A new segmentation algorithm for medical volume image based on K-means clustering
|
|
- Chad Hall
- 5 years ago
- Views:
Transcription
1 Avalable onlne Journal of Chemcal and harmaceutcal Research, 2013, 5(12): Research Artcle ISSN : CODEN(USA) : JCRC5 A new segmentaton algorthm for medcal volume mage based on K-means clusterng L Xnwu Electronc Busness department, Jangx Unversty of Fnance and Economcs, Nanchang, Jangx, Chna ABSTRACT K-means algorthm s wldly used n medcal mage segmentaton for ts powerful fuzzy nformaton process ablty but the algorthm has some shortages such as low effcency n calculaton whch lmted the usage of the algorthm. Some measures are advanced to overcome the shortages of orgnal K-means algorthm and a new medcal volume mage segmentaton algorthm s presented. Frstly, accordng to the physcal means of the medcal data, the volume data feld s preprocessed to speed up succeed clusterng processng; Secondly, the mproved K-means algorthm s deduced and analyzed through mprovng cluster seed selecton method and calculaton flow and redesgnng pxel processng and operatonal prncple of orgnal K-means algorthm. Fnally, the expermental results show that the algorthm has hgh accuracy when used to segment 3D medcal mages and can mprove calculaton speed greatly. Key words: Medcal mage segmentaton, Volume segmentaton, K-means algorthm, Clusterng processng INTRODUCTION The segmentaton of 3D medcal data feld has always been an extremely challengng subject due to magng prncple, fuzzy tssue and other factors. In the past more than 20 years, people had addressed a large number of segmentaton algorthms. However, the complexty of human body structure, rregularty of tssue organs as well as dfference among dfferent ndvduals maes the segmentaton of medcal data feld no common theory so far. Although the segmentaton of 3D medcal data feld s very dffcult, t s one of the ey technologes for data feld processng and system analyss and understandng, and an extremely mportant step of data feld vsualzaton. Only can accurate segmentaton of the data feld obtan reasonable models for subsequent renderngs. It can be sad that the realzaton of 3D vsualzaton of the medcal data feld s to carry on correct and reasonable segmentaton of the mage data at frst [1]. Haralc and Shapra regard mage segmentaton as a clusterng process [2]. The clusterng means mathematcally that a large number of d -dmensonal data samples ( n unts) are clustered nto classes ( << n ) so as to maxmze the smlarty of the samples n same class and mnmze the smlarty of the samples n dfferent class. The clusterng process s to mae contnuous classfcaton of the data objects contanng several attrbutes by the clusterng algorthm automatcally, and the data s cut nto several classes by the dentfcaton of the data characterstcs. Therefore, the algorthms can be explored by the clusterng rules and the clusterng bass of varous targets found, and then based on whch, the mage s dentfed and segmented[3]. K-means algorthm s a basc dvson method n the clusterng methods and has better scalablty, so t s wdely used Cheng [4]. In addton, whle ths algorthm that taes error square and crteron functon as the clusterng crteron functon nvolves the clusterng result nto local soluton easly and mae t dependent on the ntal value, a large amount of 3D medcal mage data causes bad algorthm tmelness. In vew of these K-means algorthm shortages, a newly mproved K-means algorthm of medcal mage volume segmentaton s provded on the bass of the thnng of seeng the optmal ntal value and mprovng algorthm s procedure. 113
2 Data feld pretreatment Each voxel s gray value (or color value) s gven accordng to the people s habts or the users requrements, and not owned by substances. Therefore, the dfference of adjacent data has a certan meanng whle the absolute value of each data s of no mportance n the data feld. Ths algorthm suggests that the functon values of orgnal 3D data feld s wthn the range of n the normalzaton ntegraton, and processed data replaces orgnal data to gve the gray level value so as to provde a gray level feld for the feature extracton, decrease the post-treatment memory demand and mprove the post-treatment speed. Although t s not smple for such problems as the tme consumpton that the data feld turns 16-bt and 12-bt gray level mages nto 8-bt under the premse of eepng up the ey nformaton of the mages n the process of the normalzaton pretreatment, the process s over n data format converson of the pretreatment and any data feld wll be treated only once so as not to affect the effcency of the whole algorthm. Algorthm Desgn K-means algorthm prncple Steps for K-means clusterng algorthm can be lsted as follows [5]. (1) Select n objects as the ntal cluster seeds on prncple; (2) Repeat (3) and (4) untl no change n each cluster; (3)Reassgn each object to the most smlar cluster n