usng ether the or relatons. Defnton: A fallng chan s a maxmal (n the sense that t ncludes any horzontal edges at ether end) chan that s monotone relatve to a vertcal lne, and havng non-ncreasng y-coordnates. A fallng chan has the property DOWNTORIGHT f the lower end s to the rght of the upper end. A rsng chan s nonmaxmal, but s otherwse smlarly defned. Defnton: A polygon s monotone f ts boundary s composed of exactly two non-ntersectng monotone chans relatve to the same lne. For example, a polygon s vertcally monotone f ts boundary s composed of two vertcally monotone chans: the polygon s left chan and rght chan. In ths case, each chan termnates at the polygon s uppermost vertex (assumng no horzontal edges at the top and bottom) and lowermost vertex and contans zero or more vertces n between. [[]] Defnton: A fallng chan s splt nto two downchans at the rghtmost vertex (wth tes beng broken by takng the lower vertex of a vertcal edge). If the rght extremum s at ether end of the fallng chan, one of the down-chans s null. Defnton: Smlarly, a rsng chan s splt nto two up-chans at the leftmost vertex (wth tes beng broken by takng the upper vertex of a vertcal edge). Agan, f the left extremum s at ether end of the rsng chan, one of the up-chans s null. Defnton: A jon vertex, or smply a jon, s a vertex that connects (.e., s an element of adjacent) up-chans and/or down-chans. That s, a jon vertex v C n +,, + m k k = C + k k +, where C and C are two successve up[down] chans of length n and m successvely. There are dstnct jon types, and are llustrated n Fgure, where the heavy drected arcs represent upchans, the lghter drected arcs represent downchans, and the jon s the connectng vertex. The shadng represents the sde of the chans nteror to the polygon (whch you wll recall s clockwse orented). LPEAK RPEAKR LVALLEY RVALLEYR UCUSP LPEAKR RPEAK LVALLEYR RVALLEY DCUSP Fgure Jon Types. (Arcs represent chans) The jon types are defned n Table. It should be noted that, because of the precondton for no nlne vertces, the condton e + = cannot occur. The algorthm proceeds by dentfyng all j jon vertces n a pass through the vertex lst. It can be readly seen that, for practcal polygons, j << n, where n s the number of vertces n the polygon, and j s the number of jon vertces. The jon vertces are then sorted lexcographcally by ther x-coordnate, and negatve y-coordnate. Whle ths sortng could be done by a Jordon sort n lnear tme, t s more effcent to use a qucksort for practcal polygons of bounded sze. The algorthm then processes (.e., sweeps) jons sequentally from left to rght. Trapezod (sequences) are generated durng the processng of certan jon types (.e., at certan sweep events.) Vertces that are not jons that are nternal to chans wll generally only be vsted twce, once durng jon dentfcaton, and once durng trapezod generaton. At any pont n the jon processng (.e., at a sweep event), there wll exst one or more wndows, whch are composed of concatenated fragments of up-chans. These chans are what can be seen lookng toward the left from any pont havng the current sweep event s x-coordnate, and therefore represent a potental sequence of left sdes for adjacent trapezods. Defnton: A wndow relatve to jon p, s a transent (exstng at sweep event p) up-chan, and s composed of fragments of wndows, connected by horzontal segments. Four operatons exst on wndows:. Creaton from an up-chan.. Splttng at a y-nterpolaton (see below).. Extenson, by concatenatng an up-chan.. Reducton (possbly completely), durng trapezod generaton. Practcal polygons are those defnng typcal graphcal objects outlned by flattened curves and straght lne segments. In these cases, the length of chans tends not to be as small as, for example, random generated and non-smoothed polygons.

5 Table Defnton of Jon Types. Jon type LPEAK LPEAKR RPEAK RPEAKR LVALLEY LVALLEYR RVALLEY RVALLEYR UCUSP DCUSP Defnton Vertex, v, jonng top of up-chan and a non- DOWNTORIGHT down-chan, such that the vector product of the ncdent edges, e >. + Vertex, v, jonng top of up-chan and a DOWNTORIGHT down-chan, such that the vector product of the ncdent edges, e >. + Vertex, v, jonng top of up-chan and a non- DOWNTORIGHT down-chan, such that the vector product of the ncdent edges, e <. + Vertex, v, jonng top of up-chan and a DOWNTORIGHT down-chan, such that the vector product of the ncdent edges, e <. + Vertex, v, jonng bottom of up-chan and a non-downtoright down-chan, such that the vector product of the ncdent edges, e + <. Vertex, v, jonng bottom of up-chan and a DOWNTORIGHT down-chan, such that the vector product of the ncdent edges, e + <. Vertex, v, jonng bottom of up-chan and a non-downtoright down-chan, such that the vector product of the ncdent edges, e + >. Vertex, v, jonng bottom of up-chan and a DOWNTORIGHT down-chan, such that the vector product of the ncdent edges, e + >. Vertex, v, jonng two up-chans. Vertex, v, jonng two down-chans. upper part of the splt wndow, (X,). The ear bounded by the down-chan (,), the bottom part of the splt wndow, and the horzontal edge from X to jon, s a vertcally monotone polygon, and wll be converted to trapezods, and taken out of consderaton for the remander of the process. Defnton: A y-nterpolaton at a heght y of a wndow W exsts f the wndow vertcally spans y, and, n ths case, s one of three types. It s the pont havng y-coordnate y, and x-coordnate determned for each of three types as follows: Type I: If there exst two vertces n W at heght y (.e., a horzontal edge at heght y): the x-coordnate of the vertex ncdent on the head end of the horzontal edge. We wll call the vertex at the tal end of the horzontal edge the secondary y-nterpolaton. Type II: If there exsts a sngle vertex n W at heght y: the x-coordnate of that vertex. Type III: If there exst no vertces n W at heght y: the x-coordnate of the ntersecton of the horzontal lne at heght y and the edge n W that vertcally spans y. The wndow y-nterpolaton types are llustrated n Fgure. The drected arcs are clockwse-specfed edges n the wndow. All ponts labeled p are y-nterpolatons at the heght ndcated by the dashed horzontal lne. For type I nterpolatons, ponts labeled p ' are secondary y-nterpolatons. Defnton: In any polygon (or polygon fragment) there s one wndow W relatve to the current jon p (sweep event), that s dstngushed by havng the largest (rghtmost) y-nterpolaton at heght y p to the left of p. Wndow, W, s termed left-facng jon p. B. Jon Processng Wndows are sequentally created, evolve, and are used up durng jon processng. The concept and role of wndows are llustrated n Fgure. The edges of ths graph represent the up-chans and down-chans, and the nodes represent the jons, whch are labeled n sweep order. The bold edges n Fgure (a), (b), and (c) represent the exstng wndows just pror to processng jons, 6, and respectvely. p p p p p p Type I Type I Type II Type III (a) Fgure Example of wndows. x 6 (b) After processng jon, the wndow from jon to denoted (, ) wll be splt at ts y-nterpolaton at the heght of jon (pont X). Now the lower wndow wll consst of the up-chan (,), a horzontal segment from jon to X, and the 6 (c) Fgure Wndow y-nterpolaton types The processng of each type of jon wll now be descrbed. For each of the peak and valley jons, there wll be exactly one ncdent up-chan. If ths up-chan s seen for the frst tme (.e., the sweep event jon s at the left end of the chan), t s marked as a wndow by labelng the top jon of the chan. If the sweep event jon s at the rght end of the up-chan, t does no harm to label the chan as a wndow, snce t wll already be labeled as such. Thus, n any case, the top of the ncdent up-chan s marked as the top of a wndow.

