A New Facial Expression Recognition Method Based on * Local Gabor Filter Bank and PCA plus LDA
|
|
- Luke Payne
- 6 years ago
- Views:
Transcription
1 Hong-Bo Deng, Lan-Wen Jn, L-Xn Zhen, Jan-Cheng Huang A New Facal Expresson Recognton Method Based on Local Gabor Flter Bank and PCA plus LDA A New Facal Expresson Recognton Method Based on * Local Gabor Flter Bank and PCA plus LDA Hong-Bo Deng 1, Lan-Wen Jn 1, L-Xn Zhen, Jan-Cheng Huang 1 School of Electronc and Informaton Engneerng, South Chna Unversty of echnology, Guangzhou, , P.R.Chna Motorola Chna Research Center, Shangha, 10000, P.R.Chna {hbdeng, eelwn}@scut.edu.cn {L-Xn.Zhen, Jan-Cheng.Huang}@motorola.com Abstract hs paper proposes a facal expresson recognton system based on Gabor feature usng a novel local Gabor flter bank. radtonally, a global Gabor flter bank wth 5 frequences and 8 orentatons s often used to extract the Gabor feature. A lot of tme wll be nvolved to extract feature and the dmensons of such Gabor feature vector are prohbtvely hgh. A novel local Gabor flter bank wth part of frequency and orentaton parameters s proposed. In order to evaluate the performance of the local Gabor flter bank, we frst employed a two-stage feature compresson method PCA plus LDA to select and compress the Gabor feature, then adopted mnmum dstance classfer to recognze facal expresson. Expermental results show that the method s effectve for both dmenson reducton and good recognton performance n comparson wth tradtonal entre Gabor flter bank. he best average recognton rate acheves 97.33% for JAFFE facal expresson database. Keyword: Local Gabor flter bank, feature extracton, PCA, LDA, facal expresson recognton. I. Introducton Facal expressons delver rch nformaton about human emoton and play an essental role n human communcatons. In order to facltate a more ntellgent and natural human machne nterface of new multmeda products, automatc facal expresson recognton [1][18][0] had been studed world wde n the last ten years, whch has become a very actve research area n computer vson and pattern recognton. here are many approaches have been proposed for facal expresson analyss from both statc mages and mage sequences [1][18] n the lterature. In ths paper we focus on the recognton of facal expresson from sngle dgtal mages wth emphass on the feature extracton. A number of approaches have been developed for extractng * Proect sponsored by: Motorola Labs Research Foundaton (No.303D80437), NSFC (No ), GDNSF (No.003C50101, ). 86
2 Internatonal Journal of Informaton echnology Vol. 11 No features from stll mages. urk and Pentland [7] proposed Egenfaces employed prncpal component analyss (PCA). PCA [8][9] s an unsupervsed learnng method, whch treats samples of the dfferent classes n the same way. Fsherfaces proposed by Belhumeour and Hespanha [6] s a supervsed learnng method usng the category nformaton assocated wth each sample to extract the most dscrmnatory features. It has been shown that Fsherfaces performs well n many applcatons. Lyons [10] used Gabor wavelet [3][4][10][14] to code facal expressons. Nowadays varous researchers reported the model-based methods [1][18][0] for feature extracton, such as actve appearance model [19], pont dstrbuton model and labeled graphs. But those methods requre heavy computaton or manually detected feature nodes to construct the model, whch can hardly be mplemented n real-tme automatc facal expresson recognton (FER). Donato and Bartlett [1] compared varous methods of feature extracton for automatcally recognzng facal expresson, ncludng PCA, LDA, Gabor wavelet, etc. Best performances were obtaned usng the Gabor wavelet presentaton. But the computaton and memory requrement of such Gabor feature are very large and the dmenson s very hgh. he novelty of our method s to select a local Gabor flter bank, wth part of the entre m- frequency, n-orentaton set of Gabor flters, nstead of usng the entre global flter bank to extract feature. In contrast to the entre global Gabor flter bank, the use of the local Gabor flter bank can effectvely decrease the computaton and reduce the dmenson, even mprove the recognton capablty n some stuatons. For further dmensonalty reducton and good recognton performance, we adopt a two-phase framework PCA plus LDA for feature compresson and selecton n our facal expresson recognton system. he remander of the paper s organzed as follows: Secton descrbes the preprocessng procedure to get the pure expresson mage. Secton 3 presents the Gabor feature extracton and our novel local Gabor flter bank. Feature compresson based on PCA and LDA s dscussed n Secton 4. In Secton 5, experments are performed on the JAFFE facal expresson database [10] wth dfferent expermental condtons. Fnally, concluson s gven n Secton 6. II. Preprocessng Procedure Preprocessng procedure s very mportant step for facal expresson recognton. he deal output of processng s to obtan pure facal expresson mages, whch have normalzed ntensty, unform sze and shape. It also should elmnate the effect of llumnaton and lghtng. he preprocessng procedure of our FER system performs the followng fve steps n convertng a IFF JAFFE mage to a normalzed pure expresson mage for feature extracton: 1). detectng facal feature ponts manually ncludng eyes, nose and mouth; ). rotatng to lne up the eye coordnates; 3) locatng and croppng the face regon usng a rectangle accordng to face model [5] as shown n Fg.1. Suppose Fg. 1. Facal model Fg.. Example of pure facal expresson mages after preprocessng from JAFFE database. 87
3 Hong-Bo Deng, Lan-Wen Jn, L-Xn Zhen, Jan-Cheng Huang A New Facal Expresson Recognton Method Based on Local Gabor Flter Bank and PCA plus LDA the dstance between two eyes s d, the rectangle wll be.d 1.8d; 4). scalng the mage to fxed sze of 18 96, locatng the center poston of the two eyes to a fxed poston; 5). usng a hstogram equalzaton method to elmnate llumnaton effect. Fg. llustrates some examples of pure facal expresson mages after preprocessng from JAFFE database. Fg. 4. he magntudes of the Gabor feature representaton of the frst face mage n Fg. III. Gabor Feature Extracton he Gabor flters, whose kernels are smlar to the D receptve feld profles of the mammalan cortcal smple cells [3][4], have been consdered as a very useful tool n computer vson and mage analyss due to ts optmal localzaton propertes n both spatal analyss and frequency doman [][14][15][16][17]. A. Gabor Flters Fg. 3. he real part of the Gabor flters wth fve frequences and eght orentatons for ω max =π/, the row corresponds to dfferent frequency ω m, the column corresponds to dfferent orentaton θ n In the spatal doman, a Gabor flter s a complex exponental modulated by a Gaussan functon [4]. he Gabor flter can be defned as follows, x' + y' ϖ σ 1 ( ) σ ϖx' ψ ( x, y, ϖ, θ ) = e [ e e πσ x' = x cosθ + y snθ, y' = xsnθ + y cosθ ] (1) where (x, y) s the pxel poston n the spatal doman, ω the radal center frequency, θ the orentaton of Gabor flter, and σ the standard devaton of the round Gaussan functon along 88