terms of the value of the cluster seeds; (4)Update the cluster seeds,.e., recompute the mean value of the object n each cluster, and tae the mean value ponts of the objects as new cluster seeds. Improvement of cluster seed selecton When calculatng the K turn of clusterng seeds wth the mproved algorthm, those data n the cluster havng a great smlarty to the K-1 category seeds should be adopted to calculate ther mean ponts (geometrcal center) as the clusterng seed of the K turn and the specfc calculaton method s below. (1) For the cluster c( 1) obtaned through the K-1 turn of clusterng, the mnmum smlarty sm _ mn of the ( 1) data n the cluster to the clusterng seed s ( 1) of the cluster s calculated; (2) The data n the cluster ( 1) clusterng seed ( 1) c s calculated that has a smlarty of more than *(1 sm_ mn ) 1 ( 1) β to the s (among, β s a constant between (0-1), and the data set s recorded as cn ( 1) ; cn are calculated as the clusterng seed of the K turn. (3) The mean ponts of the data n ( 1) (a) (b) (c) Fg.1: Comparng pctures of orgnal and mproved K-means algorthm In Fg.1, (a) shows cluster of the K-1 turn and ts seeds, (b) shows cluster of the K turn and the new seeds (ntal algorthm), and cluster of the K turn and the new seeds (mproved algorthm). Indcaton of the symbols n Fg 1 can be lsted as follows: means data pont n the cluster, means seed of the cluster n the K-1 114
3 turn, means new seed the cluster n the K turn, means range are used to calculate the new seeds. other data ponts, means the ponts wthn ts As seen n Fg. 1, the new clusterng seeds are obvously movng toward the data ntensve zone. The mproved algorthm could acheve a good clusterng effect on the cluster sets contanng solated ponts. For the processng of bg sets, ths mproved algorthm, as same as -means algorthm, s relatvely flexble and hgh-effectve. Its tme complexty s O (nt), of whch, n s the number of all objects, s the number of the clusters, whle t s the teraton number of the algorthm, and generally, and t. n Improvement of algorthm flow The general K-means algorthm s a gradent ascent teraton algorthm, each tme of teraton could cause the correspondng ncrease of the target functon values, and the teraton mght be ended n the lmted steps. However, such an algorthm also has some dsadvantages, for example, the algorthm s easly trapped n the local maxmum soluton and such a soluton depends on the selecton of ntal partton. Therefore, the means algorthm s used as local searchng process to be nlad n the local search structure of the teraton n order to obtan better clusterng results through the relatonshp between balancng the renforcement of the local search and extendng the searchng range. In the mage clusterng problems, D neghborhood of a partton refers to the partton obtaned through randomly selectng D dfferent clusters n a certan partton and redstrbutng them nto other clusters. In other words, the neghborhood of the current partton means the partton obtaned through randomly selectng one cluster and redstrbutng t nto other cluster. The calculaton flow of the K-means-based teraton clusterng algorthm of local searchng clusters s dsplayed n the followng. Input: the number of the results clusters, contanng the data set of N clusters. Output: clusters, ensurng that the clusters n all clusters are smlar or correlated. Step 1 Randomly select an ntal partton = C, C,... C } and calculate the correspondng concept n { 1 2 vector c( C ), = 1,2,..., then ntalze the current maxmum target functon value fopt and determne the endng condtons of the algorthm, the parameter value ε ( ε f 0) recevng the condtons and the maxmum teratng tmes n that the target functon value s not mproved any more [6]. Step 2 Repeat. Step 3 erform the local search on target functon value fopt and ts correspondng partton. Step 4 If fopt f fopt mproved any more, and the teraton tmes t := 0. wth the means cluster clusterng algorthm to obtan a local maxmum, the current best partton s Step 5 Repeat Step 6 Randomly generate a cluster ( 1,2,... N) = ', fopt = fopt x = and repeat the followng processes[7]., the current partton s not (1) If x s beyond the tabu lst, t wll be redstrbuted nto other cluster to calculate the ncrease f of the target functon value and the tmes of teraton wthout mprovement s t : t = t + 1; whle f x s n the tabu lst, Step 6 wll be repeated. (2) If f f ε, s the partton of redstrbuton, the target functon value s nto the tabu lst, and the tabu length of other tabu objects s deducted 1. f opt = fopt + f, x s added (3) If fopt f fopt, fopt = fopt, = ', and the tmes of teraton s t :=