6 The specfc (addtonal) processng requred by each jon type wll now be descrbed. The ntal pass over the vertces of the polygon has already provded explct clock-wse lnkages between jons/nodes. The terms pror and next when appled to jons wll refer to those lnkages. ) ucusp A UCUSP vertex s the leftmost vertex of a rsng chan, and s thus the earlest pont n a sweep when the rsng chan wll be seen. The two up-chans on ether sde of the jon are lnked nto a sngle wndow. Ths ensures that all wndows to the left of a jon wll exst (or wll have been used up durng trapezodaton) before that jon s processed. Gven explct lnkages between successve jons, the processng of ths type of jon s done n constant tme. ) dcusp By the tme ths jon s processed, t wll be the rghtmost vertex of a (remanng) monotone polygonal component. The left sde of ths polygon s the wndow ncdent on the top jon, whch s the jon pror to the dcusp jon tself. The rght sde of the polygon s the fallng chan contanng the dcusp jon,.e., the concatenaton of the two down-chans connected by the dcusp jon. Ths monotone polygon s smply reduced to a trapezod sequence. Gven the explct lnkage between adjacent jons, as well as the explct lnkage between vertces (exstng or that are created durng nterpolaton), the processng of ths type of jon s done n a tme that s lnear n the number of vertces n the monotone polygonal component beng reduced. ) rpeakr (and smlarly lvalley) No processng, (other than markng the ncdent up-chan as a wndow), s requred. The processng of ths type of jon requres constant tme. ) rpeak (AND SIMILARLY lvalleyr) As n the dcusp case, ths s the rghtmost vertex of a remanng monotone polygonal component. The left sde of ths polygon s the wndow ncdent on ths jon, and the rght sde s the down-chan ncdent on the same jon. Ths monotone polygon s smply reduced to a trapezod sequence. Agan, gven the explct lnkage between vertces, the processng of ths type of jon s done n a tme that s lnear n the number of vertces n the monotone polygonal component beng reduced. ) rvalleyr (and smlarly lpeak) W q D p U W W Fgure rvalleyr jon handlng. The handlng of rvalleyr jons s llustrated n Fgure,where vertex p s the current (rvalleyr) jon, and U and D are respectvely the up-chan and down-chan ncdent on p. The H D p U wndow, W, ncdent on q, the jon pror to p (.e., the top of D), s splt at the y-nterpolaton of W at the heght of p. A horzontal edge H s defned from ths nterpolaton (or the secondary y-nterpolaton, f t s of Type I) to p (equvalent to p or to the next vertex f there exsts a horzontal edge ncdent on p). The lower part of the splt wndow, W (up to the y-nterpolaton of W at yp, or, f t s of Type I, the secondary nterpolaton), s connected va H to the up-chan, U (equvalent to U wth the ntal horzontal edge removed, f t exsts), formng an updated wndow. The removed shaded ear s a vertcally monotone polygon whose left chan s the top part of the splt wndow, W, whose rght chan s the down-chan D, ncdent on p, and whose bottom s closed by a horzontal edge from p to the y-nterpolaton of W at heght yp. Ths monotone polygon s smply reduced to a trapezod sequence. Agan, assumng explct lnkages exst between adjacent edges, t can easly be seen that the processng of ths type of jon can be done n tme that s lnear n the number of vertces n the trapezod sequence beng reduced. 6) rvalley (AND SIMILARLY lpeakr) C C D W U W q H p q p q H p W H D U C C Fgure rvalley jon handlng. We wll call jon vertces of type RVALLEY and LPEAKR left spke jons. The handlng of RVALLEY jons s llustrated n Fgure, where vertex p s the current (RVALLEY) jon. Chans U and D are respectvely the up-chan and down-chan ncdent on p. Frst we must search (by a method dscussed below) for the wndow, W, that s left-facng p. The y-nterpolaton of W at heght y p s the pont q. The dashed arcs C and C represent the seres of chans/wndows connectng the top and bottom of W respectvely to the ends of D and U opposte to p. Pont q ' s equvalent to ether q or, n the case of type I nterpolatons, the secondary y-nterpolaton of W at heght y p. Pont p ' s equvalent to ether p or, f there exsts a horzontal edge ncdent on p, to the next vertex. Chan U ' s equvalent to U wth any ntal horzontal edge removed. Horzontal edge H s defned from q ' to p ', and H s defned from p to q. Thus the polygon s splt nto two dsconnected polygons defned by [ H, W, C, D] and [ H ', U', C, W]. C. Searchng for nterpolaton wndow In the left spke jon cases (RVALLEY and LPEAKR), a search s requred for the specfc wndow that s left-facng the spke jon. In one approach, the search begns at the jon pror to the spke jon, and traverses jon vertces n a counter-clockwse order, lookng for wndows, whch vertcally span the jon

7 (whch wll be termed canddate wndows). Each such canddate wndow s nterpolated and ether elmnated (ts y-nterpolaton s to the left of the y-nterpolaton of a prevous potental left-facng wndow, or s to the rght of the jon), or otherwse accepted as the new potental left-facng wndow. Fgure 6(a) llustrates ths process for spke jon. As jons are traversed counter-clockwse from ths jon, the frst canddate wndow (.e., one that meets vertcal spannng crteron) s A. After traversng further, canddate wndow B s dscovered wth an nterpolaton closer and to the left of vertex, and becomes the new potental left-facng wndow. Canddate wndow C s found thereafter, wth a closer nterpolaton on the left of the left spke jon. The search s termnated when the jon traversal returns to the spke jon. An optmzaton to potentally reduce the number of requred nterpolatons can be farly easly mplemented. A (a) B C 6 8 Fgure 6 Search for wndow n spke case. IV. PROOF OUTLINE A B (b) C 6 8 All left sdes of horzontal trapezods wll be part of a wndow (as defned n secton III),.e., composed of (fragments of) up-chan edges. Smlarly, the rght sdes of trapezods wll be composed of (fragments of ) down-chan edges. Ths can be seen by walkng the contour of the polygon (n the prescrbed clockwse drecton); the nsde of the (smple) polygon, and consequently the component trapezods, wll always be on the rght-hand sde. The rghtmost end, jon r, of a down-chan, D, wll trgger the trapezodaton of the (monotone polygonal) area between D, and the horzontally projected porton, W, of a unque wndow, W, that exsts at the sweep event r. W s the wndow left-facng r, and must exst at the sweep event r snce up-chans are converted to, or ncorporated nto, wndows as soon as they are seen (.e., as soon as the leftmost end of an up-chan s the current sweep event). Every pont on D must have the same unque left-facng wndow, W, that s W ' W, snce both W and D are vertcally monotone. Both D and W wll be consumed n the trapezodaton process for ths component of the polygon. Any remnant wndow W W ' wll be extended to form the bass for a new wndow at a later sweep event. V. COMPLEXITY ANALYSIS In ths analyss we assume that all vertces (ncludng vertces created by wndow nterpolatons) are doubly lnked n order around the polygon (or polygon component). We also assume that jon vertces are doubly lnked n order around the polygon. The ntal set-up of such lnkages can be done n lnear tme by a sngle pass through the vertex lst of the orgnal polygon. Jons of type UCUSP, RPEAKR, and LVALLEY can be processed n constant tme. The complete set of trapezods for a gven nput polygon s generated dsjontly durng the processng of jons of type RPEAK, LVALLEYR, DCUSP, RVALLEYR, or LPEAK. These fve jon types ndvdually ncur constant processng tme, and together they ncur tme for the generaton of n trapezods n the worst case (each tme a par of vertces are n horzontal vew of each, or there exsts a horzontal edge, the trapezod count decreases by one). Thus the total tme to process these eght jon types s lnear n n, the number of vertces n the nput polygon. The processng of spke jons requres the traversal of all jon vertces n the polygon fragment that contans the spke. At least one wndow wll meet the requrements gven n secton III.B.6), and ths (left-facng) wndow wll requre, on average, the traversal of half ts vertces. The total number of vertces vsted has an upper bound of the number of vertces n the component, wth the ntal fragment sze beng n. Each wndow splttng event dvdes the polygon nto two polygonal components. If we assume that the components are dstrbuted randomly, the complexty of treatng spke jons wll n. In practcal cases, the number of be bounded by jons, j, wll be relatvely small, that s j << n. We know that j < s, where s s the snuousty of the polygon, snce changes n drecton havng a vertcal component create peaks and valleys (.e., jons), and only some of the remanng changes n drecton (whch have a horzontal component) create UCUSPS Ο s log n. If we and DCUSP jons. Thus, a tghter bound s further assert, as dscussed n III.C, for practcal nputs, that the number of wndows that need to be nterpolated s on average very small (close to ), then a tghter estmate of the average-case complexty of jon processng tme s Ο j log j, where j < j s the number of spke jons. For s s random polygons, j j/6 snce there are two spke jon s types, wth the down-chan drecton constraned to one quadrant, and the up-chan drecton constraned to one subdvson (on average half) of that quadrant. It s beleved that j s wll be less than that n more practcal polygons, snce spke jons are somewhat unnatural. Thus, wth the excepton of spke jons, whch are expected to be farly rare n practcal polygons, the algorthm s lnear. It s beleved that spke jons themselves are not very expensve n practcal polygons. VI. IMPLEMENTATION In the followng, an mplementaton nvolvng a relatvely smple lnked data structure s descrbed. The nodes n ths

