Enhanced Face Detection Technique Based on Color Correction Approach and SMQT Features

Size: px
Start display at page:

Download "Enhanced Face Detection Technique Based on Color Correction Approach and SMQT Features"

Transcription

1 Journal of Software Engneerng and Applcatons, 2013, 6, Publshed Onlne October 2013 ( 519 Enhanced Face Detecton Technque Based on Color Correcton Approach and SMQT Features Mohamed A. El-Sayed 1,2, Nora G. Ahmed 3 1 Department of Mathematcs, Faculty of Scence, Fayoum Unversty, Al Fayoum, Egypt; 2 Department of Computer Scence, Taf Unversty, Al Hawyah, KSA; 3 Department of Mathematcs, Faculty of Scence, Sohag Unversty, Sohag, Egypt. Emal: mas06@fayoum.edu.eg Receved August 2 nd, 2013; revsed September 1 st, 2013; accepted September 8 th, 2013 Copyrght 2013 Mohamed A. El-Sayed, Nora G. Ahmed. Ths s an open access artcle dstrbuted under the Creatve Commons Attrbuton Lcense, whch permts unrestrcted use, dstrbuton, and reproducton n any medum, provded the orgnal work s properly cted. ABSTRACT Face detecton s consdered as a challengng problem n the feld of mage analyss and computer vson. There are many researches n ths area, but because of ts mportance, t needs to be further developed. Successve Mean Quantzaton Transform (SMQT) for llumnaton and sensor nsenstve operaton and Sparse Network of Wnnow (SNoW) to speed up the orgnal classfer based detecton technque presented such a good result. In ths paper we use the Mean of Medans of CbCr (MMCbCr) color correcton approach to enhance the combned SMQT features and SNoW classfer detecton technque. The proposed technque s appled on color mages gathered from varous sources such as Internet, and Georga Database. Expermental results show that the detecton performance of the proposed method s more effectve and accurate compared to SFSC method. Keywords: Face Detecton; Color Correcton; MMCbCr; SMQT Features 1. Introducton Face detecton s a computer technology that determnes the locatons and szes of human s n dgtal mages. It detects facal features and gnores anythng else, such as buldngs, trees and bodes [1-3]. In recent years, recognton has attracted much attenton and ts research has rapdly expanded by not only engneers but also neuroscentsts, snce t has many potental applcatons n computer vson communcaton and automatc access control system. Especally, detecton s an mportant part of recognton as the frst step of automatc recognton. However, detecton s not straghtforward because t has lots of varatons of mage appearance, such as pose varaton (front, non-front), occluson, mage orentaton, llumnatng condton and facal expresson [4,5]. Up to now, much work has been done n detectng and locatng s n mages and there are many detecton methods, such as SMQT Features and SNoW Classfer Method (SFSC) [6], Effcent and Rank Defcent Face Detecton Method (ERDFD) [7], Gabor-Feature Extracton and Neural Network Method (GFENN) [8], an effcent canddates selector Features Method (EFCSF) [9] and Neural network based [10]. Colors of the mages provde useful nformaton for many vson applcatons. As a result, dfferent cameras typcally produce dfferent color values for the same objects or scenes, as llustrated n Fgure 1. These dfferences complcate the task of computer vson applcatons nvolvng the use of more than one camera. A color correcton approach s thus requred to correct the mages so that colors of the same object appear to be smlar n the output from each camera [11]. There were a number of Color Correcton approaches, ncludng GW approach, WP approach, MGWWP approach, Stretch approach and MMCbCr approach [12]. In ths paper, for detecton we use (SFSC) method: local SMQT features whch can be used as feature extracton for object and SNoW classfer requre for tranng. But we found that we can enhance ths method by applyng MMCbCr Color Correcton approach on the nput mages that make the process of detecton better. The outlne of the paper s as follows. Introducton about detecton methods s presented n Secton 1. Secton 2 dscusses the challenges on detecton technques. Secton 3 explans the proposed method that uses color correcton approach to enhance SFSC