4 (4) If x s tested throughout all clusters and the tmes of teraton wthout mprovement s repeated. t p n, Step 6 wll be Step 7 Untl t = n means there s no mproved partton generated n the successve n tmes of teraton. Step 8 Randomly select several clusters from partton. and redstrbute them nto other clusters to obtan the new Step 9 Untl the endng condtons are met. xel processng and operatonal prncple of the algorthm In order to qucen the effcency and the ablty of the algorthm to process the large-scale data feld, the pxel operaton and processng have to comply wth the several followng prncples. (1)re-segment the data feld n the phase of the data feld segmentaton pretreatment,.e. use the methods of manual nteracton and model gudance. (2)Accordng to pror nowledge of the structure shape and the poston that medcal data feld dssected the tssue, gve the nteractve defnton to several seed ponts and tae these seed ponts as the ntal samples. (3)Accordng to the probablty dstrbuton of an establshed characterstc, drectly classfy the pxel ponts that the selected obvous characterstc belonged to a seed pont, namely the ponts that have the obvous characterstcs and defntely belong to a class wll be mared as a class drectly, and not be calculated. (4)Calculate the ponts that have no obvous characterstcs and are classfed strctly through mathematcal algorthm, namely the ponts that are possble to belong to dfferent classes only for the edge regon or the edge transtonal regon carry on the algorthm operaton and segmentaton. (5)As for these ponts to be calculated, use the dot nterlaced samplng to carry on the samplng calculaton n the space. Namely mae the samplng to calculate whether a pont belongs to A class from the surrounded seed pont A. When the calculaton of the n pont fnshes, next pont to be calculated wll be selected as n+2 but not n+1 accordng to the space order f the n pont belongs to A class; f the calculaton result s that the n+2 pont belongs to A class, the n+1 pont wll be fallen nto A class drectly; f the calculaton result s that the n+2 pont does not belong to A class, the attrbute that the n+1 pont drects towards A class wll be calculated repeatedly. Such ths reduces the blndness of the defned ntal sample ponts greatly so as to enhance the accuracy of the segmentaton, and also reduces the data quantty calculated by the algorthm greatly to enhance the algorthm effcency. Experment Confrmaton The algorthm performs the experment on C. C confguratons are 4 1.8G CU and 512M memory. In ths paper, the algorthm s smulated n the utlzaton of the data sets wth dfferent szes and varous dstrbutons. Due to the lmted space, the followng only ntroduces the segmentaton results of actual data felds measurement for a group of complex medcal organzaton MRI data feld. The date feld sze s The experment result shows the comparson wth general K-means algorthm[5]. (1) for segmentaton effect (see Fg. 2). Fg. 2 (a) s orgnal mage, Fg.2 (b) clustered and segmented by general K-means algorthm lacs many detals obvously, and Fg.2 (c) segmented by ths paper s algorthm has hgh segmentaton precson and good vsual effects; (2) ths paper s algorthm s also mproved sgnfcantly from the algorthm effcency (see Tab. 1). CONCLUSION General K-means clusterng algorthm s vulnerable to the local soluton n practcal applcaton, so ths paper fully utlzes pror nowledge of the segmentaton object to perform several pretreatments n the course of detaled computaton by the thnng of seeng the optmal ntal value n several samplngs and one clusterng as well as the mproved K-means clusterng algorthm procedure. As a result, large reducton of the processng unts and great mprovement of the algorthm ant-nterference mae the algorthm mprove not merely convergence speed but also segmentaton accuracy. Besdes, the practcal applcaton of K-means clusterng segmentaton algorthm s greatly mproved n 3D medcal data feld segmentaton. 116
5 (a) (b) (c) Fg.2: Orgnal mage and segmentaton effect mage wth dfferent algorthms Tab. 1 Segmentaton effcency and accuracy of dfferent algorthm Algorthm Ths paper s algorthm General K-means algorthm Tme consumpton (s) Segmentaton accuracy 94.78% 77.58% Acnowledgement Ths wor s supported by the Natonal Natural Scence Foundaton of Chna under the grant No REFERENCES [1] Bradley aul Sweet, Managasaran Shapor. Journal of Global Optmzaton. 2010, (1): [2] Haralc R.M, Shapro L. G. Computer Graphcs and Image rocessng. 2009,(4): [3] Jao Chunln, Gao Maotang, et al. Computer Engneerng and Applcaton. 2010, (20): [4] Yang Cheng, Fan Huang. Journal of Computer Vsualzaton. 2012, (11): [5] Tang Johnson, Brynjolsfson Rong et al. Journal of Computer Engneerng. 2013, (1): [6] Y Smth. IEEE Computers. 2012,(3): [7] Zhang Yangfu, Mao Jnln. Journal of Computer Applcaton, 2012,(8):
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 informationNUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS
ARPN Journal of Engneerng and Appled Scences 006-017 Asan Research Publshng Network (ARPN). All rghts reserved. NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS Igor Grgoryev, Svetlana