8 structure ntally come from the vertces of the nput polygon, and temporary nodes correspondng to edge nterpolatons may be added durng the jon processng. The suggested approach has been coded and tested, and the performance s shown n the followng secton. An ntal pass s made though the vertex lst of the nput polygon to create a doubly lnked crcular lst of vertces. Durng ths pass, jon vertces that connect up-chans and downchans are dentfed, labeled, and lnked (by a separate thread) to form a doubly lnked crcular lst. The peak and valley jons are labeled usng a -bt code where separate bts represent characterstcs of postve edge cross product, downtorght, and peak. A rsng chan that has an x-extremum strctly to the left of the top and bottom jons s splt nto two up-chans at ths vertex. Ths vertex becomes a ucusp jon (connectng the two resultng up-chans) and s lnked nto the jon lst. Smlarly, a fallng chan that has an x-extremum strctly to the rght of the top and bottom jons, s splt nto two down-chans at ths vertex. Ths dcusp vertex s also lnked nto the jon lst. Thus the vertces are doubly lnked to adjacent vertces, and, f they are jons, to pror and next jons. Cusps use two addtonal bts n the jon type code. Addtonal nodes, correspondng to edge nterpolatons, are nserted as wndows are y-nterpolated. The crcular lsts are splt nto two dsjont crcular lsts, correspondng to polygonal components, durng the processng of lpeakr, or rvalley (spke) jons. Sectons of a lst are removed (and transformed nto trapezod sequences) durng the processng of rvalleyr, or lpeak jons. Crcularly closed lsts are completely transformed nto trapezod sequences durng the processng of dcusp, rpeak, or lvalleyr jons. Ths dynamc mult-threaded lst s the only data structure used, other than a statc queue of jons to be processed. VII. PERFORMANCE MEASUREMENT Approxmately 6, polygons were extracted from a large sute of vector graphcs test pages provded by QMS, Inc. Ths provded a practcal dataset, on whch relatve tmngs were performed on the current mplementaton, n relaton to Narkhede s mplementaton of Sedel s algorthm [[6]]. The results gven n Fgure shows our algorthm to average about tmes faster, for polygon szes up to 8 vertces. Relatve tmngs were also performed on random polygons (up to vertces) generated by RPG [[]]. Here our algorthm s n the order of an average of tmes faster. It should be noted that these polygons do not have the characterstcs found n a practcal dataset. VIII. CONCLUSION A fast, practcal, determnstc algorthm to decompose an arbtrary smple polygon nto trapezods has been presented. The polygon vertex coordnates can be specfed as real numbers, and horzontal as well as vertcal edges very common n practcal polygons are explctly handled. An ntermedate output of the algorthm s a decomposton of the nput polygon nto a mnmal set of monotone polygons. The fact that the trapezods are output n useful groupngs (trapezod sequences), allows a further compresson of polygon trapezodal storage. A very fast lnear scan of vertces s used to determne a collecton of key vertces (jons), and these are processed n sweep order. No complcated data structures are needed. The practcal complexty appear to be very close to lnear. Nested holes were mplemented as a smple extenson. Speedup (Sedel/Han) 8 6 Polygon sze (num of vertces) Fgure Relatve executon tmes, Han vs Sedel. IX. ACKNOWLEDGEMENTS We would lke to thank QMS, Inc. for supportng ths research. We would also lke to thank Somnath Gulve for hs help n buldng a dynamc testng tool to experment wth, and exhaustvely test [[]], the mplementaton of the algorthm. REFERENCES [] Auer, T., and Held, M. RPG: Heurstc for the generaton of random polygons. Proc. 8th Canada Conf. Comput. Geom. Ottawa, Canada, Aug. 6, 8. [] Chazelle B. A theorem on polygon cuttng wth applcatons. Proceedngs of rd IEEE Symposum on Foundaton of Computatonal Scence,, 8. [] Chazelle B. Trangulatng a smple polygon n lnear tme. st IEEE Symposum on Foundaton of Computatonal Scence (),. [] Chazelle B., and Gubas L.J. Vsblty and ntersecton problems n plane geometry. Dscrete and Computatonal Geometry (8), 8. [] Chazelle B., and Incerp J. Trangulaton and shape-complexty. ACM TOG, ():, 8. [6] Clarkson K., Tarjan R.E., and Van Wyk C.J. A fast Las Vegas algorthm for trangulatng a smple polygon. Dscrete Computatonal Geometry, :, 8. [] Fourner A., and Montuno D.Y. Trangulatng smple polygons and equvalent problems. ACM Transactons on Graphcs, ():, 8. [8] Garey M.R., Johnson D.S., Preparata, F.P., and Tarjan R.E. Trangulatng a smple polygon. Informaton Processng Letter, :, 8. [] Han T.F., Gulve S. Interactve, Vsual Testng Strategy for Computatonal Geometry Problems. Proceedngs of 6th Annual ACM Southeast Conference, Atlanta, Georga, Aprl, 8

9 [] Hertel S., and Melhorn K. Fast trangulaton of smple polygons. Proc. of th Internatonal Conf. on Foundatonal Computatonal Theory, v.8 of Lecture Notes n Computer Scence, 8. Sprnger-Verlag, 8. [] Krkpatrck D.G., Klawe M.M., and Tarjan R.E. Polygon Trangulaton n log n tme wth smple data structures. Proceedngs of 6th Annual ACM Symposum on Computaton Geometry,,. [] Laszlo M.J. Computatonal Geometry and Computer Graphcs n C++, Prentce Hall, Englewood Clffs, NJ, 6. [] Lee D.T. and Preparata F.P. Locaton of a pont n a planar subdvson and ts applcaton. SIAM Journal of Computaton, 6:-66,. [] Lennes N.J. Theorems on smple polygon and polyhedron. Amercan J. of Mathematcs. : 6,. [] Mulmuley K. Computatonal Geometry: An Introducton Through Randomzed Algorthms. Prentce Hall, Englewood Clffs, NJ,. [6] Narkhede A., and Manochoa, D., Fast Polygon Trangulaton Based on Sedel s Algorthm. A Techncal Report, Dept. of Computer Scence, UNC, Chapel Hll. [] O Rourke J. Computatonal Geometry n C. Cambrdge Unversty Press,. [8] Preparata F.P., and Shamos M.L. Computatonal Geometry, An Introducton. Sprnger-Verlag, NY, 8. [] Sedel R. A Smple and fast ncremental randomzed algorthm for computng trapezodal decompostons and for trangulatng polygons. Computatonal Geometry Theory and Applcatons, : 6, [] Toussant G. An output-complexty-senstve polygon trangulaton algorthm. Report SICS-86.; McGll Unversty, Montreal, 88. [] Toussant G. Trangulaton and Arrangements. All Insttute Lecture at McGll Unversty, Montreal,.