2 520 Enhanced Face Detecton Technque Based on Color Correcton Approach and SMQT Features detecton method. Secton 4 descrbes the stage of local SMQT features. Secton 5 presents the concept of splt up SNoW classfer. Secton 6 explans the detecton tranng and classfcaton. In Secton 7, we have presented the effectveness of proposed algorthm. The proposed technque s appled on color mages gathered from varous sources such as Internet, UCD Face Image Database and Georga Database. Also, we compare the results of the algorthm wth SFSC method. Conclusons are presented n Secton Challenges on Face Detecton Technques The problem s further complcated by dfferng lghtng condtons, mage qualtes and geometres, as well as the possblty of partal occluson and dsguse. An deal detector would therefore be able to detect the presence of any under any set of lghtng condtons, orentaton, and camera dstance upon any background. Mng-Hsuan, et al. [1] Summarze the challenges assocated wth detecton n the followng factors: 1) Pose: the mages of a vary due to the relatve camera- pose (frontal, 45 degree, profle, upsde down), and some facal features such as an eye or the nose may become partally or wholly occluded. 2) Presence or absence of structural components: facal features such as beards, mustaches, and glasses may or may not be present and there s a great deal of varablty among these components ncludng shape, color, and sze. 3) Facal expresson: the appearance of s s drectly affected by a person s facal expresson. 4) Occluson: s may be partally occluded by other objects. In an mage wth a group of people, some s may partally occlude other s. 5) Image orentaton: mages drectly vary for dfferent rotatons about the camera s optcal axs. 6) Imagng condtons: when the mage s formed, factors such as lghtng (spectra, source dstrbuton and ntensty) and camera characterstcs (sensor response, lenses) may change appearance n the mage. Image condton ncludes also sze, lghtng condton, dstorton, nose, and compresson. 7) Face Sze: Sze of s also make dffcult to automate a system for detecton and recognton. 8) A background varaton: s another challengng factor for detecton n cluttered scenes. Dscrmnatng wndows ncludng a from non- s more dffcult when no constrants exst on background. Some closely related problems of detecton [1]: 1) Face localzaton: ams to determne the mage poston of a sngle ; ths s a smplfed detecton problem wth the assumpton that an nput mage contans only one. 2) Face recognton or dentfcaton: compares an nput mage (probe) aganst a database (gallery) and reports a match, f any. 3) Face authentcaton s to verfy the clam of the dentty of an ndvdual n an nput mage. 4) Face trackng methods contnuously estmate the locaton and possbly the orentaton of a n an mage sequence n real tme. 5) Facal expresson recognton concerns dentfyng the affectve states (happy, sad, dsgusted, etc.) of humans. 6) Feature s used to denote a pece of nformaton whch s relevant for solvng the computatonal task related to a certan applcaton. Feature s measurable heurstc propertes of the phenomena beng observed. 3. Proposed Method In the proposed method, the goal s to detect the presence of s n an mage usng MMCbCr Color Correcton approach and SFSC method to detect s unform and non-unform background color of the scene. It s able to localze s wth dfferent szes n mages taken under varyng llumnaton condtons. The phases of the proposed method as llustrated n Fgure Color Correcton Phase In ths phase we use Mean of Medans of CbCr Color Correcton approach (MMCbCr) to correct the nput mages. The Y component contans the lumnance nformaton and the chromnance nformaton s found n the chromnance blue Cb and n the chromnance red Cr. The MMCbCr Color Correcton SFSC Face Detecton Fgure 1. Images captured by three dfferent cameras. Fgure 2. The phases of proposed method.

3 Enhanced Face Detecton Technque Based on Color Correcton Approach and SMQT Features 521 RGB components were converted to the YCbCr components usng the followng formula [12,13]. Y 0.257R 0.504G 0.098B 16 Cb 0.148R 0.291G 0.439B 128 Cr 0.439R 0.368G 0.071B 128 The followng steps summarze MMCbCr approach: 1) Transform the gven mage from RGB to YCbCr color model. 2) Calculate the medan values medan (Cb), medan (Cr) for Cb and Cr color component, and maxmum value max(y) n Y. 3) Calculate the mean values mean (Cb), mean (Cr) for Cb and Cr color component. 4) an Cr Value Medan Cb Med 2. 5) For all pxels of the mage calculate Y new, Cb new, and Cr new j j j j j j Y, Y, 235 Max Y new new Cb, Cb, Value Mean Cb Cr, Cr, Value Mean Cr new 6) Transform the mage components Y new, Cb new, and Cr new to R new G new B new. 7) Apply hstogram equalzaton on R new G new B new separately. 8) Combne R new G new B new to get the fnal color mage Face Detecton Phase In ths phase, we use SFSC method to localzng s n nput mages. Here there are three stages: 1) Local SMQT features whch can be used as feature extracton for object, 2) SNoW classfer requres for tranng, and 3) Face detecton Tranng and Classfcaton. 4. Local SMQT Features The SMQT performs an automatc structural breakdown of nformaton. These propertes wll be employed on local areas n an mage to extract llumnaton nsenstve features. Local areas can be defned n several ways. Once the local area s defned t wll be a set of pxel values. SMQTL :Dx Μ x (1) where x be one pxel and D(x) be a set of D(x) = D be pxels n local area n an mage. The resultng values are nsenstve to gan and bas. These propertes are desrable wth regard to the formaton of the whole ntensty mage I(x) whch s a product of the reflectance R(x) and the lumnance E(x). Addtonally, the nfluence of the camera can be modeled as a gan factor g and a bas term b [14]. Thus, a model of the mage can be descrbed by I x ge xrx b (2) In order to desgn a robust classfer for object detecton the reflectance should be extracted snce t contans the object structure. In general, the separaton of the reflectance and the lumnance s an ll posed problem. A common approach to solvng ths problem nvolves assumng that E(x) s spatally smooth. Archtecture Further, f the lumnance can be consdered to be constant n the chosen local area then E(x) s gven by E x E, x D (3) Gven the valdty of Equaton (3), the SMQT on the local area wll yeld llumnaton and camera-nsenstve features. Ths mples that all local patterns whch contan the same structure wll yeld the same SMQT features for a specfed level L. 5. Splt up SNoW Classfer The SNoW learnng s a sparse network of lnear unts over a feature space. One of the strong propertes of SNoW s the possblty to create lookup-tables for classfcaton. Consder a Patch W of the SMQT features M(x), then a classfer non hx M x hx M x (4) xw xw non Can be acheved usng the non table h x, the table h x and defnng a threshold for θ. Snce both tables work on the same doman, ths mples that one sngle lookup-table h h h (5) non x x x can be created for sngle lookup-table classfcaton. The tranng database contan 1, 2,, N feature patches wth the SMQT features (x) and the correspondng classes c ( or non ). The non table and the table can then be traned wth the Wnnow Update Rule. Intally both tables contan zeros. If an ndex n the table s addressed for the frst tme durng tranng, the value (weght) on that ndex s set to one. There are three tranng parameters; the threshold γ, the promoton parameter α > 1 and the demoton parameter 0< β < 1. If hx M x and c s a then promo- xw ton s conducted and s a then promoton s conducted as follows hx M x hx M xxw (6) If c s a non and hx M x then demoton takes place xw h M x h M x x W (7) x x