More informationAn Improved Image Segmentation Algorithm Based on the Otsu Method
3th ACIS Internatonal Conference on Software Engneerng, Artfcal Intellgence, Networkng arallel/dstrbuted Computng An Improved Image Segmentaton Algorthm Based on the Otsu Method Mengxng Huang, enjao Yu,
More informationHierarchical 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 informationSubspace clustering. Clustering. Fundamental to all clustering techniques is the choice of distance measure between data points;
Subspace clusterng Clusterng Fundamental to all clusterng technques s the choce of dstance measure between data ponts; D q ( ) ( ) 2 x x = x x, j k = 1 k jk Squared Eucldean dstance Assumpton: All features
More informationBIN XIA et al: AN IMPROVED K-MEANS ALGORITHM BASED ON CLOUD PLATFORM FOR DATA MINING
An Improved K-means Algorthm based on Cloud Platform for Data Mnng Bn Xa *, Yan Lu 2. School of nformaton and management scence, Henan Agrcultural Unversty, Zhengzhou, Henan 450002, P.R. Chna 2. College
More informationMaximum Variance Combined with Adaptive Genetic Algorithm for Infrared Image Segmentation
Internatonal Conference on Logstcs Engneerng, Management and Computer Scence (LEMCS 5) Maxmum Varance Combned wth Adaptve Genetc Algorthm for Infrared Image Segmentaton Huxuan Fu College of Automaton Harbn
More informationA Fast Visual Tracking Algorithm Based on Circle Pixels Matching
A Fast Vsual Trackng Algorthm Based on Crcle Pxels Matchng Zhqang Hou hou_zhq@sohu.com Chongzhao Han czhan@mal.xjtu.edu.cn Ln Zheng Abstract: A fast vsual trackng algorthm based on crcle pxels matchng
More informationA Novel Adaptive Descriptor Algorithm for Ternary Pattern Textures
A Novel Adaptve Descrptor Algorthm for Ternary Pattern Textures Fahuan Hu 1,2, Guopng Lu 1 *, Zengwen Dong 1 1.School of Mechancal & Electrcal Engneerng, Nanchang Unversty, Nanchang, 330031, Chna; 2. School
More informationContent 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 informationLearning the Kernel Parameters in Kernel Minimum Distance Classifier
Learnng the Kernel Parameters n Kernel Mnmum Dstance Classfer Daoqang Zhang 1,, Songcan Chen and Zh-Hua Zhou 1* 1 Natonal Laboratory for Novel Software Technology Nanjng Unversty, Nanjng 193, Chna Department
More informationA fast algorithm for color image segmentation
Unersty of Wollongong Research Onlne Faculty of Informatcs - Papers (Arche) Faculty of Engneerng and Informaton Scences 006 A fast algorthm for color mage segmentaton L. Dong Unersty of Wollongong, lju@uow.edu.au
More informationThe Comparison of Calibration Method of Binocular Stereo Vision System Ke Zhang a *, Zhao Gao b
3rd Internatonal Conference on Materal, Mechancal and Manufacturng Engneerng (IC3ME 2015) The Comparson of Calbraton Method of Bnocular Stereo Vson System Ke Zhang a *, Zhao Gao b College of Engneerng,
More informationA 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 informationApplication of adaptive MRF based on region in segmentation of microscopic image
Lhong L, Mnglu Zhang, Yazhou Wu, Lngyu Sun Applcaton of adaptve MRF based on regon n segmentaton of mcroscopc mage Lhong L 1,2,Mnglu Zhang 2,Yazhou Wu 1,Lngyu Sun 2 1 School of Informaton and Electronc
More informationAn Image Fusion Approach Based on Segmentation Region
Rong Wang, L-Qun Gao, Shu Yang, Yu-Hua Cha, and Yan-Chun Lu An Image Fuson Approach Based On Segmentaton Regon An Image Fuson Approach Based on Segmentaton Regon Rong Wang, L-Qun Gao, Shu Yang 3, Yu-Hua
More informationA 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 informationBioTechnology. An Indian Journal FULL PAPER. Trade Science Inc.
[Type text] [Type text] [Type text] ISSN : 0974-74 Volume 0 Issue BoTechnology 04 An Indan Journal FULL PAPER BTAIJ 0() 04 [684-689] Revew on Chna s sports ndustry fnancng market based on market -orented
More informationMULTISPECTRAL 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 informationAPPLICATION OF IMPROVED K-MEANS ALGORITHM IN THE DELIVERY LOCATION
An Open Access, Onlne Internatonal Journal Avalable at http://www.cbtech.org/pms.htm 2016 Vol. 6 (2) Aprl-June, pp. 11-17/Sh Research Artcle APPLICATION OF IMPROVED K-MEANS ALGORITHM IN THE DELIVERY LOCATION
More informationDetermining the Optimal Bandwidth Based on Multi-criterion Fusion
Proceedngs of 01 4th Internatonal Conference on Machne Learnng and Computng IPCSIT vol. 5 (01) (01) IACSIT Press, Sngapore Determnng the Optmal Bandwdth Based on Mult-crteron Fuson Ha-L Lang 1+, Xan-Mn
More informationThe Research of Support Vector Machine in Agricultural Data Classification
The Research of Support Vector Machne n Agrcultural Data Classfcaton Le Sh, Qguo Duan, Xnmng Ma, Me Weng College of Informaton and Management Scence, HeNan Agrcultural Unversty, Zhengzhou 45000 Chna Zhengzhou
More informationTECHNIQUE OF FORMATION HOMOGENEOUS SAMPLE SAME OBJECTS. Muradaliyev A.Z.