An Optimal Algorithm for Prufer Codes *

An Optimal Algorithm for Prufer Codes * J. Software Engneerng & Applcatons, 2009, 2: 111-115 do:10.4236/jsea.2009.22016 Publshed Onlne July 2009 (www.scrp.org/journal/jsea) An Optmal Algorthm for Prufer Codes * Xaodong Wang 1, 2, Le Wang 3,

More information

Parallelism for Nested Loops with Non-uniform and Flow Dependences

Parallelism for Nested Loops with Non-uniform and Flow Dependences Parallelsm for Nested Loops wth Non-unform and Flow Dependences Sam-Jn Jeong Dept. of Informaton & Communcaton Engneerng, Cheonan Unversty, 5, Anseo-dong, Cheonan, Chungnam, 330-80, Korea. seong@cheonan.ac.kr

More information

Scan Conversion & Shading

Scan Conversion & Shading 1 3D Renderng Ppelne (for drect llumnaton) 2 Scan Converson & Shadng Adam Fnkelsten Prnceton Unversty C0S 426, Fall 2001 3DPrmtves 3D Modelng Coordnates Modelng Transformaton 3D World Coordnates Lghtng

More information

Scan Conversion & Shading

Scan Conversion & Shading Scan Converson & Shadng Thomas Funkhouser Prnceton Unversty C0S 426, Fall 1999 3D Renderng Ppelne (for drect llumnaton) 3D Prmtves 3D Modelng Coordnates Modelng Transformaton 3D World Coordnates Lghtng

More information

For instance, ; the five basic number-sets are increasingly more n A B & B A A = B (1)

For instance, ; the five basic number-sets are increasingly more n A B & B A A = B (1) Secton 1.2 Subsets and the Boolean operatons on sets If every element of the set A s an element of the set B, we say that A s a subset of B, or that A s contaned n B, or that B contans A, and we wrte A

More information

Compiler Design. Spring Register Allocation. Sample Exercises and Solutions. Prof. Pedro C. Diniz

Compiler Design. Spring Register Allocation. Sample Exercises and Solutions. Prof. Pedro C. Diniz Compler Desgn Sprng 2014 Regster Allocaton Sample Exercses and Solutons Prof. Pedro C. Dnz USC / Informaton Scences Insttute 4676 Admralty Way, Sute 1001 Marna del Rey, Calforna 90292 pedro@s.edu Regster

More information

Assignment # 2. Farrukh Jabeen Algorithms 510 Assignment #2 Due Date: June 15, 2009.

Assignment # 2. Farrukh Jabeen Algorithms 510 Assignment #2 Due Date: June 15, 2009. Farrukh Jabeen Algorthms 51 Assgnment #2 Due Date: June 15, 29. Assgnment # 2 Chapter 3 Dscrete Fourer Transforms Implement the FFT for the DFT. Descrbed n sectons 3.1 and 3.2. Delverables: 1. Concse descrpton

More information

Sorting Review. Sorting. Comparison Sorting. CSE 680 Prof. Roger Crawfis. Assumptions

Sorting Review. Sorting. Comparison Sorting. CSE 680 Prof. Roger Crawfis. Assumptions Sortng Revew Introducton to Algorthms Qucksort CSE 680 Prof. Roger Crawfs Inserton Sort T(n) = Θ(n 2 ) In-place Merge Sort T(n) = Θ(n lg(n)) Not n-place Selecton Sort (from homework) T(n) = Θ(n 2 ) In-place

More information

A Binarization Algorithm specialized on Document Images and Photos

A Binarization Algorithm specialized on Document Images and Photos A Bnarzaton Algorthm specalzed on Document mages and Photos Ergna Kavalleratou Dept. of nformaton and Communcaton Systems Engneerng Unversty of the Aegean kavalleratou@aegean.gr Abstract n ths paper, a

More information

Lecture 5: Multilayer Perceptrons

Lecture 5: Multilayer Perceptrons Lecture 5: Multlayer Perceptrons Roger Grosse 1 Introducton So far, we ve only talked about lnear models: lnear regresson and lnear bnary classfers. We noted that there are functons that can t be represented

More information

UNIT 2 : INEQUALITIES AND CONVEX SETS

UNIT 2 : INEQUALITIES AND CONVEX SETS UNT 2 : NEQUALTES AND CONVEX SETS ' Structure 2. ntroducton Objectves, nequaltes and ther Graphs Convex Sets and ther Geometry Noton of Convex Sets Extreme Ponts of Convex Set Hyper Planes and Half Spaces

More information

6.854 Advanced Algorithms Petar Maymounkov Problem Set 11 (November 23, 2005) With: Benjamin Rossman, Oren Weimann, and Pouya Kheradpour

6.854 Advanced Algorithms Petar Maymounkov Problem Set 11 (November 23, 2005) With: Benjamin Rossman, Oren Weimann, and Pouya Kheradpour 6.854 Advanced Algorthms Petar Maymounkov Problem Set 11 (November 23, 2005) Wth: Benjamn Rossman, Oren Wemann, and Pouya Kheradpour Problem 1. We reduce vertex cover to MAX-SAT wth weghts, such that the

More information

Problem Set 3 Solutions

Problem Set 3 Solutions Introducton to Algorthms October 4, 2002 Massachusetts Insttute of Technology 6046J/18410J Professors Erk Demane and Shaf Goldwasser Handout 14 Problem Set 3 Solutons (Exercses were not to be turned n,

More information

Today s Outline. Sorting: The Big Picture. Why Sort? Selection Sort: Idea. Insertion Sort: Idea. Sorting Chapter 7 in Weiss.

Today s Outline. Sorting: The Big Picture. Why Sort? Selection Sort: Idea. Insertion Sort: Idea. Sorting Chapter 7 in Weiss. Today s Outlne Sortng Chapter 7 n Wess CSE 26 Data Structures Ruth Anderson Announcements Wrtten Homework #6 due Frday 2/26 at the begnnng of lecture Proect Code due Mon March 1 by 11pm Today s Topcs:

More information

R s s f. m y s. SPH3UW Unit 7.3 Spherical Concave Mirrors Page 1 of 12. Notes

R s s f. m y s. SPH3UW Unit 7.3 Spherical Concave Mirrors Page 1 of 12. Notes SPH3UW Unt 7.3 Sphercal Concave Mrrors Page 1 of 1 Notes Physcs Tool box Concave Mrror If the reflectng surface takes place on the nner surface of the sphercal shape so that the centre of the mrror bulges

More information

Reading. 14. Subdivision curves. Recommended:

Reading. 14. Subdivision curves. Recommended: eadng ecommended: Stollntz, Deose, and Salesn. Wavelets for Computer Graphcs: heory and Applcatons, 996, secton 6.-6., A.5. 4. Subdvson curves Note: there s an error n Stollntz, et al., secton A.5. Equaton

More information

CHAPTER 2 DECOMPOSITION OF GRAPHS

CHAPTER 2 DECOMPOSITION OF GRAPHS CHAPTER DECOMPOSITION OF GRAPHS. INTRODUCTION A graph H s called a Supersubdvson of a graph G f H s obtaned from G by replacng every edge uv of G by a bpartte graph,m (m may vary for each edge by dentfyng

More information

CMPS 10 Introduction to Computer Science Lecture Notes

CMPS 10 Introduction to Computer Science Lecture Notes CPS 0 Introducton to Computer Scence Lecture Notes Chapter : Algorthm Desgn How should we present algorthms? Natural languages lke Englsh, Spansh, or French whch are rch n nterpretaton and meanng are not

More information

Concurrent Apriori Data Mining Algorithms

Concurrent Apriori Data Mining Algorithms Concurrent Apror Data Mnng Algorthms Vassl Halatchev Department of Electrcal Engneerng and Computer Scence York Unversty, Toronto October 8, 2015 Outlne Why t s mportant Introducton to Assocaton Rule Mnng

More information

CSCI 104 Sorting Algorithms. Mark Redekopp David Kempe

CSCI 104 Sorting Algorithms. Mark Redekopp David Kempe CSCI 104 Sortng Algorthms Mark Redekopp Davd Kempe Algorthm Effcency SORTING 2 Sortng If we have an unordered lst, sequental search becomes our only choce If we wll perform a lot of searches t may be benefcal

More information

Mathematics 256 a course in differential equations for engineering students

Mathematics 256 a course in differential equations for engineering students Mathematcs 56 a course n dfferental equatons for engneerng students Chapter 5. More effcent methods of numercal soluton Euler s method s qute neffcent. Because the error s essentally proportonal to the

More information

Insertion Sort. Divide and Conquer Sorting. Divide and Conquer. Mergesort. Mergesort Example. Auxiliary Array

Insertion Sort. Divide and Conquer Sorting. Divide and Conquer. Mergesort. Mergesort Example. Auxiliary Array Inserton Sort Dvde and Conquer Sortng CSE 6 Data Structures Lecture 18 What f frst k elements of array are already sorted? 4, 7, 1, 5, 1, 16 We can shft the tal of the sorted elements lst down and then

More information

Chapter 6 Programmng the fnte element method Inow turn to the man subject of ths book: The mplementaton of the fnte element algorthm n computer programs. In order to make my dscusson as straghtforward

More information

S1 Note. Basis functions.