4 Internatonal Journal of Informaton echnology Vol. 11 No ϖ the x- and y-axes. In addton, the second term of the Gabor flter, σ / e, compensates for the DC value because the cosne component has nonzero mean whle the sne component has zero mean. Accordng to [4], we set σ π/ω to defne the relatonshp between σ and ω n our experments. In most cases a Gabor flter bank wth fve frequences and eght orentatons [1][][18] s used to extract the Gabor feature for face representaton. Selectng the maxmum frequency ω max =π/, ω m =ω (max) λ -(m-1), m={1,,3,4,5}, λ =, θ n =(n-1)π/8, n={1,,,8}, the real part of the Gabor flters wth fve frequences and eght orentatons s shown n Fg.3. From Fg.3 t can be seen that the Gabor flters exhbt strong characterstcs of spatal localty and orentaton selectvty. B. Gabor Feature Representaton he Gabor feature representaton of an mage I(x, y) s the convoluton of the mage wth the Gabor flter bank ψ(x, y, ω m, θ n ) as gven by: O m,n ( m n x, y) = I( x, y) ψ ( x, y, ϖ, θ ) () where * denotes the convoluton operator. he magntude of the convoluton outputs of a sample mage (the frst mage n Fg.) correspondng to the flter bank n Fg.3 s shown n Fg.4. In practce, the tme for performng Gabor feature extracton s very long and the dmenson of Gabor feature vector s prohbtvely large. For example, f the sze of normalzed Fg. 5. Examples of several global and local Gabor flter bank, the black blocks are the selected flter for LG(mxn) mage s 18 96, the dmenson of the Gabor feature vector wth 40 flters wll result n ( ). C. Local Gabor Flter Bank It can be seen that the Gabor representatons are very smlar usng the flters wth the same orentaton, especally usng the flters wth the two neghborng frequences, such as the frst column n Fg.4. It s found that the Gabor feature vector wth all the 40 flters becomes very redundant and correlatve. For the global Gabor flter bank wth all the m frequences and n orentatons, we denoted t as G(mxn). In ths paper we proposed a novel local flter bank wth part of the entre m frequences and n orentatons, and we denoted t as LG(mxn). In order to select few Gabor flters to reduce the dmenson and computaton wthout degradng the recognton performance, t should cover all the frequences and orentatons, but only select one frequency for each orentaton or ncrease the nterval between the neghborng frequences wth the same orentaton. Several global and local Gabor flter banks are shown n Fg.5. he method of selectng the LG1(mxn) s that the parameter m of frequency ncreases repeatedly 89
5 Hong-Bo Deng, Lan-Wen Jn, L-Xn Zhen, Jan-Cheng Huang A New Facal Expresson Recognton Method Based on Local Gabor Flter Bank and PCA plus LDA from mn to max, and the parameter n of orentaton adds one for each tme. he dfference of LG(mxn) s that the parameter m of frequency decreases from max to mn. For LG3(mxn), t s selected wth an nterval between any two flters. he computaton and memory requred by dfferent global and local Gabor flter bank are gven n able1. By comparng the performance of G(mxn) wth LG(mxn), LG(mxn) has the advantages of shortenng the tme for feature extracton, reducng the dmenson, decreasng the computaton and storage. he recognton performance wll be depcted n Secton 5. Our experments were conducted usng a Pentum IV.8G PC wth 51MB memory. able 1. Computaton and memory requred by dfferent Gabor flter bank Gabor Flter Bank Number of Flters Orgnal Dmenson (D) Feature Extracton me(ms) Samplng Feature dmenson PCA Matrx G(5x8) G(4x8) G(3x8) LG1(3x8) LG3(3x8) o encompass the propertes of spatal localty and orentaton selectvty, t should concatenate [][13] all the outputs of Gabor flter bank and derve the Gabor feature vector. Before the concatenaton, we frst downsample each output of Gabor flter, and then normalze t to zero mean and unt varance. One smple scheme s to sample the facal feature n a regular grd [13], whch sample over the whole face regons wth regular nterval. he last columns of able1 show the dmenson and PCA matrx of samplng feature when the samplng nterval s 8 pxels. IV. Feature Compresson One approach to copng wth the problem of excessve dmensonalty s to reduce the dmensonalty by lnear combnng features [9]. In effect, lnear methods proect the hghdmensonal data onto a lower dmensonal space, we call t feature compresson. here are two classcal approaches to fndng effectve lnear transformatons, whch are Prncpal Component Analyss (PCA) [1][6][7][8][9] and Lnear Dscrmnant Analyss (LDA) [1][6][8][9]. PCA seeks a proecton that best represents the orgnal data n a least-squares sense, and LDA seeks a proecton that best separates the data n a least-squares sense. A. PCA Let us consder a set of N sample mages {x 1, x,, x N } represented by t-dmensonal Gabor feature vector. he PCA [8][9] can be used to fnd a lnear transformaton mappng the orgnal 90
6 Internatonal Journal of Informaton echnology Vol. 11 No t-dmensonal feature space nto an f-dmensonal feature subspace, where normally f << t. he f new feature vector y R are defned by y = W x ( = 1,,,N) (3) pca where W pca s the lnear transformatons matrx, s the number of sample mages. he columns of W pca are the f egenvectors assocated wth the f largest egenvalues of the scatter matrx S, whch s defned as S = N = 1 ( x µ )( x µ ) (4) t where µ R s the mean mage of all samples. he dsadvantage of PCA s that t may lose mportant nformaton for dscrmnaton between dfferent classes. B. LDA LDA [8][9] s a supervsed learnng method, whch utlzes the category nformaton assocated wth each sample. he goal of LDA s to maxmze the between-class scatter whle mnmzng the wthn-class scatter. Mathematcally speakng, the wthn-class scatter matrx S w and between-class scatter matrx S b are defned as S S w b = = c = 1 = 1 c = 1 N ( x µ )( x ( µ µ )( µ µ ) µ ) (5) where x s the th sample of class, µ s