TECHNIQUE OF FORMATION HOMOGENEOUS SAMPLE SAME OBJECTS Muradalyev AZ Azerbajan Scentfc-Research and Desgn-Prospectng Insttute of Energetc AZ1012, Ave HZardab-94 E-mal:aydn_murad@yahoocom Importance of
More informationAvailable online at Available online at Advanced in Control Engineering and Information Science
Avalable onlne at wwwscencedrectcom Avalable onlne at wwwscencedrectcom Proceda Proceda Engneerng Engneerng 00 (2011) 15000 000 (2011) 1642 1646 Proceda Engneerng wwwelsevercom/locate/proceda Advanced
More informationRobust Shot Boundary Detection from Video Using Dynamic Texture
Sensors & Transducers 204 by IFSA Publshng, S. L. http://www.sensorsportal.com Robust Shot Boundary Detecton from Vdeo Usng Dynamc Teture, 3 Peng Tale, 2 Zhang Wenjun School of Communcaton & Informaton
More informationMathematics 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 informationA Clustering Algorithm for Key Frame Extraction Based on Density Peak
Journal of Computer and Communcatons, 2018, 6, 118-128 http://www.scrp.org/ournal/cc ISSN Onlne: 2327-5227 ISSN Prnt: 2327-5219 A Clusterng Algorthm for Key Frame Extracton Based on Densty Peak Hong Zhao
More informationA CALCULATION METHOD OF DEEP WEB ENTITIES RECOGNITION
A CALCULATION METHOD OF DEEP WEB ENTITIES RECOGNITION 1 FENG YONG, DANG XIAO-WAN, 3 XU HONG-YAN School of Informaton, Laonng Unversty, Shenyang Laonng E-mal: 1 fyxuhy@163.com, dangxaowan@163.com, 3 xuhongyan_lndx@163.com
More informationOutline. Type of Machine Learning. Examples of Application. Unsupervised Learning
Outlne Artfcal Intellgence and ts applcatons Lecture 8 Unsupervsed Learnng Professor Danel Yeung danyeung@eee.org Dr. Patrck Chan patrckchan@eee.org South Chna Unversty of Technology, Chna Introducton
More informationResearch and Application of Fingerprint Recognition Based on MATLAB
Send Orders for Reprnts to reprnts@benthamscence.ae The Open Automaton and Control Systems Journal, 205, 7, 07-07 Open Access Research and Applcaton of Fngerprnt Recognton Based on MATLAB Nng Lu* Department
More informationEdge Detection in Noisy Images Using the Support Vector Machines
Edge Detecton n Nosy Images Usng the Support Vector Machnes Hlaro Gómez-Moreno, Saturnno Maldonado-Bascón, Francsco López-Ferreras Sgnal Theory and Communcatons Department. Unversty of Alcalá Crta. Madrd-Barcelona
More informationThe 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 informationVISUAL 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 informationSuppression for Luminance Difference of Stereo Image-Pair Based on Improved Histogram Equalization
Suppresson for Lumnance Dfference of Stereo Image-Par Based on Improved Hstogram Equalzaton Zhao Llng,, Zheng Yuhu 3, Sun Quansen, Xa Deshen School of Computer Scence and Technology, NJUST, Nanjng, Chna.School
More informationSmoothing Spline ANOVA for variable screening
Smoothng Splne ANOVA for varable screenng a useful tool for metamodels tranng and mult-objectve optmzaton L. Rcco, E. Rgon, A. Turco Outlne RSM Introducton Possble couplng Test case MOO MOO wth Game Theory
More informationProblem 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 informationClassifier Selection Based on Data Complexity Measures *
Classfer Selecton Based on Data Complexty Measures * Edth Hernández-Reyes, J.A. Carrasco-Ochoa, and J.Fco. Martínez-Trndad Natonal Insttute for Astrophyscs, Optcs and Electroncs, Lus Enrque Erro No.1 Sta.
More informationResearch of Dynamic Access to Cloud Database Based on Improved Pheromone Algorithm
, pp.197-202 http://dx.do.org/10.14257/dta.2016.9.5.20 Research of Dynamc Access to Cloud Database Based on Improved Pheromone Algorthm Yongqang L 1 and Jn Pan 2 1 (Software Technology Vocatonal College,
More informationFeature 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 informationLobachevsky 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 informationRemote Sensing Image Retrieval Algorithm based on MapReduce and Characteristic Information
Remote Sensng Image Retreval Algorthm based on MapReduce and Characterstc Informaton Zhang Meng 1, 1 Computer School, Wuhan Unversty Hube, Wuhan430097 Informaton Center, Wuhan Unversty Hube, Wuhan430097
More informationFast 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 informationVector Quantization Codebook Design and Application Based on the Clonal Selection Algorithm
Sensors & Transducers 03 by IFSA http://www.sensorsportal.com Vector Quantzaton Codeboo Desgn and Applcaton Based on the Clonal Selecton Algorthm Menglng Zhao, Hongwe Lu School of Scence, Xdan Unversty,
More informationOutline. Self-Organizing Maps (SOM) US Hebbian Learning, Cntd. The learning rule is Hebbian like:
Self-Organzng Maps (SOM) Turgay İBRİKÇİ, PhD. Outlne Introducton Structures of SOM SOM Archtecture Neghborhoods SOM Algorthm Examples Summary 1 2 Unsupervsed Hebban Learnng US Hebban Learnng, Cntd 3 A
More informationUnsupervised Learning
Pattern Recognton Lecture 8 Outlne Introducton Unsupervsed Learnng Parametrc VS Non-Parametrc Approach Mxture of Denstes Maxmum-Lkelhood Estmates Clusterng Prof. Danel Yeung School of Computer Scence and