4 522 Enhanced Face Detecton Technque Based on Color Correcton Approach and SMQT Features Ths procedure s repeated untl no changes occur. Tranng of the non table s performed n the same manner, and fnally the sngle table s created accordng to Equaton (5). One way to speed up the classfcaton n object recognton s to create a cascade of classfers [15]. Here the full SNoW classfer wll be splt up n sub classfers to acheve ths goal. Note that there wll be no addtonal tranng of sub classfers nstead the full classfer wll be dvded. Consder all possble feature combnatons for one feature, P, 1,2,, 2L D, then 2LD 1 vx hx P, x W results n a relevance value wth respectve sgnfcance to all features n the feature patch. Sortng all the feature relevance values n the patch wll result n an mportance lst. Let W W be a subset chosen to contan the features wth the largest relevance values. Then xw x (8) h M x (9) can functon as a weak classfer, rejectng no s wthn the tranng database, but at the cost of an ncreased number of false detectons. The desred threshold used on θ' s found from the n the tranng database that results n the lowest classfcaton value from Equaton (9). Extendng the number of sub classfers can be acheved by selectng more subsets and performng the same operatons as descrbed for one sub classfer. Consder any dvson, accordng to the relevance values, of the full set WW W. Then W' has fewer features and more false detectons compared to W'' and so forth n the same manner untl the full classfer s reached. One of the advantages of ths dvson s that W'' wll use the sum result from W'. Hence, the maxmum of summatons and lookups n the table wll be the number of features n the patch W. 6. Face Detecton Tranng and Classfcaton The detector analyzes mage patches pxels s appled. Ths patch s extracted and classfed by jumpng Δx = 1and Δy = 1 pxels through the whole mage. In order to fnd s of varous szes, the mage s repeatedly downscaled and reszed wth a scale factor Sc = 1.2. To overcome the llumnaton and sensor problem, the proposed local SMQT features are extracted. Each pxel wll get one feature vector by analyzng ts vcnty. Ths feature vector can further be recalculated to an ndex m V x L (10) where V(x ) s a value from the feature vector at poston. Ths feature ndex can be calculated for all pxels whch results n the feature ndces mage. A crcular mask contanng P = 648 pxels s appled to each patch to remove background pxels, avod edge effects from possble flterng and to avod undefned pxels at rotaton operaton. The and non tables are traned wth the parameters α = 1.005, β = and γ = 200. The two traned tables are then combned nto one table accordng to Equaton (5). Gven the SNoW classfer table, the proposed splt up SNoW classfer s created. The splts are here performed on 20, 50, 100, 200 and 648 summatons. Ths settng wll remove over 90% of the background patches n the ntal stages from vdeo frames recorded n an offce envronment. Overlapped detectons are pruned usng geometrcal locaton and classfcaton scores. Each detecton s tested aganst all other detectons. If one of the area overlap ratos s over a fxed threshold, then the dfferent detectons are consdered to belong to the same. Gven that two detectons overlap each other, the detecton wth the hghest classfcaton score s kept and the other one s removed. Ths procedure s repeated untl no more overlappng detectons are found. 7. Expermental Dscusson & Results Our experments are performed usng Matlab ver. 7.4, CPU 2.13GHZ to verfy the effectveness of the proposed method. The proposed method s appled on 150 color mages gathered from varous sources such as Internet, UCD Face Image Database and Georga Database. These mages are varyng n: sze, lghtng effects, unform and nonunform background, number of person n each mage and the rotaton angle of person. Fgure 3 shows some of the output of tested mages n Fgure 4 obtaned by applyng proposed method and SFSC method. As can been seen n Fgure 3 the detecton performance of the proposed method s better than SFSC method. Fgure 5 llustrates Comparson between proposed method and SFSC method n terms of detecton rate, false postve rate and false negatve rate. As can be seen n Fgure 5, detecton rate n proposed method s better than SFSC method. The proposed method could detect approxmately 84.1% of the s correctly and SFSC method could detect approxmately 74.6% of the s correctly. Although false postve rate and false negatve rate n proposed method s less than n SFSC method. In proposed method false postve rate s 10.4% and false negatve rate s 15.9%. In SFSC method false postve rate s 22.0% and false negatve rate s 25.4%. Fgure 6 llustrates detecton tme among 150 mages n comparson of proposed method and SFSC method, as can be seen detecton tme n proposed method s a lttle bt ncreased.

5 Enhanced Face Detecton Technque Based on Color Correcton Approach and SMQT Features SFSC method 523 Proposed method Fgure 3. Detected s after applyng SFSC method and proposed method.