the mean of class, µ s the mean mage of all classes, c s the number of classes, and N s the number of samples of class. One way to select W lda s to maxmze the rato det S b /det S w. If S w s nonsngular matrx then ths rato s maxmzed, when the transformaton matrx W lda conssts of g generalzed egenvectors correspondng to the g largest egenvalues of S w -1 S b [1][6][8][9]. Note that there are at most c-1 nonzero generalzed egenvalues, and so an upper bound on g s c-1. In ths paper, we consder seven knds of facal expressons, so the dmenson of LDA s up to 6. C. PCA+LDA In order to guarantee S w not to become sngular, we requre at least t+c samples. In practce t s dffcult to obtan so many samples when the dmenson of feature s very hgh. o solve ths problem, a two-phase framework PCA plus LDA s proposed by [1][6][8][18]. It can be descrbed that PCA maps the orgnal t-dmensonal feature x to the f-dmensonal feature y as an ntermedate space, and then LDA proects the PCA output to a new g-dmensonal feature vectors z. More formally, t s gven by 91
7 Hong-Bo Deng, Lan-Wen Jn, L-Xn Zhen, Jan-Cheng Huang A New Facal Expresson Recognton Method Based on Local Gabor Flter Bank and PCA plus LDA z = W W x ( = 1,,,N) (6) lda pca o compare the performance of PCA wth PCA+LDA, recognton results usng PCA feature and PCA+LDA feature respectvely wll be reported n Secton 5. V. Experments and Results he proposed method s evaluated n terms of ts recognton performance usng the JAFFE female facal expresson database [10][11], whch ncludes 13 facal expresson mages correspondng to 10 persons. Every person posed 3 or 4 examples of each of the seven facal expressons (happness, sadness, surprse, anger, dsgust, fear, neural). wo facal expresson mages of each expresson of each subect were randomly selected as tranng samples, whle the remanng samples were used as test data. We have 138 tranng mages and 75 testng mages for each tral. Snce the sze of the JAFFE database s lmted, we perform the tral over 3 tmes to get the average recognton rate. In our experments, the nearest neghbor rule s then used to classfy the facal expresson mages. Experments were performed wth the PCA feature and PCA+LDA feature respectvely. wo dstance measures Eucldean (L) and ctyblock (L1) are employed by the classfer. A. Experment performance of dfferent Gabor Flter Bank he frst experment was desgned to compare the recognton performance usng dfferent Gabor flter bank. able shows the recognton results. he classfcaton performance shown n able suggested the followng conclusons: 1). he recognton rate of PCA feature s from 76.89% to 90.67%, L1 performs better than L wth PCA feature. When usng PCA+LDA feature to for FER, the recognton rate ncreases several percent and get up to 97.33%. L s slghtly better than L1 wth PCA+LDA feature. ). In comparson wth the performance of dfferent Gabor flter bank, t s found that G(5x8) s the best whle G(4x8) and G(3x8) decrease a lttle along wth fewer Gabor flters used. However local Gabor flter banks such as LG1(3x8), LG(3x8) and LG3(3x8) have almost the same classfcaton performance as global Gabor flter bank G(3x8). LG3(3x8) even outperforms than G(3x8) when usng PCA+LDA. able. Recognton rates usng PCA feature and PCA+LDA feature separately correspondng to dfferent Gabor flter bank Gabor Flter Bank Classfcaton Methods PCA PCA+LDA L L1 L L1 9
8 Internatonal Journal of Informaton echnology Vol. 11 No G(5x8) G(4x8) G(3x8) LG1(3x8) LG(3x8) LG3(3x8) LG3(4x8) B. Recognton performance aganst llumnaton normalzaton hs experment was desgned to test the effect of llumnaton normalzaton whch utlzed a hstogram equalzaton method. Selectng LG3(3x8) to extract feature, usng L1 for PCA feature and L for PCA+LDA feature, the recognton results aganst llumnaton normalzaton are gven by able3. able 3. Recognton rates wth and wthout llumnaton normalzaton Illumnaton Classfcaton Methods Normalzaton PCA PCA+LDA No (wthout) Yes (wth) From able3 t s obvous to see that llumnaton normalzaton s effectve for PCA feature to acheve hgh performance, whose recognton rate s mproved 4% or so. As to PCA+LDA feature, llumnaton normalzaton also works better, but t s not as clear as PCA feature. We can draw the concluson that PCA feature s senstve to varous llumnaton, but PCA+LDA feature may be less senstve to dfferent llumnaton. C. Elmnatng the frst several prncpal components Prevous studes n the feld of face recognton [6][7][8] reported that dscardng the frst one to three prncpal components (PCs) mproved performance. In ths paper, we dscarded the frst one to nne PCs to analyze the recognton effect for FER. Selectng G(3x8) and LG3(3x8) to extract feature, usng L1 for PCA feature and L for PCA+LDA feature, able4 depcts the results from ths experment. able 4. Recognton results by elmnatng the frst one to nne prncpal components Gabor Flter Bank Input Feature Number of Elmnated Prncpal Component
9 Hong-Bo Deng, Lan-Wen Jn, L-Xn Zhen, Jan-Cheng Huang A New Facal Expresson Recognton Method Based on Local Gabor Flter Bank and PCA plus LDA G(3x8) PCA PCA+LDA LG3(3x8) PCA PCA+LDA able4 demonstrated that elmnatng the frst one to three PCs wll reach to the best performance rates. Best performance for PCA feature, 91.11%, was obtaned by elmnatng the frst three PCs. Best performance for PCA+LDA feature, 97.33%, was obtaned by removng the frst two PCs. If dscardng more than three PCs, n general, the results wll become worse. VI. Conclusons In ths paper, we ntroduced our FER system based on Gabor feature and PCA+LDA. We proposed a novel local Gabor flter bank for feature extracton. A mnmum dstance classfer was employed to evaluate the recognton performance n dfferent experment condtons. he experments suggest the followng conclusons: 1). Local Gabor flter bank outperforms global Gabor flter bank n the aspects of shortenng the tme for feature extracton, reducng the hgh dmensonal feature, decreasng the requred computaton and storage, even achevng better performance n some stuatons. ). PCA can sgnfcantly reduce the dmensonalty of the orgnal feature wthout loss of much nformaton n the sense of representaton, but t may lose mportant nformaton for dscrmnaton between dfferent classes. When usng PCA feature to classfy, the L1 dstance measure performs better than L. Illumnaton normalzaton s effectve for PCA feature to acheve hgh performance. 