More informationImprovement 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 informationQuality Improvement Algorithm for Tetrahedral Mesh Based on Optimal Delaunay Triangulation
Intellgent Informaton Management, 013, 5, 191-195 Publshed Onlne November 013 (http://www.scrp.org/journal/m) http://dx.do.org/10.36/m.013.5601 Qualty Improvement Algorthm for Tetrahedral Mesh Based on
More informationApplication of Clustering Algorithm in Big Data Sample Set Optimization
Applcaton of Clusterng Algorthm n Bg Data Sample Set Optmzaton Yutang Lu 1, Qn Zhang 2 1 Department of Basc Subjects, Henan Insttute of Technology, Xnxang 453002, Chna 2 School of Mathematcs and Informaton
More informationFEATURE 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 informationJournal of Chemical and Pharmaceutical Research, 2014, 6(6): Research Article. A selective ensemble classification method on microarray data
Avalable onlne www.ocpr.com Journal of Chemcal and Pharmaceutcal Research, 2014, 6(6):2860-2866 Research Artcle ISSN : 0975-7384 CODEN(USA) : JCPRC5 A selectve ensemble classfcaton method on mcroarray
More informationContours Planning and Visual Servo Control of XXY Positioning System Using NURBS Interpolation Approach
Inventon Journal of Research Technology n Engneerng & Management (IJRTEM) ISSN: 2455-3689 www.jrtem.com olume 1 Issue 4 ǁ June. 2016 ǁ PP 16-23 Contours Plannng and sual Servo Control of XXY Postonng System
More informationModular PCA Face Recognition Based on Weighted Average
odern Appled Scence odular PCA Face Recognton Based on Weghted Average Chengmao Han (Correspondng author) Department of athematcs, Lny Normal Unversty Lny 76005, Chna E-mal: hanchengmao@163.com Abstract
More informationObject-Based Techniques for Image Retrieval
54 Zhang, Gao, & Luo Chapter VII Object-Based Technques for Image Retreval Y. J. Zhang, Tsnghua Unversty, Chna Y. Y. Gao, Tsnghua Unversty, Chna Y. Luo, Tsnghua Unversty, Chna ABSTRACT To overcome the
More informationSCALABLE AND VISUALIZATION-ORIENTED CLUSTERING FOR EXPLORATORY SPATIAL ANALYSIS
SCALABLE AND VISUALIZATION-ORIENTED CLUSTERING FOR EXPLORATORY SPATIAL ANALYSIS J.H.Guan, F.B.Zhu, F.L.Ban a School of Computer, Spatal Informaton & Dgtal Engneerng Center, Wuhan Unversty, Wuhan, 430079,
More informationCHAPTER 2 PROPOSED IMPROVED PARTICLE SWARM OPTIMIZATION
24 CHAPTER 2 PROPOSED IMPROVED PARTICLE SWARM OPTIMIZATION The present chapter proposes an IPSO approach for multprocessor task schedulng problem wth two classfcatons, namely, statc ndependent tasks and
More informationMeta-heuristics for Multidimensional Knapsack Problems
2012 4th Internatonal Conference on Computer Research and Development IPCSIT vol.39 (2012) (2012) IACSIT Press, Sngapore Meta-heurstcs for Multdmensonal Knapsack Problems Zhbao Man + Computer Scence Department,
More informationSHAPE RECOGNITION METHOD BASED ON THE k-nearest NEIGHBOR RULE
SHAPE RECOGNITION METHOD BASED ON THE k-nearest NEIGHBOR RULE Dorna Purcaru Faculty of Automaton, Computers and Electroncs Unersty of Craoa 13 Al. I. Cuza Street, Craoa RO-1100 ROMANIA E-mal: dpurcaru@electroncs.uc.ro
More informationAn Indian Journal FULL PAPER ABSTRACT KEYWORDS. Trade Science Inc.
[Type text] [Type text] [Type text] ISSN : 97-735 Volume Issue 9 BoTechnology An Indan Journal FULL PAPER BTAIJ, (9), [333-3] Matlab mult-dmensonal model-based - 3 Chnese football assocaton super league
More informationChinese Word Segmentation based on the Improved Particle Swarm Optimization Neural Networks
Chnese Word Segmentaton based on the Improved Partcle Swarm Optmzaton Neural Networks Ja He Computatonal Intellgence Laboratory School of Computer Scence and Engneerng, UESTC Chengdu, Chna Department of
More informationParallelism 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 informationHigh-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 informationAn Efficient Genetic Algorithm with Fuzzy c-means Clustering for Traveling Salesman Problem
An Effcent Genetc Algorthm wth Fuzzy c-means Clusterng for Travelng Salesman Problem Jong-Won Yoon and Sung-Bae Cho Dept. of Computer Scence Yonse Unversty Seoul, Korea jwyoon@sclab.yonse.ac.r, sbcho@cs.yonse.ac.r
More informationSkew 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 informationK-means and Hierarchical Clustering
Note to other teachers and users of these sldes. Andrew would be delghted f you found ths source materal useful n gvng your own lectures. Feel free to use these sldes verbatm, or to modfy them to ft your
More informationA 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 informationSLAM Summer School 2006 Practical 2: SLAM using Monocular Vision
SLAM Summer School 2006 Practcal 2: SLAM usng Monocular Vson Javer Cvera, Unversty of Zaragoza Andrew J. Davson, Imperal College London J.M.M Montel, Unversty of Zaragoza. josemar@unzar.es, jcvera@unzar.es,
More informationCollaboratively Regularized Nearest Points for Set Based Recognition
Academc Center for Computng and Meda Studes, Kyoto Unversty Collaboratvely Regularzed Nearest Ponts for Set Based Recognton Yang Wu, Mchhko Mnoh, Masayuk Mukunok Kyoto Unversty 9/1/013 BMVC 013 @ Brstol,
More informationWishing you all a Total Quality New Year!