S1 Note. Basis functions. S1 Note. Bass functons. Contents Types of bass functons...1 The Fourer bass...2 B-splne bass...3 Power and type I error rates wth dfferent numbers of bass functons...4 Table S1. Smulaton results of type

More information

1 Dynamic Connectivity

1 Dynamic Connectivity 15-850: Advanced Algorthms CMU, Sprng 2017 Lecture #3: Dynamc Graph Connectvty algorthms 01/30/17 Lecturer: Anupam Gupta Scrbe: Hu Han Chn, Jacob Imola Dynamc graph algorthms s the study of standard graph

More information

F Geometric Mean Graphs

F Geometric Mean Graphs Avalable at http://pvamu.edu/aam Appl. Appl. Math. ISSN: 1932-9466 Vol. 10, Issue 2 (December 2015), pp. 937-952 Applcatons and Appled Mathematcs: An Internatonal Journal (AAM) F Geometrc Mean Graphs A.

More information

Complex Numbers. Now we also saw that if a and b were both positive then ab = a b. For a second let s forget that restriction and do the following.

Complex Numbers. Now we also saw that if a and b were both positive then ab = a b. For a second let s forget that restriction and do the following. Complex Numbers The last topc n ths secton s not really related to most of what we ve done n ths chapter, although t s somewhat related to the radcals secton as we wll see. We also won t need the materal

More information

Related-Mode Attacks on CTR Encryption Mode

Related-Mode Attacks on CTR Encryption Mode Internatonal Journal of Network Securty, Vol.4, No.3, PP.282 287, May 2007 282 Related-Mode Attacks on CTR Encrypton Mode Dayn Wang, Dongda Ln, and Wenlng Wu (Correspondng author: Dayn Wang) Key Laboratory

More information

Content Based Image Retrieval Using 2-D Discrete Wavelet with Texture Feature with Different Classifiers

Content Based Image Retrieval Using 2-D Discrete Wavelet with Texture Feature with Different Classifiers IOSR Journal of Electroncs and Communcaton Engneerng (IOSR-JECE) e-issn: 78-834,p- ISSN: 78-8735.Volume 9, Issue, Ver. IV (Mar - Apr. 04), PP 0-07 Content Based Image Retreval Usng -D Dscrete Wavelet wth

More information

Sequential search. Building Java Programs Chapter 13. Sequential search. Sequential search

Sequential search. Building Java Programs Chapter 13. Sequential search. Sequential search Sequental search Buldng Java Programs Chapter 13 Searchng and Sortng sequental search: Locates a target value n an array/lst by examnng each element from start to fnsh. How many elements wll t need to

More information

Multiblock method for database generation in finite element programs

Multiblock method for database generation in finite element programs Proc. of the 9th WSEAS Int. Conf. on Mathematcal Methods and Computatonal Technques n Electrcal Engneerng, Arcachon, October 13-15, 2007 53 Multblock method for database generaton n fnte element programs

More information

An Approach in Coloring Semi-Regular Tilings on the Hyperbolic Plane

An Approach in Coloring Semi-Regular Tilings on the Hyperbolic Plane An Approach n Colorng Sem-Regular Tlngs on the Hyperbolc Plane Ma Louse Antonette N De Las Peñas, mlp@mathscmathadmueduph Glenn R Lago, glago@yahoocom Math Department, Ateneo de Manla Unversty, Loyola

More information

The Shortest Path of Touring Lines given in the Plane

The Shortest Path of Touring Lines given in the Plane Send Orders for Reprnts to reprnts@benthamscence.ae 262 The Open Cybernetcs & Systemcs Journal, 2015, 9, 262-267 The Shortest Path of Tourng Lnes gven n the Plane Open Access Ljuan Wang 1,2, Dandan He

More information

Parallel matrix-vector multiplication

Parallel matrix-vector multiplication Appendx A Parallel matrx-vector multplcaton The reduced transton matrx of the three-dmensonal cage model for gel electrophoress, descrbed n secton 3.2, becomes excessvely large for polymer lengths more

More information

CSE 326: Data Structures Quicksort Comparison Sorting Bound

CSE 326: Data Structures Quicksort Comparison Sorting Bound CSE 326: Data Structures Qucksort Comparson Sortng Bound Bran Curless Sprng 2008 Announcements (5/14/08) Homework due at begnnng of class on Frday. Secton tomorrow: Graded homeworks returned More dscusson

More information

2x x l. Module 3: Element Properties Lecture 4: Lagrange and Serendipity Elements

2x x l. Module 3: Element Properties Lecture 4: Lagrange and Serendipity Elements Module 3: Element Propertes Lecture : Lagrange and Serendpty Elements 5 In last lecture note, the nterpolaton functons are derved on the bass of assumed polynomal from Pascal s trangle for the fled varable.

More information

Module Management Tool in Software Development Organizations

Module Management Tool in Software Development Organizations Journal of Computer Scence (5): 8-, 7 ISSN 59-66 7 Scence Publcatons Management Tool n Software Development Organzatons Ahmad A. Al-Rababah and Mohammad A. Al-Rababah Faculty of IT, Al-Ahlyyah Amman Unversty,

More information

CSE 326: Data Structures Quicksort Comparison Sorting Bound

CSE 326: Data Structures Quicksort Comparison Sorting Bound CSE 326: Data Structures Qucksort Comparson Sortng Bound Steve Setz Wnter 2009 Qucksort Qucksort uses a dvde and conquer strategy, but does not requre the O(N) extra space that MergeSort does. Here s the

More information

Support Vector Machines

Support Vector Machines /9/207 MIST.6060 Busness Intellgence and Data Mnng What are Support Vector Machnes? Support Vector Machnes Support Vector Machnes (SVMs) are supervsed learnng technques that analyze data and recognze patterns.

More information

The Codesign Challenge

The Codesign Challenge ECE 4530 Codesgn Challenge Fall 2007 Hardware/Software Codesgn The Codesgn Challenge Objectves In the codesgn challenge, your task s to accelerate a gven software reference mplementaton as fast as possble.