3). When usng PCA+LDA method, the dmensonalty drastcally reduced to 6 dmensons and the recognton performance s mproved several percent compared wth PCA. Experments show that PCA+LDA feature may partly elmnate the senstvty of llumnaton. 4). Dscardng the frst one to three PCs wll reach to the best performance rates. If removng more than three PCs, t wll commonly worsen the results. he best performance, usng LG3(3x8) and PCA+LDA feature by elmnatng the frst two PCs, was 97.33% n our experments. References [1] G. Donato, M. S. Bartlett, J. C. Hager, Classfyng Facal Actons, IEEE rans. Pattern Analyss and Machne Intellgence, Vol. 1, 1999, pp [] C. Lu, H. Wechsler, Independent Component Analyss of Gabor Features for Face recognton, IEEE rans. Neural Networks, Vol. 14, 003, pp [3] J. G. Daugman, Complete Dscrete -D Gabor ransforms by Neural Networks for Image Analyss and Compresson, IEEE rans. Acoustc, speech and sgnal processng, Vol. 36, 1988, pp [4]. S. Lee, Image Representaton Usng D Gabor Wavelets, IEEE rans. Pattern Analyss and Machne Intellgence, Vol. 18, 1996, pp
10 Internatonal Journal of Informaton echnology Vol. 11 No [5] F. Y. Shh, C. Chuang, Automatc extracton of head and face boundares and facal features, Informaton Scences, Vol. 158, 004, pp [6] P. N. Belhumeour, J. P. Hespanha, D. J. Kregman, Egenfaces vs. Fsherfaces: Recognton Usng Class Specfc Lnear Proecton, IEEE rans. Pattern Analyss and Machne Intellgence, Vol. 19, 1997, pp [7] M. urk, A. Pentland, Egenfaces for Recognton, Journal Cogntve Neuro-scence, Vol. 3, 1991, pp [8] A. M. Martnez, A. C. Kak, PCA versus LDA, IEEE rans. Pattern Analyss and Machne Intellgence, Vol. 3, 001, pp.8-33 [9] R. O. Duda, P. E. Hart, D. G. Stork, Pattern Classfcaton. Wley, New York (001) [10] M. J. Lyons, S. Akamatsu, M. Kamach, J. Gyoba, Codng Facal Expressons wth Gabor Wavelets, In: Proceedngs of the 3th IEEE Internatonal Conference on Automatc Face and Gesture Recognton, Nara, Japan, 1998, pp [11] Z. Zhang, M. Lyons, M. Schuster, S. Akamatsu, Comparson Between Geometry-Based and Gabor-Wavelets-Based Facal Expresson Recognton Usng Mult-Layer Perceptron, In: Proceedngs of the hrd IEEE Internatonal Conference on Automatc Face and Gesture Recognton, Nara, Japan, 1998, pp [1] I. A. Essa, A. P. Pentland, Codng, Analyss, Interpretaton, and Recognton of Facal Expressons, IEEE rans. Pattern Analyss and Machne Intellgence, Vol. 19, 1997, pp [13] D. H. Lu, K. M. Lam, L. S. Shen, Optmal samplng of Gabor features for face recognton, Pattern Recognton Letters, Vol. 5, 004, pp [14] C. Lu, H. Wechsler, Gabor Feature Based Classfcaton Usng the Enhanced Fsher Lnear Dscrmnant Model for Face Recognton, IEEE rans. Image Processng, Vol. 11, 00, pp [15] I. R. Fasel, M. S. Bartlett, J. R. Movellan, A Comparson of Gabor Flter Methods for Automatc Detecton of Facal Landmarks, In: Proceedngs of Ffth IEEE Internatonal Conf. Automatc Face and Gesture Recognton, Washngton, USA, 00, pp [16] L. L. Huang, A. Shmzu, H. Kobatake, Classfcaton-Based Face Detecton Usng Gabor Flter Features, In: Proceedngs of Ffth IEEE Internatonal Conference on Automatc Face and Gesture Recognton, Seoul, Korea, 004, pp [17] V. Kyrk, J. K. Kamaranen, H. Kalvanen, Smple Gabor feature space for nvarant obect recognton, Pattern Recognton Letters, Vol. 5, 004, pp [18] B. Fasel, J. Luettn, Automatc facal expresson analyss: a survey, Pattern Recognton, Vol. 36, 003, pp [19]. Cootes, G. Edwards, C. aylor, Actve appearance models, IEEE rans. Pattern Analyss and Machne Intellgence, Vol. 3, 001, pp [0] M. Pantc, J. M. Rothkrantz, Automatc Analyss of Facal Expressons: he State of the Art, IEEE rans. Pattern Analyss and Machne Intellgence, Vol., 000, pp Hong-Bo Deng receved hs B.S. degree n Electroncal Engneerng from South Chna Unversty of echnology, Chna, n 003. He s currently a M.S. student n the School of Electronc and Informaton Engneerng, South Chna Unversty of echnology, Chna. Hs current nterests nclude Face Expresson Recognton, Image Processng, Computer Vson, Human-Computer Interface. 95
11 Hong-Bo Deng, Lan-Wen Jn, L-Xn Zhen, Jan-Cheng Huang A New Facal Expresson Recognton Method Based on Local Gabor Flter Bank and PCA plus LDA Lan-Wen Jn, an IEEE member, receved hs Ph.D degrees from South Chna Unversty of echnology, Chna, n He s currently a professor at School of Electronc and Informaton Engneerng, South Chna Unversty of echnology, Chna. He has research nterests n varous aspects of Character Recognton, Human-Computer Interface, Image Processng, Neural Network, and Computer Vson. Dr. L-Xn Zhen s currently a senor research manager of Motorola Labs Chna Research Center. Dr. Jan-Cheng Huang s currently the drector of Motorola Labs Chna Research Center. 96
Regularized Discriminant Analysis for Face Recognition
1 Regularzed Dscrmnant Analyss for Face Recognton Itz Pma, Mayer Aladem Department of Electrcal and Computer Engneerng, Ben-Guron Unversty of the Negev P.O.Box 653, Beer-Sheva, 845, Israel. Abstract Ths
More informationA Novel Biometric Feature Extraction Algorithm using Two Dimensional Fisherface in 2DPCA subspace for Face Recognition
A Novel ometrc Feature Extracton Algorthm usng wo Dmensonal Fsherface n 2DPA subspace for Face Recognton R. M. MUELO, W.L. WOO, and S.S. DLAY School of Electrcal, Electronc and omputer Engneerng Unversty
More informationUnified Subspace Analysis for Face Recognition
Unfed Subspace Analyss for Face Recognton Xaogang Wang and Xaoou Tang Department of Informaton Engneerng The Chnese Unversty of Hong Kong Shatn, Hong Kong {xgwang, xtang}@e.cuhk.edu.hk Abstract PCA, LDA
More informationSubspace Learning Based on Tensor Analysis. by Deng Cai, Xiaofei He, and Jiawei Han
Report No. UIUCDCS-R-2005-2572 UILU-ENG-2005-1767 Subspace Learnng Based on Tensor Analyss by Deng Ca, Xaofe He, and Jawe Han May 2005 Subspace Learnng Based on Tensor Analyss Deng Ca Xaofe He Jawe Han
More informationTensor Subspace Analysis
Tensor Subspace Analyss Xaofe He 1 Deng Ca Partha Nyog 1 1 Department of Computer Scence, Unversty of Chcago {xaofe, nyog}@cs.uchcago.edu Department of Computer Scence, Unversty of Illnos at Urbana-Champagn
More informationMULTISPECTRAL IMAGE CLASSIFICATION USING BACK-PROPAGATION NEURAL NETWORK IN PCA DOMAIN
MULTISPECTRAL IMAGE CLASSIFICATION USING BACK-PROPAGATION NEURAL NETWORK IN PCA DOMAIN S. Chtwong, S. Wtthayapradt, S. Intajag, and F. Cheevasuvt Faculty of Engneerng, Kng Mongkut s Insttute of Technology
More informationSemi-supervised Classification with Active Query Selection