Total Qualty Management and Sx Sgma Post Graduate Program 214-15 Sesson 4 Vnay Kumar Kalakband Assstant Professor Operatons & Systems Area 1 Wshng you all a Total Qualty New Year! Hope you acheve Sx sgma
More informationNetwork Intrusion Detection Based on PSO-SVM
TELKOMNIKA Indonesan Journal of Electrcal Engneerng Vol.1, No., February 014, pp. 150 ~ 1508 DOI: http://dx.do.org/10.11591/telkomnka.v1.386 150 Network Intruson Detecton Based on PSO-SVM Changsheng Xang*
More informationFPGA-based implementation of circular interpolation
Avalable onlne www.jocpr.com Journal of Chemcal and Pharmaceutcal Research, 04, 6(7):585-593 Research Artcle ISSN : 0975-7384 CODEN(USA) : JCPRC5 FPGA-based mplementaton of crcular nterpolaton Mngyu Gao,
More informationUsing Fuzzy Logic to Enhance the Large Size Remote Sensing Images
Internatonal Journal of Informaton and Electroncs Engneerng Vol. 5 No. 6 November 015 Usng Fuzzy Logc to Enhance the Large Sze Remote Sensng Images Trung Nguyen Tu Huy Ngo Hoang and Thoa Vu Van Abstract
More informationThe Discriminate Analysis and Dimension Reduction Methods of High Dimension
Open Journal of Socal Scences, 015, 3, 7-13 Publshed Onlne March 015 n ScRes. http://www.scrp.org/journal/jss http://dx.do.org/10.436/jss.015.3300 The Dscrmnate Analyss and Dmenson Reducton Methods of
More informationX- Chart Using ANOM Approach
ISSN 1684-8403 Journal of Statstcs Volume 17, 010, pp. 3-3 Abstract X- Chart Usng ANOM Approach Gullapall Chakravarth 1 and Chaluvad Venkateswara Rao Control lmts for ndvdual measurements (X) chart are
More informationTsinghua 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 informationVirtual Machine Migration based on Trust Measurement of Computer Node
Appled Mechancs and Materals Onlne: 2014-04-04 ISSN: 1662-7482, Vols. 536-537, pp 678-682 do:10.4028/www.scentfc.net/amm.536-537.678 2014 Trans Tech Publcatons, Swtzerland Vrtual Machne Mgraton based on
More informationA Load-balancing and Energy-aware Clustering Algorithm in Wireless Ad-hoc Networks
A Load-balancng and Energy-aware Clusterng Algorthm n Wreless Ad-hoc Networks Wang Jn, Shu Le, Jnsung Cho, Young-Koo Lee, Sungyoung Lee, Yonl Zhong Department of Computer Engneerng Kyung Hee Unversty,
More informationDetecting Maximum Inscribed Rectangle Based On Election Campaign Algorithm Qing-Hua XIE1,a,*, Xiang-Wei ZHANG1,b, Wen-Ge LV1,c and Si-Yuan CHENG1,d
6th Internatonal onference on Advanced Desgn and Manufacturng Engneerng (IADME 2016) Detectng Maxmum Inscrbed Rectangle Based On Electon ampagn Algorthm Qng-Hua XIE1,a,*, Xang-We ZHAG1,b, Wen-Ge LV1,c
More informationModule 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 informationDesign of Simulation Model on the Battlefield Environment ZHANG Jianli 1,a, ZHANG Lin 2,b *, JI Lijian 1,c, GUO Zhongwei 1,d
Internatonal Conference on Materals Engneerng and Informaton Technology Applcatons (MEITA 2015 Desgn of Smulaton Model on the Battlefeld Envronment ZHANG Janl 1,a, ZHANG Ln 2,b *, JI Ljan 1,c, GUO Zhongwe
More informationFitting: 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 informationCorner-Based Image Alignment using Pyramid Structure with Gradient Vector Similarity
Journal of Sgnal and Informaton Processng, 013, 4, 114-119 do:10.436/jsp.013.43b00 Publshed Onlne August 013 (http://www.scrp.org/journal/jsp) Corner-Based Image Algnment usng Pyramd Structure wth Gradent
More informationResearch on Categorization of Animation Effect Based on Data Mining
MATEC Web of Conferences 22, 0102 0 ( 2015) DOI: 10.1051/ matecconf/ 2015220102 0 C Owned by the authors, publshed by EDP Scences, 2015 Research on Categorzaton of Anmaton Effect Based on Data Mnng Na
More informationCMPS 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 informationFinite Element Analysis of Rubber Sealing Ring Resilience Behavior Qu Jia 1,a, Chen Geng 1,b and Yang Yuwei 2,c
Advanced Materals Research Onlne: 03-06-3 ISSN: 66-8985, Vol. 705, pp 40-44 do:0.408/www.scentfc.net/amr.705.40 03 Trans Tech Publcatons, Swtzerland Fnte Element Analyss of Rubber Sealng Rng Reslence Behavor