More information

Sorting: The Big Picture. The steps of QuickSort. QuickSort Example. QuickSort Example. QuickSort Example. Recursive Quicksort

Sorting: The Big Picture. The steps of QuickSort. QuickSort Example. QuickSort Example. QuickSort Example. Recursive Quicksort Sortng: The Bg Pcture Gven n comparable elements n an array, sort them n an ncreasng (or decreasng) order. Smple algorthms: O(n ) Inserton sort Selecton sort Bubble sort Shell sort Fancer algorthms: O(n

More information

Improvement of Spatial Resolution Using BlockMatching Based Motion Estimation and Frame. Integration

Improvement of Spatial Resolution Using BlockMatching Based Motion Estimation and Frame. Integration Improvement of Spatal Resoluton Usng BlockMatchng Based Moton Estmaton and Frame Integraton Danya Suga and Takayuk Hamamoto Graduate School of Engneerng, Tokyo Unversty of Scence, 6-3-1, Nuku, Katsuska-ku,

More information

Problem Definitions and Evaluation Criteria for Computational Expensive Optimization

Problem Definitions and Evaluation Criteria for Computational Expensive Optimization Problem efntons and Evaluaton Crtera for Computatonal Expensve Optmzaton B. Lu 1, Q. Chen and Q. Zhang 3, J. J. Lang 4, P. N. Suganthan, B. Y. Qu 6 1 epartment of Computng, Glyndwr Unversty, UK Faclty

More information

Programming in Fortran 90 : 2017/2018

Programming in Fortran 90 : 2017/2018 Programmng n Fortran 90 : 2017/2018 Programmng n Fortran 90 : 2017/2018 Exercse 1 : Evaluaton of functon dependng on nput Wrte a program who evaluate the functon f (x,y) for any two user specfed values

More information

A Fast Content-Based Multimedia Retrieval Technique Using Compressed Data

A Fast Content-Based Multimedia Retrieval Technique Using Compressed Data A Fast Content-Based Multmeda Retreval Technque Usng Compressed Data Borko Furht and Pornvt Saksobhavvat NSF Multmeda Laboratory Florda Atlantc Unversty, Boca Raton, Florda 3343 ABSTRACT In ths paper,

More information

Type-2 Fuzzy Non-uniform Rational B-spline Model with Type-2 Fuzzy Data

Type-2 Fuzzy Non-uniform Rational B-spline Model with Type-2 Fuzzy Data Malaysan Journal of Mathematcal Scences 11(S) Aprl : 35 46 (2017) Specal Issue: The 2nd Internatonal Conference and Workshop on Mathematcal Analyss (ICWOMA 2016) MALAYSIAN JOURNAL OF MATHEMATICAL SCIENCES

More information

The Greedy Method. Outline and Reading. Change Money Problem. Greedy Algorithms. Applications of the Greedy Strategy. The Greedy Method Technique

The Greedy Method. Outline and Reading. Change Money Problem. Greedy Algorithms. Applications of the Greedy Strategy. The Greedy Method Technique //00 :0 AM Outlne and Readng The Greedy Method The Greedy Method Technque (secton.) Fractonal Knapsack Problem (secton..) Task Schedulng (secton..) Mnmum Spannng Trees (secton.) Change Money Problem Greedy

More information

User Authentication Based On Behavioral Mouse Dynamics Biometrics

User Authentication Based On Behavioral Mouse Dynamics Biometrics User Authentcaton Based On Behavoral Mouse Dynamcs Bometrcs Chee-Hyung Yoon Danel Donghyun Km Department of Computer Scence Department of Computer Scence Stanford Unversty Stanford Unversty Stanford, CA

More information

Load Balancing for Hex-Cell Interconnection Network

Load Balancing for Hex-Cell Interconnection Network Int. J. Communcatons, Network and System Scences,,, - Publshed Onlne Aprl n ScRes. http://www.scrp.org/journal/jcns http://dx.do.org/./jcns.. Load Balancng for Hex-Cell Interconnecton Network Saher Manaseer,

More information

A MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS

A MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS Proceedngs of the Wnter Smulaton Conference M E Kuhl, N M Steger, F B Armstrong, and J A Jones, eds A MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS Mark W Brantley Chun-Hung

More information

High-Boost Mesh Filtering for 3-D Shape Enhancement

High-Boost Mesh Filtering for 3-D Shape Enhancement Hgh-Boost Mesh Flterng for 3-D Shape Enhancement Hrokazu Yagou Λ Alexander Belyaev y Damng We z Λ y z ; ; Shape Modelng Laboratory, Unversty of Azu, Azu-Wakamatsu 965-8580 Japan y Computer Graphcs Group,

More information

Fast Computation of Shortest Path for Visiting Segments in the Plane

Fast Computation of Shortest Path for Visiting Segments in the Plane Send Orders for Reprnts to reprnts@benthamscence.ae 4 The Open Cybernetcs & Systemcs Journal, 04, 8, 4-9 Open Access Fast Computaton of Shortest Path for Vstng Segments n the Plane Ljuan Wang,, Bo Jang

More information

3D vector computer graphics

3D vector computer graphics 3D vector computer graphcs Paolo Varagnolo: freelance engneer Padova Aprl 2016 Prvate Practce ----------------------------------- 1. Introducton Vector 3D model representaton n computer graphcs requres

More information

Term Weighting Classification System Using the Chi-square Statistic for the Classification Subtask at NTCIR-6 Patent Retrieval Task

Term Weighting Classification System Using the Chi-square Statistic for the Classification Subtask at NTCIR-6 Patent Retrieval Task Proceedngs of NTCIR-6 Workshop Meetng, May 15-18, 2007, Tokyo, Japan Term Weghtng Classfcaton System Usng the Ch-square Statstc for the Classfcaton Subtask at NTCIR-6 Patent Retreval Task Kotaro Hashmoto

More information

Vectorization of Image Outlines Using Rational Spline and Genetic Algorithm

Vectorization of Image Outlines Using Rational Spline and Genetic Algorithm 01 Internatonal Conference on Image, Vson and Computng (ICIVC 01) IPCSIT vol. 50 (01) (01) IACSIT Press, Sngapore DOI: 10.776/IPCSIT.01.V50.4 Vectorzaton of Image Outlnes Usng Ratonal Splne and Genetc

More information

the nber of vertces n the graph. spannng tree T beng part of a par of maxmally dstant trees s called extremal. Extremal trees are useful n the mxed an

the nber of vertces n the graph. spannng tree T beng part of a par of maxmally dstant trees s called extremal. Extremal trees are useful n the mxed an On Central Spannng Trees of a Graph S. Bezrukov Unverstat-GH Paderborn FB Mathematk/Informatk Furstenallee 11 D{33102 Paderborn F. Kaderal, W. Poguntke FernUnverstat Hagen LG Kommunkatonssysteme Bergscher

More information

Interpolation of the Irregular Curve Network of Ship Hull Form Using Subdivision Surfaces

Interpolation of the Irregular Curve Network of Ship Hull Form Using Subdivision Surfaces 7 Interpolaton of the Irregular Curve Network of Shp Hull Form Usng Subdvson Surfaces Kyu-Yeul Lee, Doo-Yeoun Cho and Tae-Wan Km Seoul Natonal Unversty, kylee@snu.ac.kr,whendus@snu.ac.kr,taewan}@snu.ac.kr

More information

Wavefront Reconstructor

Wavefront Reconstructor A Dstrbuted Smplex B-Splne Based Wavefront Reconstructor Coen de Vsser and Mchel Verhaegen 14-12-201212 2012 Delft Unversty of Technology Contents Introducton Wavefront reconstructon usng Smplex B-Splnes

More information

Multi-Resolution Geometric Fusion

Multi-Resolution Geometric Fusion Internatonal Conference on Recent Advances n 3-D Dgtal Imagng and Modellng, Ottawa, Canada May 12 15, 1997 Mult-Resoluton Geometrc Fuson Adran Hlton and John Illngworth Centre for Vson, Speech and Sgnal

More information

The Erdős Pósa property for vertex- and edge-disjoint odd cycles in graphs on orientable surfaces

The Erdős Pósa property for vertex- and edge-disjoint odd cycles in graphs on orientable surfaces Dscrete Mathematcs 307 (2007) 764 768 www.elsever.com/locate/dsc Note The Erdős Pósa property for vertex- and edge-dsjont odd cycles n graphs on orentable surfaces Ken-Ich Kawarabayash a, Atsuhro Nakamoto

More information

Collision Detection. Overview. Efficient Collision Detection. Collision Detection with Rays: Example. C = nm + (n choose 2)

Collision Detection. Overview. Efficient Collision Detection. Collision Detection with Rays: Example. C = nm + (n choose 2) Overvew Collson detecton wth Rays Collson detecton usng BSP trees Herarchcal Collson Detecton OBB tree, k-dop tree algorthms Multple object CD system Collson Detecton Fundamental to graphcs, VR applcatons