Sem-supervsed Classfcaton wth Actve Query Selecton Jao Wang and Swe Luo School of Computer and Informaton Technology, Beng Jaotong Unversty, Beng 00044, Chna Wangjao088@63.com Abstract. Labeled samples
More informationLINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity
LINEAR REGRESSION ANALYSIS MODULE IX Lecture - 30 Multcollnearty Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur 2 Remedes for multcollnearty Varous technques have
More informationStatistical pattern recognition
Statstcal pattern recognton Bayes theorem Problem: decdng f a patent has a partcular condton based on a partcular test However, the test s mperfect Someone wth the condton may go undetected (false negatve
More informationKernel Methods and SVMs Extension
Kernel Methods and SVMs Extenson The purpose of ths document s to revew materal covered n Machne Learnng 1 Supervsed Learnng regardng support vector machnes (SVMs). Ths document also provdes a general
More informationPop-Click Noise Detection Using Inter-Frame Correlation for Improved Portable Auditory Sensing
Advanced Scence and Technology Letters, pp.164-168 http://dx.do.org/10.14257/astl.2013 Pop-Clc Nose Detecton Usng Inter-Frame Correlaton for Improved Portable Audtory Sensng Dong Yun Lee, Kwang Myung Jeon,
More informationLecture 10: Dimensionality reduction
Lecture : Dmensonalt reducton g The curse of dmensonalt g Feature etracton s. feature selecton g Prncpal Components Analss g Lnear Dscrmnant Analss Intellgent Sensor Sstems Rcardo Guterrez-Osuna Wrght
More informationLecture 12: Classification
Lecture : Classfcaton g Dscrmnant functons g The optmal Bayes classfer g Quadratc classfers g Eucldean and Mahalanobs metrcs g K Nearest Neghbor Classfers Intellgent Sensor Systems Rcardo Guterrez-Osuna
More informationRotation Invariant Shape Contexts based on Feature-space Fourier Transformation
Fourth Internatonal Conference on Image and Graphcs Rotaton Invarant Shape Contexts based on Feature-space Fourer Transformaton Su Yang 1, Yuanyuan Wang Dept of Computer Scence and Engneerng, Fudan Unversty,
More informationAn Improved multiple fractal algorithm
Advanced Scence and Technology Letters Vol.31 (MulGraB 213), pp.184-188 http://dx.do.org/1.1427/astl.213.31.41 An Improved multple fractal algorthm Yun Ln, Xaochu Xu, Jnfeng Pang College of Informaton
More informationModule 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur
Module 3 LOSSY IMAGE COMPRESSION SYSTEMS Verson ECE IIT, Kharagpur Lesson 6 Theory of Quantzaton Verson ECE IIT, Kharagpur Instructonal Objectves At the end of ths lesson, the students should be able to:
More informationP R. Lecture 4. Theory and Applications of Pattern Recognition. Dept. of Electrical and Computer Engineering /
Theory and Applcatons of Pattern Recognton 003, Rob Polkar, Rowan Unversty, Glassboro, NJ Lecture 4 Bayes Classfcaton Rule Dept. of Electrcal and Computer Engneerng 0909.40.0 / 0909.504.04 Theory & Applcatons
More informationVQ widely used in coding speech, image, and video
at Scalar quantzers are specal cases of vector quantzers (VQ): they are constraned to look at one sample at a tme (memoryless) VQ does not have such constrant better RD perfomance expected Source codng
More informationUsing Random Subspace to Combine Multiple Features for Face Recognition
Usng Random Subspace to Combne Multple Features for Face Recognton Xaogang Wang and Xaoou ang Department of Informaton Engneerng he Chnese Unversty of Hong Kong Shatn, Hong Kong {xgwang1, xtang}@e.cuhk.edu.hk
More informationCOMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS
Avalable onlne at http://sck.org J. Math. Comput. Sc. 3 (3), No., 6-3 ISSN: 97-537 COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS
More informationFeature Extraction by Maximizing the Average Neighborhood Margin
Feature Extracton by Maxmzng the Average Neghborhood Margn Fe Wang, Changshu Zhang State Key Laboratory of Intellgent Technologes and Systems Department of Automaton, Tsnghua Unversty, Bejng, Chna. 184.
More informationCS 3710: Visual Recognition Classification and Detection. Adriana Kovashka Department of Computer Science January 13, 2015
CS 3710: Vsual Recognton Classfcaton and Detecton Adrana Kovashka Department of Computer Scence January 13, 2015 Plan for Today Vsual recognton bascs part 2: Classfcaton and detecton Adrana s research
More informationOne-sided finite-difference approximations suitable for use with Richardson extrapolation
Journal of Computatonal Physcs 219 (2006) 13 20 Short note One-sded fnte-dfference approxmatons sutable for use wth Rchardson extrapolaton Kumar Rahul, S.N. Bhattacharyya * Department of Mechancal Engneerng,
More informationMicrowave Diversity Imaging Compression Using Bioinspired
Mcrowave Dversty Imagng Compresson Usng Bonspred Neural Networks Youwe Yuan 1, Yong L 1, Wele Xu 1, Janghong Yu * 1 School of Computer Scence and Technology, Hangzhou Danz Unversty, Hangzhou, Zhejang,
More informationLinear Classification, SVMs and Nearest Neighbors
1 CSE 473 Lecture 25 (Chapter 18) Lnear Classfcaton, SVMs and Nearest Neghbors CSE AI faculty + Chrs Bshop, Dan Klen, Stuart Russell, Andrew Moore Motvaton: Face Detecton How do we buld a classfer to dstngush
More informationCOMPUTATIONALLY EFFICIENT WAVELET AFFINE INVARIANT FUNCTIONS FOR SHAPE RECOGNITION. Erdem Bala, Dept. of Electrical and Computer Engineering,
COMPUTATIONALLY EFFICIENT WAVELET AFFINE INVARIANT FUNCTIONS FOR SHAPE RECOGNITION Erdem Bala, Dept. of Electrcal and Computer Engneerng, Unversty of Delaware, 40 Evans Hall, Newar, DE, 976 A. Ens Cetn,
More informationReport on Image warping
Report on Image warpng Xuan Ne, Dec. 20, 2004 Ths document summarzed the algorthms of our mage warpng soluton for further study, and there s a detaled descrpton about the mplementaton of these algorthms.
More informationSupport Vector Machines. Vibhav Gogate The University of Texas at dallas
Support Vector Machnes Vbhav Gogate he Unversty of exas at dallas What We have Learned So Far? 1. Decson rees. Naïve Bayes 3. Lnear Regresson 4. Logstc Regresson 5. Perceptron 6. Neural networks 7. K-Nearest
More informationA Fast Fractal Image Compression Algorithm Using Predefined Values for Contrast Scaling
Proceedngs of the World Congress on Engneerng and Computer Scence 007 WCECS 007, October 4-6, 007, San Francsco, USA A Fast Fractal Image Compresson Algorthm Usng Predefned Values for Contrast Scalng H.