More informationCourse 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 informationFuzzy C-Means Initialized by Fixed Threshold Clustering for Improving Image Retrieval
Fuzzy -Means Intalzed by Fxed Threshold lusterng for Improvng Image Retreval NAWARA HANSIRI, SIRIPORN SUPRATID,HOM KIMPAN 3 Faculty of Informaton Technology Rangst Unversty Muang-Ake, Paholyotn Road, Patumtan,
More informationStraight Line Detection Based on Particle Swarm Optimization
Sensors & ransducers 013 b IFSA http://www.sensorsportal.com Straght Lne Detecton Based on Partcle Swarm Optmzaton Shengzhou XU, Jun IE College of computer scence, South-Central Unverst for Natonaltes,
More informationA Cluster Number Adaptive Fuzzy c-means Algorithm for Image Segmentation
, pp.9-204 http://dx.do.org/0.4257/jsp.203.6.5.7 A Cluster Number Adaptve Fuzzy c-means Algorthm for Image Segmentaton Shaopng Xu, Lngyan Hu, Xaohu Yang and Xaopng Lu,2 School of Informaton Engneerng,
More informationAnalysis on the Workspace of Six-degrees-of-freedom Industrial Robot Based on AutoCAD
Analyss on the Workspace of Sx-degrees-of-freedom Industral Robot Based on AutoCAD Jn-quan L 1, Ru Zhang 1,a, Fang Cu 1, Q Guan 1 and Yang Zhang 1 1 School of Automaton, Bejng Unversty of Posts and Telecommuncatons,
More informationAPPLICATION OF MULTIVARIATE LOSS FUNCTION FOR ASSESSMENT OF THE QUALITY OF TECHNOLOGICAL PROCESS MANAGEMENT
3. - 5. 5., Brno, Czech Republc, EU APPLICATION OF MULTIVARIATE LOSS FUNCTION FOR ASSESSMENT OF THE QUALITY OF TECHNOLOGICAL PROCESS MANAGEMENT Abstract Josef TOŠENOVSKÝ ) Lenka MONSPORTOVÁ ) Flp TOŠENOVSKÝ
More informationA Shadow Detection Method for Remote Sensing Images Using Affinity Propagation Algorithm
Proceedngs of the 009 IEEE Internatonal Conference on Systems, Man, and Cybernetcs San Antono, TX, USA - October 009 A Shadow Detecton Method for Remote Sensng Images Usng Affnty Propagaton Algorthm Huayng
More informationA Low Energy Algorithm of Wireless Sensor Networks Based on Fractal Dimension
Sensors & Transducers 2014 by IFSA Publshng, S. L. http://www.sensorsportal.com A Low Energy Algorthm of Wreless Sensor Networks ased on Fractal Dmenson Tng Dong, Chunxao Fan, Zhgang Wen School of Electronc
More informationUnsupervised Learning and Clustering
Unsupervsed Learnng and Clusterng Supervsed vs. Unsupervsed Learnng Up to now we consdered supervsed learnng scenaro, where we are gven 1. samples 1,, n 2. class labels for all samples 1,, n Ths s also
More informationImage Representation & Visualization Basic Imaging Algorithms Shape Representation and Analysis. outline
mage Vsualzaton mage Vsualzaton mage Representaton & Vsualzaton Basc magng Algorthms Shape Representaton and Analyss outlne mage Representaton & Vsualzaton Basc magng Algorthms Shape Representaton and
More informationActive 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 informationConfiguration Management in Multi-Context Reconfigurable Systems for Simultaneous Performance and Power Optimizations*
Confguraton Management n Mult-Context Reconfgurable Systems for Smultaneous Performance and Power Optmzatons* Rafael Maestre, Mlagros Fernandez Departamento de Arqutectura de Computadores y Automátca Unversdad
More informationProgramming 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 informationVirtual 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 informationDiscrete Cosine Transform Optimization in Image Compression Based on Genetic Algorithm
015 8th Internatonal Congress on Image and Sgnal Processng (CISP 015) Dscrete Cosne Transform Optmzaton n Image Compresson Based on Genetc Algorthm LIU Yuan-yuan 1 CHE He-xn 1 College of Communcaton Engneerng,
More informationRecommended Items Rating Prediction based on RBF Neural Network Optimized by PSO Algorithm
Recommended Items Ratng Predcton based on RBF Neural Network Optmzed by PSO Algorthm Chengfang Tan, Cayn Wang, Yuln L and Xx Q Abstract In order to mtgate the data sparsty and cold-start problems of recommendaton
More information