6 524 Enhanced Face Detecton Technque Based on Color Correcton Approach and SMQT Features Detecton Tme Image Number SFSC proposed Fgure 6. Detecton tme of the two methods. Is099p5sn fortosearch,com Is fotosearch.com 8. Concluson In ths paper, we presented a new approach for detecton usng the MMCbCr Color Correcton approach and SFSC detecton method. The whole experment s appled on 150 color mages obtaned from dfferent sources from Internet, and Georga Database. Usng matlab 7.4, the expermental results show that the proposed method s more effectve and accurate compared to SFSC detecton method. Rate (Percent) Fgure 4. Samples of test mages. Detecton Rate False postve rate False negatve rate Fgure 5. Comparson of two methods. Proposed SFSC REFERENCES [1] Y. Mng-Hsuan, K. Davd and A. Narendra, Detectng Faces n Images: A Survey, IEEE Transactons on Pattern Analyss and Machne Intellgence, Vol. 24, No. 1, 2002, pp [2] I. Km, J. Hyung Shm and J. Yang, Face Detecton, Stanford Unversty, eports/ee368group02.pdf [3] M. A. El-Sayed and N. Aboelwafa, Study of Face Recognton Approach Based on Smlarty Measures, Internatonal Journal of Computer Scence Issues (IJCSI), Vol. 9, No. 2, 2012, pp [4] M. A. El-Sayed and M. A. Khafagy, An Identfcaton System Usng Eye Detecton Based on Wavelets and Neural Networks, Internatonal Journal of Computer and Informaton Technology, Vol. 1, No. 2, 2012, pp [5] M. A. El-Sayed, Edges Detecton Based on Reny Entropy wth Splt/Merge, Computer Engneerng and Intellgent Systems (CEIS), Vol. 3, No. 9, 2012, pp [6] M. Nlsson, J. Nordberg and I. Claesson, Face Detecton Usng Local SMQT Features and Splt up SNOW Classfer, IEEE Internatonal conference on Acoustcs, Speech, and Sgnal Processng (ICASSP), Vol. 2, 2007, pp [7] W. Kenzle, G. Bakr, M. Franz and B. Schölkopf, Face Detecton Effcent and Rank Defcent, In: Y. Wess, Ed., Advances n Neural Informaton Processng Systems,

7 Enhanced Face Detecton Technque Based on Color Correcton Approach and SMQT Features 525 Vol. 17, MIT Press, Cambrdge, 2005, pp [8] Z. Shaaban, Face Detecton Methods, World Scentfc and Engneerng Academy and Socety (WSEAS), [9] J. Wu and Z.-H. Zhou, Effcent Face Canddates Selector for Face Detecton, Pattern Recognton, Vol. 36, No. 5, 2003, pp [10] H. A. Rowley, S. Baluja and T. Kanade, Neural Network-Based Face Detecton, IEEE Transactons on Pattern Analyss and Machne Intellgence, Vol. 20, No. 1, 1998, pp [11] J. Yn and J. R. Cooperstock, Color Correcton Methods wth Applcatons to Dgtal Projecton Envronments, Journal of WSCG, 2004, n press. [12] M. A. Berbar, Novel Colors Correcton Approaches for Natural Scenes and Skn Detecton Technques, Interna- tonal Journal of Vdeo & Image Processng and Network Securty IJVIPNS-IJENS, Vol. 11, No. 2, 2011, pp [13] E. Prathbha, A. Manjunath and R Lktha, RGB to YCbCr Color Converson Usng VHDL Approach, Internatonal Journal of Engneerng Research and Development, Vol. 1, No. 3, 2012, pp [14] B. Froba and A. Ernst, Face Detecton wth the Modfed Census Transform, 6th IEEE Internatonal Conference on Automatc Face and Gesture Recognton, Seoul, May 2004, pp [15] P. Vola and M. Jones, Rapd Object Detecton Usng a Boosted Cascade of Smple Features, Proceedngs of the 2001 IEEE Computer Socety Conference on Computer Vson and Pattern Recognton (CVPR), Vol. 1, 2001, pp

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

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

SLAM Summer School 2006 Practical 2: SLAM using Monocular Vision

SLAM 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 information

A Fast Visual Tracking Algorithm Based on Circle Pixels Matching

A 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 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

Fast Feature Value Searching for Face Detection

Fast Feature Value Searching for Face Detection Vol., No. 2 Computer and Informaton Scence Fast Feature Value Searchng for Face Detecton Yunyang Yan Department of Computer Engneerng Huayn Insttute of Technology Hua an 22300, Chna E-mal: areyyyke@63.com

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

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

Corner-Based Image Alignment using Pyramid Structure with Gradient Vector Similarity

Corner-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 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

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

Learning the Kernel Parameters in Kernel Minimum Distance Classifier

Learning 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 information

Learning-based License Plate Detection on Edge Features

Learning-based License Plate Detection on Edge Features Learnng-based Lcense Plate Detecton on Edge Features Wng Teng Ho, Woo Hen Yap, Yong Haur Tay Computer Vson and Intellgent Systems (CVIS) Group Unverst Tunku Abdul Rahman, Malaysa wngteng_h@yahoo.com, woohen@yahoo.com,

More information

The Research of Ellipse Parameter Fitting Algorithm of Ultrasonic Imaging Logging in the Casing Hole

The Research of Ellipse Parameter Fitting Algorithm of Ultrasonic Imaging Logging in the Casing Hole Appled Mathematcs, 04, 5, 37-3 Publshed Onlne May 04 n ScRes. http://www.scrp.org/journal/am http://dx.do.org/0.436/am.04.584 The Research of Ellpse Parameter Fttng Algorthm of Ultrasonc Imagng Loggng

More information

EYE CENTER LOCALIZATION ON A FACIAL IMAGE BASED ON MULTI-BLOCK LOCAL BINARY PATTERNS

EYE CENTER LOCALIZATION ON A FACIAL IMAGE BASED ON MULTI-BLOCK LOCAL BINARY PATTERNS P.G. Demdov Yaroslavl State Unversty Anatoly Ntn, Vladmr Khryashchev, Olga Stepanova, Igor Kostern EYE CENTER LOCALIZATION ON A FACIAL IMAGE BASED ON MULTI-BLOCK LOCAL BINARY PATTERNS Yaroslavl, 2015 Eye