More information

Constructing Minimum Connected Dominating Set: Algorithmic approach

Constructing Minimum Connected Dominating Set: Algorithmic approach Constructng Mnmum Connected Domnatng Set: Algorthmc approach G.N. Puroht and Usha Sharma Centre for Mathematcal Scences, Banasthal Unversty, Rajasthan 304022 usha.sharma94@yahoo.com Abstract: Connected

More information

Simplification of 3D Meshes

Simplification of 3D Meshes Smplfcaton of 3D Meshes Addy Ngan /4/00 Outlne Motvaton Taxonomy of smplfcaton methods Hoppe et al, Mesh optmzaton Hoppe, Progressve meshes Smplfcaton of 3D Meshes 1 Motvaton Hgh detaled meshes becomng

More information

Virtual Memory. Background. No. 10. Virtual Memory: concept. Logical Memory Space (review) Demand Paging(1) Virtual Memory

Virtual Memory. Background. No. 10. Virtual Memory: concept. Logical Memory Space (review) Demand Paging(1) Virtual Memory Background EECS. Operatng System Fundamentals No. Vrtual Memory Prof. Hu Jang Department of Electrcal Engneerng and Computer Scence, York Unversty Memory-management methods normally requres the entre process

More information

All-Pairs Shortest Paths. Approximate All-Pairs shortest paths Approximate distance oracles Spanners and Emulators. Uri Zwick Tel Aviv University

All-Pairs Shortest Paths. Approximate All-Pairs shortest paths Approximate distance oracles Spanners and Emulators. Uri Zwick Tel Aviv University Approxmate All-Pars shortest paths Approxmate dstance oracles Spanners and Emulators Ur Zwck Tel Avv Unversty Summer School on Shortest Paths (PATH05 DIKU, Unversty of Copenhagen All-Pars Shortest Paths

More information

A mathematical programming approach to the analysis, design and scheduling of offshore oilfields

A mathematical programming approach to the analysis, design and scheduling of offshore oilfields 17 th European Symposum on Computer Aded Process Engneerng ESCAPE17 V. Plesu and P.S. Agach (Edtors) 2007 Elsever B.V. All rghts reserved. 1 A mathematcal programmng approach to the analyss, desgn and

More information

2D Raster Graphics. Integer grid Sequential (left-right, top-down) scan. Computer Graphics

2D Raster Graphics. Integer grid Sequential (left-right, top-down) scan. Computer Graphics 2D Graphcs 2D Raster Graphcs Integer grd Sequental (left-rght, top-down scan j Lne drawng A ver mportant operaton used frequentl, block dagrams, bar charts, engneerng drawng, archtecture plans, etc. curves

More information

Private Information Retrieval (PIR)

Private Information Retrieval (PIR) 2 Levente Buttyán Problem formulaton Alce wants to obtan nformaton from a database, but she does not want the database to learn whch nformaton she wanted e.g., Alce s an nvestor queryng a stock-market

More information

Hierarchical clustering for gene expression data analysis

Hierarchical clustering for gene expression data analysis Herarchcal clusterng for gene expresson data analyss Gorgo Valentn e-mal: valentn@ds.unm.t Clusterng of Mcroarray Data. Clusterng of gene expresson profles (rows) => dscovery of co-regulated and functonally

More information

Querying by sketch geographical databases. Yu Han 1, a *

Querying by sketch geographical databases. Yu Han 1, a * 4th Internatonal Conference on Sensors, Measurement and Intellgent Materals (ICSMIM 2015) Queryng by sketch geographcal databases Yu Han 1, a * 1 Department of Basc Courses, Shenyang Insttute of Artllery,

More information

Brave New World Pseudocode Reference

Brave New World Pseudocode Reference Brave New World Pseudocode Reference Pseudocode s a way to descrbe how to accomplsh tasks usng basc steps lke those a computer mght perform. In ths week s lab, you'll see how a form of pseudocode can be

More information

Cluster Analysis of Electrical Behavior

Cluster Analysis of Electrical Behavior Journal of Computer and Communcatons, 205, 3, 88-93 Publshed Onlne May 205 n ScRes. http://www.scrp.org/ournal/cc http://dx.do.org/0.4236/cc.205.350 Cluster Analyss of Electrcal Behavor Ln Lu Ln Lu, School

More information

Report on On-line Graph Coloring

Report on On-line Graph Coloring 2003 Fall Semester Comp 670K Onlne Algorthm Report on LO Yuet Me (00086365) cndylo@ust.hk Abstract Onlne algorthm deals wth data that has no future nformaton. Lots of examples demonstrate that onlne algorthm

More information

Open Access A New Algorithm for the Shortest Path of Touring Disjoint Convex Polygons

Open Access A New Algorithm for the Shortest Path of Touring Disjoint Convex Polygons Send Orders for Reprnts to reprnts@benthamscence.ae 1364 The Open Automaton and Control Systems Journal, 2015, 7, 1364-1368 Open Access A New Algorthm for the Shortest Path of Tourng Dsjont Convex Polygons

More information

VISUAL SELECTION OF SURFACE FEATURES DURING THEIR GEOMETRIC SIMULATION WITH THE HELP OF COMPUTER TECHNOLOGIES

VISUAL SELECTION OF SURFACE FEATURES DURING THEIR GEOMETRIC SIMULATION WITH THE HELP OF COMPUTER TECHNOLOGIES UbCC 2011, Volume 6, 5002981-x manuscrpts OPEN ACCES UbCC Journal ISSN 1992-8424 www.ubcc.org VISUAL SELECTION OF SURFACE FEATURES DURING THEIR GEOMETRIC SIMULATION WITH THE HELP OF COMPUTER TECHNOLOGIES

More information

TN348: Openlab Module - Colocalization

TN348: Openlab Module - Colocalization TN348: Openlab Module - Colocalzaton Topc The Colocalzaton module provdes the faclty to vsualze and quantfy colocalzaton between pars of mages. The Colocalzaton wndow contans a prevew of the two mages

More information

Course Introduction. Algorithm 8/31/2017. COSC 320 Advanced Data Structures and Algorithms. COSC 320 Advanced Data Structures and Algorithms

Course Introduction. Algorithm 8/31/2017. COSC 320 Advanced Data Structures and Algorithms. COSC 320 Advanced Data Structures and Algorithms Course Introducton Course Topcs Exams, abs, Proects A quc loo at a few algorthms 1 Advanced Data Structures and Algorthms Descrpton: We are gong to dscuss algorthm complexty analyss, algorthm desgn technques

More information

Skew Angle Estimation and Correction of Hand Written, Textual and Large areas of Non-Textual Document Images: A Novel Approach

Skew Angle Estimation and Correction of Hand Written, Textual and Large areas of Non-Textual Document Images: A Novel Approach Angle Estmaton and Correcton of Hand Wrtten, Textual and Large areas of Non-Textual Document Images: A Novel Approach D.R.Ramesh Babu Pyush M Kumat Mahesh D Dhannawat PES Insttute of Technology Research

More information

Active Contours/Snakes

Active Contours/Snakes Actve Contours/Snakes Erkut Erdem Acknowledgement: The sldes are adapted from the sldes prepared by K. Grauman of Unversty of Texas at Austn Fttng: Edges vs. boundares Edges useful sgnal to ndcate occludng

More information

COMPLETE CALCULATION OF DISCONNECTION PROBABILITY IN PLANAR GRAPHS. G. Tsitsiashvili. IAM, FEB RAS, Vladivostok, Russia s:

COMPLETE CALCULATION OF DISCONNECTION PROBABILITY IN PLANAR GRAPHS. G. Tsitsiashvili. IAM, FEB RAS, Vladivostok, Russia  s: G. Tstsashvl COMPLETE CALCULATION OF ISCONNECTION PROBABILITY IN PLANAR GRAPHS RT&A # 0 (24) (Vol.) 202, March COMPLETE CALCULATION OF ISCONNECTION PROBABILITY IN PLANAR GRAPHS G. Tstsashvl IAM, FEB RAS,

More information

FEATURE EXTRACTION. Dr. K.Vijayarekha. Associate Dean School of Electrical and Electronics Engineering SASTRA University, Thanjavur