More informationMultigradient for Neural Networks for Equalizers 1
Multgradent for Neural Netorks for Equalzers 1 Chulhee ee, Jnook Go and Heeyoung Km Department of Electrcal and Electronc Engneerng Yonse Unversty 134 Shnchon-Dong, Seodaemun-Ku, Seoul 1-749, Korea ABSTRACT
More informationA New Evolutionary Computation Based Approach for Learning Bayesian Network
Avalable onlne at www.scencedrect.com Proceda Engneerng 15 (2011) 4026 4030 Advanced n Control Engneerng and Informaton Scence A New Evolutonary Computaton Based Approach for Learnng Bayesan Network Yungang
More informationHiding data in images by simple LSB substitution
Pattern Recognton 37 (004) 469 474 www.elsever.com/locate/patcog Hdng data n mages by smple LSB substtuton Ch-Kwong Chan, L.M. Cheng Department of Computer Engneerng and Informaton Technology, Cty Unversty
More information2E Pattern Recognition Solutions to Introduction to Pattern Recognition, Chapter 2: Bayesian pattern classification
E395 - Pattern Recognton Solutons to Introducton to Pattern Recognton, Chapter : Bayesan pattern classfcaton Preface Ths document s a soluton manual for selected exercses from Introducton to Pattern Recognton
More informationA Bayes Algorithm for the Multitask Pattern Recognition Problem Direct Approach
A Bayes Algorthm for the Multtask Pattern Recognton Problem Drect Approach Edward Puchala Wroclaw Unversty of Technology, Char of Systems and Computer etworks, Wybrzeze Wyspanskego 7, 50-370 Wroclaw, Poland
More informationOrientation Model of Elite Education and Mass Education
Proceedngs of the 8th Internatonal Conference on Innovaton & Management 723 Orentaton Model of Elte Educaton and Mass Educaton Ye Peng Huanggang Normal Unversty, Huanggang, P.R.Chna, 438 (E-mal: yepeng@hgnc.edu.cn)
More informationNatural Images, Gaussian Mixtures and Dead Leaves Supplementary Material
Natural Images, Gaussan Mxtures and Dead Leaves Supplementary Materal Danel Zoran Interdscplnary Center for Neural Computaton Hebrew Unversty of Jerusalem Israel http://www.cs.huj.ac.l/ danez Yar Wess
More informationAutomatic Object Trajectory- Based Motion Recognition Using Gaussian Mixture Models
Automatc Object Trajectory- Based Moton Recognton Usng Gaussan Mxture Models Fasal I. Bashr, Ashfaq A. Khokhar, Dan Schonfeld Electrcal and Computer Engneerng, Unversty of Illnos at Chcago. Chcago, IL,
More informationMarkov Chain Monte Carlo Lecture 6
where (x 1,..., x N ) X N, N s called the populaton sze, f(x) f (x) for at least one {1, 2,..., N}, and those dfferent from f(x) are called the tral dstrbutons n terms of mportance samplng. Dfferent ways
More informationA Network Intrusion Detection Method Based on Improved K-means Algorithm
Advanced Scence and Technology Letters, pp.429-433 http://dx.do.org/10.14257/astl.2014.53.89 A Network Intruson Detecton Method Based on Improved K-means Algorthm Meng Gao 1,1, Nhong Wang 1, 1 Informaton
More informationEfficient and Robust Feature Extraction by Maximum Margin Criterion
Effcent and Robust Feature Extracton by Maxmum Margn Crteron Hafeng L Tao Jang Department of Computer Scence Unversty of Calforna Rversde, CA 95 {hl,jang}@cs.ucr.edu Keshu Zhang Department of Electrcal
More informationCONTRAST ENHANCEMENT FOR MIMIMUM MEAN BRIGHTNESS ERROR FROM HISTOGRAM PARTITIONING INTRODUCTION
CONTRAST ENHANCEMENT FOR MIMIMUM MEAN BRIGHTNESS ERROR FROM HISTOGRAM PARTITIONING N. Phanthuna 1,2, F. Cheevasuvt 2 and S. Chtwong 2 1 Department of Electrcal Engneerng, Faculty of Engneerng Rajamangala
More informationThe lower and upper bounds on Perron root of nonnegative irreducible matrices
Journal of Computatonal Appled Mathematcs 217 (2008) 259 267 wwwelsevercom/locate/cam The lower upper bounds on Perron root of nonnegatve rreducble matrces Guang-Xn Huang a,, Feng Yn b,keguo a a College
More informationThe Order Relation and Trace Inequalities for. Hermitian Operators
Internatonal Mathematcal Forum, Vol 3, 08, no, 507-57 HIKARI Ltd, wwwm-hkarcom https://doorg/0988/mf088055 The Order Relaton and Trace Inequaltes for Hermtan Operators Y Huang School of Informaton Scence
More informationThe Study of Teaching-learning-based Optimization Algorithm
Advanced Scence and Technology Letters Vol. (AST 06), pp.05- http://dx.do.org/0.57/astl.06. The Study of Teachng-learnng-based Optmzaton Algorthm u Sun, Yan fu, Lele Kong, Haolang Q,, Helongang Insttute
More informationSimulated Power of the Discrete Cramér-von Mises Goodness-of-Fit Tests
Smulated of the Cramér-von Mses Goodness-of-Ft Tests Steele, M., Chaselng, J. and 3 Hurst, C. School of Mathematcal and Physcal Scences, James Cook Unversty, Australan School of Envronmental Studes, Grffth
More informationarxiv:cs.cv/ Jun 2000
Correlaton over Decomposed Sgnals: A Non-Lnear Approach to Fast and Effectve Sequences Comparson Lucano da Fontoura Costa arxv:cs.cv/0006040 28 Jun 2000 Cybernetc Vson Research Group IFSC Unversty of São
More informationComposite Hypotheses testing
Composte ypotheses testng In many hypothess testng problems there are many possble dstrbutons that can occur under each of the hypotheses. The output of the source s a set of parameters (ponts n a parameter
More informationThe Synchronous 8th-Order Differential Attack on 12 Rounds of the Block Cipher HyRAL
The Synchronous 8th-Order Dfferental Attack on 12 Rounds of the Block Cpher HyRAL Yasutaka Igarash, Sej Fukushma, and Tomohro Hachno Kagoshma Unversty, Kagoshma, Japan Emal: {garash, fukushma, hachno}@eee.kagoshma-u.ac.jp
More informationNon-linear Canonical Correlation Analysis Using a RBF Network
ESANN' proceedngs - European Smposum on Artfcal Neural Networks Bruges (Belgum), 4-6 Aprl, d-sde publ., ISBN -97--, pp. 57-5 Non-lnear Canoncal Correlaton Analss Usng a RBF Network Sukhbnder Kumar, Elane
More informationChapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems
Numercal Analyss by Dr. Anta Pal Assstant Professor Department of Mathematcs Natonal Insttute of Technology Durgapur Durgapur-713209 emal: anta.bue@gmal.com 1 . Chapter 5 Soluton of System of Lnear Equatons
More information4 Analysis of Variance (ANOVA) 5 ANOVA. 5.1 Introduction. 5.2 Fixed Effects ANOVA
4 Analyss of Varance (ANOVA) 5 ANOVA 51 Introducton ANOVA ANOVA s a way to estmate and test the means of multple populatons We wll start wth one-way ANOVA If the populatons ncluded n the study are selected
More informationLecture Notes on Linear Regression
Lecture Notes on Lnear Regresson Feng L fl@sdueducn Shandong Unversty, Chna Lnear Regresson Problem In regresson problem, we am at predct a contnuous target value gven an nput feature vector We assume
More informationBACKGROUND SUBTRACTION WITH EIGEN BACKGROUND METHODS USING MATLAB
BACKGROUND SUBTRACTION WITH EIGEN BACKGROUND METHODS USING MATLAB 1 Ilmyat Sar 2 Nola Marna 1 Pusat Stud Komputas Matematka, Unverstas Gunadarma e-mal: lmyat@staff.gunadarma.ac.d 2 Pusat Stud Komputas
More informationResearch Article Green s Theorem for Sign Data
Internatonal Scholarly Research Network ISRN Appled Mathematcs Volume 2012, Artcle ID 539359, 10 pages do:10.5402/2012/539359 Research Artcle Green s Theorem for Sgn Data Lous M. Houston The Unversty of
More informationImprovement of Histogram Equalization for Minimum Mean Brightness Error
Proceedngs of the 7 WSEAS Int. Conference on Crcuts, Systems, Sgnal and elecommuncatons, Gold Coast, Australa, January 7-9, 7 3 Improvement of Hstogram Equalzaton for Mnmum Mean Brghtness Error AAPOG PHAHUA*,
More informationNUMERICAL DIFFERENTIATION
NUMERICAL DIFFERENTIATION 1 Introducton Dfferentaton s a method to compute the rate at whch a dependent output y changes wth respect to the change n the ndependent nput x. Ths rate of change s called the
More informationCSE 252C: Computer Vision III
CSE 252C: Computer Vson III Lecturer: Serge Belonge Scrbe: Catherne Wah LECTURE 15 Kernel Machnes 15.1. Kernels We wll study two methods based on a specal knd of functon k(x, y) called a kernel: Kernel
More informationGEMINI GEneric Multimedia INdexIng
GEMINI GEnerc Multmeda INdexIng Last lecture, LSH http://www.mt.edu/~andon/lsh/ Is there another possble soluton? Do we need to perform ANN? 1 GEnerc Multmeda INdexIng dstance measure Sub-pattern Match
More informationPulse Coded Modulation
Pulse Coded Modulaton PCM (Pulse Coded Modulaton) s a voce codng technque defned by the ITU-T G.711 standard and t s used n dgtal telephony to encode the voce sgnal. The frst step n the analog to dgtal
More informationCHAPTER-5 INFORMATION MEASURE OF FUZZY MATRIX AND FUZZY BINARY RELATION
CAPTER- INFORMATION MEASURE OF FUZZY MATRI AN FUZZY BINARY RELATION Introducton The basc concept of the fuzz matr theor s ver smple and can be appled to socal and natural stuatons A branch of fuzz matr
More informationA Robust Method for Calculating the Correlation Coefficient
A Robust Method for Calculatng the Correlaton Coeffcent E.B. Nven and C. V. Deutsch Relatonshps between prmary and secondary data are frequently quantfed usng the correlaton coeffcent; however, the tradtonal
More informationCS 468 Lecture 16: Isometry Invariance and Spectral Techniques
CS 468 Lecture 16: Isometry Invarance and Spectral Technques Justn Solomon Scrbe: Evan Gawlk Introducton. In geometry processng, t s often desrable to characterze the shape of an object n a manner that
More informationCSci 6974 and ECSE 6966 Math. Tech. for Vision, Graphics and Robotics Lecture 21, April 17, 2006 Estimating A Plane Homography
CSc 6974 and ECSE 6966 Math. Tech. for Vson, Graphcs and Robotcs Lecture 21, Aprl 17, 2006 Estmatng A Plane Homography Overvew We contnue wth a dscusson of the major ssues, usng estmaton of plane projectve
More informationScroll Generation with Inductorless Chua s Circuit and Wien Bridge Oscillator
Latest Trends on Crcuts, Systems and Sgnals Scroll Generaton wth Inductorless Chua s Crcut and Wen Brdge Oscllator Watcharn Jantanate, Peter A. Chayasena, and Sarawut Sutorn * Abstract An nductorless Chua
More informationOdd/Even Scroll Generation with Inductorless Chua s and Wien Bridge Oscillator Circuits
Watcharn Jantanate, Peter A. Chayasena, Sarawut Sutorn Odd/Even Scroll Generaton wth Inductorless Chua s and Wen Brdge Oscllator Crcuts Watcharn Jantanate, Peter A. Chayasena, and Sarawut Sutorn * School
More informationPattern Classification
Pattern Classfcaton All materals n these sldes ere taken from Pattern Classfcaton (nd ed) by R. O. Duda, P. E. Hart and D. G. Stork, John Wley & Sons, 000 th the permsson of the authors and the publsher
More informationBOOTSTRAP METHOD FOR TESTING OF EQUALITY OF SEVERAL MEANS. M. Krishna Reddy, B. Naveen Kumar and Y. Ramu
BOOTSTRAP METHOD FOR TESTING OF EQUALITY OF SEVERAL MEANS M. Krshna Reddy, B. Naveen Kumar and Y. Ramu Department of Statstcs, Osmana Unversty, Hyderabad -500 007, Inda. nanbyrozu@gmal.com, ramu0@gmal.com
More informationLecture 12: Discrete Laplacian
Lecture 12: Dscrete Laplacan Scrbe: Tanye Lu Our goal s to come up wth a dscrete verson of Laplacan operator for trangulated surfaces, so that we can use t n practce to solve related problems We are mostly
More informationImage classification. Given the bag-of-features representations of images from different classes, how do we learn a model for distinguishing i them?
Image classfcaton Gven te bag-of-features representatons of mages from dfferent classes ow do we learn a model for dstngusng tem? Classfers Learn a decson rule assgnng bag-offeatures representatons of
More informationStructure and Drive Paul A. Jensen Copyright July 20, 2003
Structure and Drve Paul A. Jensen Copyrght July 20, 2003 A system s made up of several operatons wth flow passng between them. The structure of the system descrbes the flow paths from nputs to outputs.