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

Face Detection with Deep Learning

Face Detection with Deep Learning Face Detecton wth Deep Learnng Yu Shen Yus122@ucsd.edu A13227146 Kuan-We Chen kuc010@ucsd.edu A99045121 Yzhou Hao y3hao@ucsd.edu A98017773 Mn Hsuan Wu mhwu@ucsd.edu A92424998 Abstract The project here

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

Recognizing Faces. Outline

Recognizing Faces. Outline Recognzng Faces Drk Colbry Outlne Introducton and Motvaton Defnng a feature vector Prncpal Component Analyss Lnear Dscrmnate Analyss !"" #$""% http://www.nfotech.oulu.f/annual/2004 + &'()*) '+)* 2 ! &

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

Edge Detection in Noisy Images Using the Support Vector Machines

Edge 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 information

A Gradient Difference based Technique for Video Text Detection

A Gradient Difference based Technique for Video Text Detection A Gradent Dfference based Technque for Vdeo Text Detecton Palaahnakote Shvakumara, Trung Quy Phan and Chew Lm Tan School of Computng, Natonal Unversty of Sngapore {shva, phanquyt, tancl }@comp.nus.edu.sg

More information

An Image Fusion Approach Based on Segmentation Region

An 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 information

Local Quaternary Patterns and Feature Local Quaternary Patterns

Local Quaternary Patterns and Feature Local Quaternary Patterns Local Quaternary Patterns and Feature Local Quaternary Patterns Jayu Gu and Chengjun Lu The Department of Computer Scence, New Jersey Insttute of Technology, Newark, NJ 0102, USA Abstract - Ths paper presents

More information

A Gradient Difference based Technique for Video Text Detection

A Gradient Difference based Technique for Video Text Detection 2009 10th Internatonal Conference on Document Analyss and Recognton A Gradent Dfference based Technque for Vdeo Text Detecton Palaahnakote Shvakumara, Trung Quy Phan and Chew Lm Tan School of Computng,

More information

Classifying Acoustic Transient Signals Using Artificial Intelligence

Classifying Acoustic Transient Signals Using Artificial Intelligence Classfyng Acoustc Transent Sgnals Usng Artfcal Intellgence Steve Sutton, Unversty of North Carolna At Wlmngton (suttons@charter.net) Greg Huff, Unversty of North Carolna At Wlmngton (jgh7476@uncwl.edu)

More information

Hybrid Non-Blind Color Image Watermarking

Hybrid Non-Blind Color Image Watermarking Hybrd Non-Blnd Color Image Watermarkng Ms C.N.Sujatha 1, Dr. P. Satyanarayana 2 1 Assocate Professor, Dept. of ECE, SNIST, Yamnampet, Ghatkesar Hyderabad-501301, Telangana 2 Professor, Dept. of ECE, AITS,

More information

Positive Semi-definite Programming Localization in Wireless Sensor Networks

Positive Semi-definite Programming Localization in Wireless Sensor Networks Postve Sem-defnte Programmng Localzaton n Wreless Sensor etworks Shengdong Xe 1,, Jn Wang, Aqun Hu 1, Yunl Gu, Jang Xu, 1 School of Informaton Scence and Engneerng, Southeast Unversty, 10096, anjng Computer

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

On Modeling Variations For Face Authentication

On Modeling Variations For Face Authentication On Modelng Varatons For Face Authentcaton Xaomng Lu Tsuhan Chen B.V.K. Vjaya Kumar Department of Electrcal and Computer Engneerng, Carnege Mellon Unversty Abstract In ths paper, we present a scheme for

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

Research and Application of Fingerprint Recognition Based on MATLAB

Research 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 information

An Efficient Face Detection Method Using Adaboost and Facial Parts

An Efficient Face Detection Method Using Adaboost and Facial Parts An Effcent Face Detecton Method Usng Adaboost and Facal Parts Yasaman Heydarzadeh, Abolfazl Torogh Haghghat Computer, IT and Electronc department Azad Unversty of Qazvn Tehran, Iran heydarzadeh@ qau.ac.r,

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

Detection of an Object by using Principal Component Analysis

Detection of an Object by using Principal Component Analysis Detecton of an Object by usng Prncpal Component Analyss 1. G. Nagaven, 2. Dr. T. Sreenvasulu Reddy 1. M.Tech, Department of EEE, SVUCE, Trupath, Inda. 2. Assoc. Professor, Department of ECE, SVUCE, Trupath,

More information

An Efficient Background Updating Scheme for Real-time Traffic Monitoring

An Efficient Background Updating Scheme for Real-time Traffic Monitoring 2004 IEEE Intellgent Transportaton Systems Conference Washngton, D.C., USA, October 3-6, 2004 WeA1.3 An Effcent Background Updatng Scheme for Real-tme Traffc Montorng Suchendra M. Bhandarkar and Xngzh

More information

Classification of Face Images Based on Gender using Dimensionality Reduction Techniques and SVM

Classification of Face Images Based on Gender using Dimensionality Reduction Techniques and SVM Classfcaton of Face Images Based on Gender usng Dmensonalty Reducton Technques and SVM Fahm Mannan 260 266 294 School of Computer Scence McGll Unversty Abstract Ths report presents gender classfcaton based

More information

NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS

NUMERICAL 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 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

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

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

Palmprint Feature Extraction Using 2-D Gabor Filters

Palmprint Feature Extraction Using 2-D Gabor Filters Palmprnt Feature Extracton Usng 2-D Gabor Flters Wa Kn Kong Davd Zhang and Wenxn L Bometrcs Research Centre Department of Computng The Hong Kong Polytechnc Unversty Kowloon Hong Kong Correspondng author:

More information

Image Representation & Visualization Basic Imaging Algorithms Shape Representation and Analysis. outline

Image 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 information

Enhanced Watermarking Technique for Color Images using Visual Cryptography

Enhanced Watermarking Technique for Color Images using Visual Cryptography Informaton Assurance and Securty Letters 1 (2010) 024-028 Enhanced Watermarkng Technque for Color Images usng Vsual Cryptography Enas F. Al rawashdeh 1, Rawan I.Zaghloul 2 1 Balqa Appled Unversty, MIS

More information

An efficient method to build panoramic image mosaics

An efficient method to build panoramic image mosaics An effcent method to buld panoramc mage mosacs Pattern Recognton Letters vol. 4 003 Dae-Hyun Km Yong-In Yoon Jong-Soo Cho School of Electrcal Engneerng and Computer Scence Kyungpook Natonal Unv. Abstract

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

A Novel Adaptive Descriptor Algorithm for Ternary Pattern Textures

A 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 information

Steps for Computing the Dissimilarity, Entropy, Herfindahl-Hirschman and. Accessibility (Gravity with Competition) Indices

Steps for Computing the Dissimilarity, Entropy, Herfindahl-Hirschman and. Accessibility (Gravity with Competition) Indices Steps for Computng the Dssmlarty, Entropy, Herfndahl-Hrschman and Accessblty (Gravty wth Competton) Indces I. Dssmlarty Index Measurement: The followng formula can be used to measure the evenness between

More information

The Research of Support Vector Machine in Agricultural Data Classification

The 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 information

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

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

Real-time Motion Capture System Using One Video Camera Based on Color and Edge Distribution

Real-time Motion Capture System Using One Video Camera Based on Color and Edge Distribution Real-tme Moton Capture System Usng One Vdeo Camera Based on Color and Edge Dstrbuton YOSHIAKI AKAZAWA, YOSHIHIRO OKADA, AND KOICHI NIIJIMA Graduate School of Informaton Scence and Electrcal Engneerng,

More information

Face Recognition University at Buffalo CSE666 Lecture Slides Resources:

Face Recognition University at Buffalo CSE666 Lecture Slides Resources: Face Recognton Unversty at Buffalo CSE666 Lecture Sldes Resources: http://www.face-rec.org/algorthms/ Overvew of face recognton algorthms Correlaton - Pxel based correspondence between two face mages Structural

More information

Face Recognition Based on SVM and 2DPCA

Face Recognition Based on SVM and 2DPCA Vol. 4, o. 3, September, 2011 Face Recognton Based on SVM and 2DPCA Tha Hoang Le, Len Bu Faculty of Informaton Technology, HCMC Unversty of Scence Faculty of Informaton Scences and Engneerng, Unversty

More information

Quality Improvement Algorithm for Tetrahedral Mesh Based on Optimal Delaunay Triangulation

Quality 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 information

A PATTERN RECOGNITION APPROACH TO IMAGE SEGMENTATION

A PATTERN RECOGNITION APPROACH TO IMAGE SEGMENTATION 1 THE PUBLISHING HOUSE PROCEEDINGS OF THE ROMANIAN ACADEMY, Seres A, OF THE ROMANIAN ACADEMY Volume 4, Number 2/2003, pp.000-000 A PATTERN RECOGNITION APPROACH TO IMAGE SEGMENTATION Tudor BARBU Insttute

More information

A Background Subtraction for a Vision-based User Interface *

A Background Subtraction for a Vision-based User Interface * A Background Subtracton for a Vson-based User Interface * Dongpyo Hong and Woontack Woo KJIST U-VR Lab. {dhon wwoo}@kjst.ac.kr Abstract In ths paper, we propose a robust and effcent background subtracton

More information

A Modified Median Filter for the Removal of Impulse Noise Based on the Support Vector Machines

A Modified Median Filter for the Removal of Impulse Noise Based on the Support Vector Machines A Modfed Medan Flter for the Removal of Impulse Nose Based on the Support Vector Machnes H. GOMEZ-MORENO, S. MALDONADO-BASCON, F. LOPEZ-FERRERAS, M. UTRILLA- MANSO AND P. GIL-JIMENEZ Departamento de Teoría

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

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

Gender Classification using Interlaced Derivative Patterns

Gender Classification using Interlaced Derivative Patterns Gender Classfcaton usng Interlaced Dervatve Patterns Author Shobernejad, Ameneh, Gao, Yongsheng Publshed 2 Conference Ttle Proceedngs of the 2th Internatonal Conference on Pattern Recognton (ICPR 2) DOI

More information

An Iterative Solution Approach to Process Plant Layout using Mixed Integer Optimisation

An Iterative Solution Approach to Process Plant Layout using Mixed Integer Optimisation 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 An Iteratve Soluton Approach to Process Plant Layout usng Mxed

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

Face Tracking Using Motion-Guided Dynamic Template Matching

Face Tracking Using Motion-Guided Dynamic Template Matching ACCV2002: The 5th Asan Conference on Computer Vson, 23--25 January 2002, Melbourne, Australa. Face Trackng Usng Moton-Guded Dynamc Template Matchng Lang Wang, Tenu Tan, Wemng Hu atonal Laboratory of Pattern

More information

Kernel Collaborative Representation Classification Based on Adaptive Dictionary Learning