FEATURE EXTRACTION. Dr. K.Vijayarekha. Associate Dean School of Electrical and Electronics Engineering SASTRA University, Thanjavur FEATURE EXTRACTION Dr. K.Vjayarekha Assocate Dean School of Electrcal and Electroncs Engneerng SASTRA Unversty, Thanjavur613 41 Jont Intatve of IITs and IISc Funded by MHRD Page 1 of 8 Table of Contents

More information

Accounting for the Use of Different Length Scale Factors in x, y and z Directions

Accounting for the Use of Different Length Scale Factors in x, y and z Directions 1 Accountng for the Use of Dfferent Length Scale Factors n x, y and z Drectons Taha Soch (taha.soch@kcl.ac.uk) Imagng Scences & Bomedcal Engneerng, Kng s College London, The Rayne Insttute, St Thomas Hosptal,

More information

Non-Split Restrained Dominating Set of an Interval Graph Using an Algorithm

Non-Split Restrained Dominating Set of an Interval Graph Using an Algorithm Internatonal Journal of Advancements n Research & Technology, Volume, Issue, July- ISS - on-splt Restraned Domnatng Set of an Interval Graph Usng an Algorthm ABSTRACT Dr.A.Sudhakaraah *, E. Gnana Deepka,

More information

Feature Reduction and Selection

Feature Reduction and Selection Feature Reducton and Selecton Dr. Shuang LIANG School of Software Engneerng TongJ Unversty Fall, 2012 Today s Topcs Introducton Problems of Dmensonalty Feature Reducton Statstc methods Prncpal Components

More information

Sorting. Sorting. Why Sort? Consistent Ordering

Sorting. Sorting. Why Sort? Consistent Ordering Sortng CSE 6 Data Structures Unt 15 Readng: Sectons.1-. Bubble and Insert sort,.5 Heap sort, Secton..6 Radx sort, Secton.6 Mergesort, Secton. Qucksort, Secton.8 Lower bound Sortng Input an array A of data

More information

Array transposition in CUDA shared memory

Array transposition in CUDA shared memory Array transposton n CUDA shared memory Mke Gles February 19, 2014 Abstract Ths short note s nspred by some code wrtten by Jeremy Appleyard for the transposton of data through shared memory. I had some

More information

Explicit Formulas and Efficient Algorithm for Moment Computation of Coupled RC Trees with Lumped and Distributed Elements

Explicit Formulas and Efficient Algorithm for Moment Computation of Coupled RC Trees with Lumped and Distributed Elements Explct Formulas and Effcent Algorthm for Moment Computaton of Coupled RC Trees wth Lumped and Dstrbuted Elements Qngan Yu and Ernest S.Kuh Electroncs Research Lab. Unv. of Calforna at Berkeley Berkeley

More information

Lobachevsky State University of Nizhni Novgorod. Polyhedron. Quick Start Guide

Lobachevsky State University of Nizhni Novgorod. Polyhedron. Quick Start Guide Lobachevsky State Unversty of Nzhn Novgorod Polyhedron Quck Start Gude Nzhn Novgorod 2016 Contents Specfcaton of Polyhedron software... 3 Theoretcal background... 4 1. Interface of Polyhedron... 6 1.1.

More information

On Some Entertaining Applications of the Concept of Set in Computer Science Course

On Some Entertaining Applications of the Concept of Set in Computer Science Course On Some Entertanng Applcatons of the Concept of Set n Computer Scence Course Krasmr Yordzhev *, Hrstna Kostadnova ** * Assocate Professor Krasmr Yordzhev, Ph.D., Faculty of Mathematcs and Natural Scences,

More information

CE 221 Data Structures and Algorithms

CE 221 Data Structures and Algorithms CE 1 ata Structures and Algorthms Chapter 4: Trees BST Text: Read Wess, 4.3 Izmr Unversty of Economcs 1 The Search Tree AT Bnary Search Trees An mportant applcaton of bnary trees s n searchng. Let us assume

More information

More on the Linear k-arboricity of Regular Graphs R. E. L. Aldred Department of Mathematics and Statistics University of Otago P.O. Box 56, Dunedin Ne

More on the Linear k-arboricity of Regular Graphs R. E. L. Aldred Department of Mathematics and Statistics University of Otago P.O. Box 56, Dunedin Ne More on the Lnear k-arborcty of Regular Graphs R E L Aldred Department of Mathematcs and Statstcs Unversty of Otago PO Box 56, Dunedn New Zealand Ncholas C Wormald Department of Mathematcs Unversty of

More information

Fitting: Deformable contours April 26 th, 2018

Fitting: Deformable contours April 26 th, 2018 4/6/08 Fttng: Deformable contours Aprl 6 th, 08 Yong Jae Lee UC Davs Recap so far: Groupng and Fttng Goal: move from array of pxel values (or flter outputs) to a collecton of regons, objects, and shapes.

More information

UB at GeoCLEF Department of Geography Abstract

UB at GeoCLEF Department of Geography   Abstract UB at GeoCLEF 2006 Mguel E. Ruz (1), Stuart Shapro (2), June Abbas (1), Slva B. Southwck (1) and Davd Mark (3) State Unversty of New York at Buffalo (1) Department of Lbrary and Informaton Studes (2) Department

More information

Tsinghua University at TAC 2009: Summarizing Multi-documents by Information Distance

Tsinghua University at TAC 2009: Summarizing Multi-documents by Information Distance Tsnghua Unversty at TAC 2009: Summarzng Mult-documents by Informaton Dstance Chong Long, Mnle Huang, Xaoyan Zhu State Key Laboratory of Intellgent Technology and Systems, Tsnghua Natonal Laboratory for

More information

MULTISPECTRAL IMAGES CLASSIFICATION BASED ON KLT AND ATR AUTOMATIC TARGET RECOGNITION

MULTISPECTRAL IMAGES CLASSIFICATION BASED ON KLT AND ATR AUTOMATIC TARGET RECOGNITION MULTISPECTRAL IMAGES CLASSIFICATION BASED ON KLT AND ATR AUTOMATIC TARGET RECOGNITION Paulo Quntlano 1 & Antono Santa-Rosa 1 Federal Polce Department, Brasla, Brazl. E-mals: quntlano.pqs@dpf.gov.br and

More information

Analysis of Continuous Beams in General

Analysis of Continuous Beams in General Analyss of Contnuous Beams n General Contnuous beams consdered here are prsmatc, rgdly connected to each beam segment and supported at varous ponts along the beam. onts are selected at ponts of support,

More information

Machine Learning: Algorithms and Applications

Machine Learning: Algorithms and Applications 14/05/1 Machne Learnng: Algorthms and Applcatons Florano Zn Free Unversty of Bozen-Bolzano Faculty of Computer Scence Academc Year 011-01 Lecture 10: 14 May 01 Unsupervsed Learnng cont Sldes courtesy of

More information

Some Tutorial about the Project. Computer Graphics

Some Tutorial about the Project. Computer Graphics Some Tutoral about the Project Lecture 6 Rastersaton, Antalasng, Texture Mappng, I have already covered all the topcs needed to fnsh the 1 st practcal Today, I wll brefly explan how to start workng on

More information

NAG Fortran Library Chapter Introduction. G10 Smoothing in Statistics

NAG Fortran Library Chapter Introduction. G10 Smoothing in Statistics Introducton G10 NAG Fortran Lbrary Chapter Introducton G10 Smoothng n Statstcs Contents 1 Scope of the Chapter... 2 2 Background to the Problems... 2 2.1 Smoothng Methods... 2 2.2 Smoothng Splnes and Regresson

More information

Line Clipping by Convex and Nonconvex Polyhedra in E 3

Line Clipping by Convex and Nonconvex Polyhedra in E 3 Lne Clppng by Convex and Nonconvex Polyhedra n E 3 Václav Skala 1 Department of Informatcs and Computer Scence Unversty of West Bohema Unverztní 22, Box 314, 306 14 Plzeò Czech Republc e-mal: skala@kv.zcu.cz

More information