Kernel Collaborative Representation Classification Based on Adaptive Dictionary Learning Internatonal Journal of Intellgent Informaton Systems 2018; 7(2): 15-22 http://www.scencepublshnggroup.com/j/js do: 10.11648/j.js.20180702.11 ISSN: 2328-7675 (Prnt); ISSN: 2328-7683 (Onlne) Kernel Collaboratve

More information

A New Approach For the Ranking of Fuzzy Sets With Different Heights

A New Approach For the Ranking of Fuzzy Sets With Different Heights New pproach For the ankng of Fuzzy Sets Wth Dfferent Heghts Pushpnder Sngh School of Mathematcs Computer pplcatons Thapar Unversty, Patala-7 00 Inda pushpndersnl@gmalcom STCT ankng of fuzzy sets plays

More information

Pictures at an Exhibition

Pictures at an Exhibition 1 Pctures at an Exhbton Stephane Kwan and Karen Zhu Department of Electrcal Engneerng Stanford Unversty, Stanford, CA 9405 Emal: {skwan1, kyzhu}@stanford.edu Abstract An mage processng algorthm s desgned

More information

Learning a Class-Specific Dictionary for Facial Expression Recognition

Learning a Class-Specific Dictionary for Facial Expression Recognition BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 16, No 4 Sofa 016 Prnt ISSN: 1311-970; Onlne ISSN: 1314-4081 DOI: 10.1515/cat-016-0067 Learnng a Class-Specfc Dctonary for

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

Robust visual tracking based on Informative random fern

Robust visual tracking based on Informative random fern 5th Internatonal Conference on Computer Scences and Automaton Engneerng (ICCSAE 205) Robust vsual trackng based on Informatve random fern Hao Dong, a, Ru Wang, b School of Instrumentaton Scence and Opto-electroncs

More information

Subspace clustering. Clustering. Fundamental to all clustering techniques is the choice of distance measure between data points;

Subspace 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 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

Novel Pattern-based Fingerprint Recognition Technique Using 2D Wavelet Decomposition

Novel Pattern-based Fingerprint Recognition Technique Using 2D Wavelet Decomposition Mathematcal Methods for Informaton Scence and Economcs Novel Pattern-based Fngerprnt Recognton Technque Usng D Wavelet Decomposton TUDOR BARBU Insttute of Computer Scence of the Romanan Academy T. Codrescu,,

More information

Helsinki University Of Technology, Systems Analysis Laboratory Mat Independent research projects in applied mathematics (3 cr)

Helsinki University Of Technology, Systems Analysis Laboratory Mat Independent research projects in applied mathematics (3 cr) Helsnk Unversty Of Technology, Systems Analyss Laboratory Mat-2.08 Independent research projects n appled mathematcs (3 cr) "! #$&% Antt Laukkanen 506 R ajlaukka@cc.hut.f 2 Introducton...3 2 Multattrbute

More information

Key-Selective Patchwork Method for Audio Watermarking

Key-Selective Patchwork Method for Audio Watermarking Internatonal Journal of Dgtal Content Technology and ts Applcatons Volume 4, Number 4, July 2010 Key-Selectve Patchwork Method for Audo Watermarkng 1 Ch-Man Pun, 2 Jng-Jng Jang 1, Frst and Correspondng

More information

Brushlet Features for Texture Image Retrieval

Brushlet Features for Texture Image Retrieval DICTA00: Dgtal Image Computng Technques and Applcatons, 1 January 00, Melbourne, Australa 1 Brushlet Features for Texture Image Retreval Chbao Chen and Kap Luk Chan Informaton System Research Lab, School

More information

Discriminative Dictionary Learning with Pairwise Constraints

Discriminative Dictionary Learning with Pairwise Constraints Dscrmnatve Dctonary Learnng wth Parwse Constrants Humn Guo Zhuoln Jang LARRY S. DAVIS UNIVERSITY OF MARYLAND Nov. 6 th, Outlne Introducton/motvaton Dctonary Learnng Dscrmnatve Dctonary Learnng wth Parwse

More information

SHAPE RECOGNITION METHOD BASED ON THE k-nearest NEIGHBOR RULE

SHAPE 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 information

Open Access Early Fire Smoke Image Segmentation in a Complex Large Space

Open Access Early Fire Smoke Image Segmentation in a Complex Large Space Send Orders for Reprnts to reprnts@benthamscence.ae The Open Constructon and Buldng Technology Journal, 2015, 9, 27-31 27 Open Access Early Fre Smoke Image Segmentaton n a Complex Large Space Hu Yan 1,2,*,

More information

Hermite Splines in Lie Groups as Products of Geodesics

Hermite Splines in Lie Groups as Products of Geodesics Hermte Splnes n Le Groups as Products of Geodescs Ethan Eade Updated May 28, 2017 1 Introducton 1.1 Goal Ths document defnes a curve n the Le group G parametrzed by tme and by structural parameters n the

More information

An Entropy-Based Approach to Integrated Information Needs Assessment

An Entropy-Based Approach to Integrated Information Needs Assessment Dstrbuton Statement A: Approved for publc release; dstrbuton s unlmted. An Entropy-Based Approach to ntegrated nformaton Needs Assessment June 8, 2004 Wllam J. Farrell Lockheed Martn Advanced Technology

More information

Vol. 5, No. 3 March 2014 ISSN Journal of Emerging Trends in Computing and Information Sciences CIS Journal. All rights reserved.

Vol. 5, No. 3 March 2014 ISSN Journal of Emerging Trends in Computing and Information Sciences CIS Journal. All rights reserved. Journal of Emergng Trends n Computng and Informaton Scences 009-03 CIS Journal. All rghts reserved. http://www.csjournal.org Unhealthy Detecton n Lvestock Texture Images usng Subsampled Contourlet Transform

More information

Classifier Selection Based on Data Complexity Measures *

Classifier 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 information

Histogram of Template for Pedestrian Detection

Histogram of Template for Pedestrian Detection PAPER IEICE TRANS. FUNDAMENTALS/COMMUN./ELECTRON./INF. & SYST., VOL. E85-A/B/C/D, No. xx JANUARY 20xx Hstogram of Template for Pedestran Detecton Shaopeng Tang, Non Member, Satosh Goto Fellow Summary In

More information

FACE RECOGNITION USING MAP DISCRIMINANT ON YCBCR COLOR SPACE

FACE RECOGNITION USING MAP DISCRIMINANT ON YCBCR COLOR SPACE FAC RCOGNIION USING MAP DISCRIMINAN ON YCBCR COLOR SPAC I Gede Pasek Suta Wjaya lectrcal ngneerng Department, ngneerng Faculty, Mataram Unversty. Jl. Majapaht 62 Mataram, West Nusa enggara, Indonesa. mal:

More information

Collaboratively Regularized Nearest Points for Set Based Recognition

Collaboratively 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 information

USING LINEAR REGRESSION FOR THE AUTOMATION OF SUPERVISED CLASSIFICATION IN MULTITEMPORAL IMAGES

USING LINEAR REGRESSION FOR THE AUTOMATION OF SUPERVISED CLASSIFICATION IN MULTITEMPORAL IMAGES USING LINEAR REGRESSION FOR THE AUTOMATION OF SUPERVISED CLASSIFICATION IN MULTITEMPORAL IMAGES 1 Fetosa, R.Q., 2 Merelles, M.S.P., 3 Blos, P. A. 1,3 Dept. of Electrcal Engneerng ; Catholc Unversty of

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

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

Outline. Discriminative classifiers for image recognition. Where in the World? A nearest neighbor recognition example 4/14/2011. CS 376 Lecture 22 1

Outline. Discriminative classifiers for image recognition. Where in the World? A nearest neighbor recognition example 4/14/2011. CS 376 Lecture 22 1 4/14/011 Outlne Dscrmnatve classfers for mage recognton Wednesday, Aprl 13 Krsten Grauman UT-Austn Last tme: wndow-based generc obect detecton basc ppelne face detecton wth boostng as case study Today:

More information

Real-time Joint Tracking of a Hand Manipulating an Object from RGB-D Input

Real-time Joint Tracking of a Hand Manipulating an Object from RGB-D Input Real-tme Jont Tracng of a Hand Manpulatng an Object from RGB-D Input Srnath Srdhar 1 Franzsa Mueller 1 Mchael Zollhöfer 1 Dan Casas 1 Antt Oulasvrta 2 Chrstan Theobalt 1 1 Max Planc Insttute for Informatcs

More information

An Automatic Eye Detection Method for Gray Intensity Facial Images

An Automatic Eye Detection Method for Gray Intensity Facial Images www.ijcsi.org 272 An Automatc Eye Detecton Method for Gray Intensty Facal Images M. Hassaballah 1,2, Kenj Murakam 1, Shun Ido 1 1 Department of Computer Scence, Ehme Unversty, 790-8577, Japan 2 Department

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

An Image Compression Algorithm based on Wavelet Transform and LZW

An Image Compression Algorithm based on Wavelet Transform and LZW An Image Compresson Algorthm based on Wavelet Transform and LZW Png Luo a, Janyong Yu b School of Chongqng Unversty of Posts and Telecommuncatons, Chongqng, 400065, Chna Abstract a cylpng@63.com, b y27769864@sna.cn

More information

Using Fuzzy Logic to Enhance the Large Size Remote Sensing Images

Using 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 information

2-Dimensional Image Representation. Using Beta-Spline

2-Dimensional Image Representation. Using Beta-Spline Appled Mathematcal cences, Vol. 7, 03, no. 9, 4559-4569 HIKARI Ltd, www.m-hkar.com http://dx.do.org/0.988/ams.03.3359 -Dmensonal Image Representaton Usng Beta-plne Norm Abdul Had Faculty of Computer and

More information

EDGE DETECTION USING MULTISPECTRAL THRESHOLDING

EDGE DETECTION USING MULTISPECTRAL THRESHOLDING ISSN: 0976-90 (ONLINE) DOI: 0.97/jvp.06.084 ICTACT JOURNAL ON IMAGE AND VIDEO PROCESSING, MAY 06, VOLUME: 06, ISSUE: 04 EDGE DETECTION USING MULTISPECTRAL THRESHOLDING K.P. Svagam, S.K. Jayanth, S. Aranganayag

More information

A fast algorithm for color image segmentation

A 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 information

A New Knowledge-Based Face Image Indexing System through the Internet

A New Knowledge-Based Face Image Indexing System through the Internet Ne Knoledge-ased Face Image Indexng System through the Internet Shu-Sheng La a Geeng-Neng You b Fu-Song Syu c Hsu-Me Huang d a General Educaton Center, Chna Medcal Unversty, Taan bc Department of Multmeda

More information

Face Recognition using 3D Directional Corner Points

Face Recognition using 3D Directional Corner Points 2014 22nd Internatonal Conference on Pattern Recognton Face Recognton usng 3D Drectonal Corner Ponts Xun Yu, Yongsheng Gao School of Engneerng Grffth Unversty Nathan, QLD, Australa xun.yu@grffthun.edu.au,

More information