More informationDe-noising Method Based on Kernel Adaptive Filtering for Telemetry Vibration Signal of the Vehicle Test Kejun ZENG
6th Internatonal Conference on Mechatroncs, Materals, Botechnology and Envronment (ICMMBE 6) De-nosng Method Based on Kernel Adaptve Flterng for elemetry Vbraton Sgnal of the Vehcle est Kejun ZEG PLA 955
More informationLinear Feature Engineering 11
Lnear Feature Engneerng 11 2 Least-Squares 2.1 Smple least-squares Consder the followng dataset. We have a bunch of nputs x and correspondng outputs y. The partcular values n ths dataset are x y 0.23 0.19
More informationA NEW DISCRETE WAVELET TRANSFORM
A NEW DISCRETE WAVELET TRANSFORM ALEXANDRU ISAR, DORINA ISAR Keywords: Dscrete wavelet, Best energy concentraton, Low SNR sgnals The Dscrete Wavelet Transform (DWT) has two parameters: the mother of wavelets
More informationAdaptive Manifold Learning
Adaptve Manfold Learnng Jng Wang, Zhenyue Zhang Department of Mathematcs Zhejang Unversty, Yuquan Campus, Hangzhou, 327, P. R. Chna wroarng@sohu.com zyzhang@zju.edu.cn Hongyuan Zha Department of Computer
More informationECE559VV Project Report
ECE559VV Project Report (Supplementary Notes Loc Xuan Bu I. MAX SUM-RATE SCHEDULING: THE UPLINK CASE We have seen (n the presentaton that, for downlnk (broadcast channels, the strategy maxmzng the sum-rate
More informationA New Scrambling Evaluation Scheme based on Spatial Distribution Entropy and Centroid Difference of Bit-plane
A New Scramblng Evaluaton Scheme based on Spatal Dstrbuton Entropy and Centrod Dfference of Bt-plane Lang Zhao *, Avshek Adhkar Kouch Sakura * * Graduate School of Informaton Scence and Electrcal Engneerng,
More informationNumerical Heat and Mass Transfer
Master degree n Mechancal Engneerng Numercal Heat and Mass Transfer 06-Fnte-Dfference Method (One-dmensonal, steady state heat conducton) Fausto Arpno f.arpno@uncas.t Introducton Why we use models and
More informationDiscretization of Continuous Attributes in Rough Set Theory and Its Application*
Dscretzaton of Contnuous Attrbutes n Rough Set Theory and Its Applcaton* Gexang Zhang 1,2, Lazhao Hu 1, and Wedong Jn 2 1 Natonal EW Laboratory, Chengdu 610036 Schuan, Chna dylan7237@sna.com 2 School of
More informationChapter 3 Describing Data Using Numerical Measures
Chapter 3 Student Lecture Notes 3-1 Chapter 3 Descrbng Data Usng Numercal Measures Fall 2006 Fundamentals of Busness Statstcs 1 Chapter Goals To establsh the usefulness of summary measures of data. The
More informationDiscriminative Dictionary Learning with Low-Rank Regularization for Face Recognition
Dscrmnatve Dctonary Learnng wth Low-Rank Regularzaton for Face Recognton Langyue L, Sheng L, and Yun Fu Department of Electrcal and Computer Engneerng Northeastern Unversty Boston, MA 02115, USA {l.langy,
More informationSaliency and Active Contour based Traffic Sign Detection
ISSN 1746-7659, England, UK Journal of Informaton and Computng Scence Vol. 7, No. 3, 01, pp. 35-40 Salency and ctve Contour based Traffc Sgn Detecton Shangbng Gao 1 and Yunyang Yan 1 1 The School Computer
More informationMulti-Task Learning in Heterogeneous Feature Spaces
Proceedngs of the Twenty-Ffth AAAI Conference on Artfcal Intellgence Mult-Task Learnng n Heterogeneous Feature Spaces Yu Zhang & Dt-Yan Yeung Department of Computer Scence and Engneerng Hong Kong Unversty
More informationUncertainty in measurements of power and energy on power networks
Uncertanty n measurements of power and energy on power networks E. Manov, N. Kolev Department of Measurement and Instrumentaton, Techncal Unversty Sofa, bul. Klment Ohrdsk No8, bl., 000 Sofa, Bulgara Tel./fax:
More informationEEE 241: Linear Systems
EEE : Lnear Systems Summary #: Backpropagaton BACKPROPAGATION The perceptron rule as well as the Wdrow Hoff learnng were desgned to tran sngle layer networks. They suffer from the same dsadvantage: they
More informationNovel Pre-Compression Rate-Distortion Optimization Algorithm for JPEG 2000
Novel Pre-Compresson Rate-Dstorton Optmzaton Algorthm for JPEG 2000 Yu-We Chang, Hung-Ch Fang, Chung-Jr Lan, and Lang-Gee Chen DSP/IC Desgn Laboratory, Graduate Insttute of Electroncs Engneerng Natonal
More informationAppendix B: Resampling Algorithms
407 Appendx B: Resamplng Algorthms A common problem of all partcle flters s the degeneracy of weghts, whch conssts of the unbounded ncrease of the varance of the mportance weghts ω [ ] of the partcles
More informationPower law and dimension of the maximum value for belief distribution with the max Deng entropy
Power law and dmenson of the maxmum value for belef dstrbuton wth the max Deng entropy Bngy Kang a, a College of Informaton Engneerng, Northwest A&F Unversty, Yanglng, Shaanx, 712100, Chna. Abstract Deng
More informationModule 9. Lecture 6. Duality in Assignment Problems
Module 9 1 Lecture 6 Dualty n Assgnment Problems In ths lecture we attempt to answer few other mportant questons posed n earler lecture for (AP) and see how some of them can be explaned through the concept
More informationMore metrics on cartesian products
More metrcs on cartesan products If (X, d ) are metrc spaces for 1 n, then n Secton II4 of the lecture notes we defned three metrcs on X whose underlyng topologes are the product topology The purpose of
More informationProbability Density Function Estimation by different Methods
EEE 739Q SPRIG 00 COURSE ASSIGMET REPORT Probablty Densty Functon Estmaton by dfferent Methods Vas Chandraant Rayar Abstract The am of the assgnment was to estmate the probablty densty functon (PDF of
More informationPattern Recognition 42 (2009) Contents lists available at ScienceDirect. Pattern Recognition. journal homepage:
Pattern Recognton 4 (9) 764 -- 779 Contents lsts avalable at ScenceDrect Pattern Recognton ournal homepage: www.elsever.com/locate/pr Perturbaton LDA: Learnng the dfference between the class emprcal mean
More informationA New Metric for Quality Assessment of Digital Images Based on Weighted-Mean Square Error 1
A New Metrc for Qualty Assessment of Dgtal Images Based on Weghted-Mean Square Error Proceedngs of SPIE, vol. 4875, 2002 Kawen Zhang, Shuozhong Wang, and Xnpen Zhang School of Communcaton and Informaton
More informationA neural network with localized receptive fields for visual pattern classification
Unversty of Wollongong Research Onlne Faculty of Informatcs - Papers (Archve) Faculty of Engneerng and Informaton Scences 2005 A neural network wth localzed receptve felds for vsual pattern classfcaton
More informationCHALMERS, GÖTEBORGS UNIVERSITET. SOLUTIONS to RE-EXAM for ARTIFICIAL NEURAL NETWORKS. COURSE CODES: FFR 135, FIM 720 GU, PhD
CHALMERS, GÖTEBORGS UNIVERSITET SOLUTIONS to RE-EXAM for ARTIFICIAL NEURAL NETWORKS COURSE CODES: FFR 35, FIM 72 GU, PhD Tme: Place: Teachers: Allowed materal: Not allowed: January 2, 28, at 8 3 2 3 SB
More informationDepartment of Electrical & Electronic Engineeing Imperial College London. E4.20 Digital IC Design. Median Filter Project Specification
Desgn Project Specfcaton Medan Flter Department of Electrcal & Electronc Engneeng Imperal College London E4.20 Dgtal IC Desgn Medan Flter Project Specfcaton A medan flter s used to remove nose from a sampled
More informationSupporting Information
Supportng Informaton The neural network f n Eq. 1 s gven by: f x l = ReLU W atom x l + b atom, 2 where ReLU s the element-wse rectfed lnear unt, 21.e., ReLUx = max0, x, W atom R d d s the weght matrx to
More informationPsychology 282 Lecture #24 Outline Regression Diagnostics: Outliers
Psychology 282 Lecture #24 Outlne Regresson Dagnostcs: Outlers In an earler lecture we studed the statstcal assumptons underlyng the regresson model, ncludng the followng ponts: Formal statement of assumptons.
More informationWhich Separator? Spring 1
Whch Separator? 6.034 - Sprng 1 Whch Separator? Mamze the margn to closest ponts 6.034 - Sprng Whch Separator? Mamze the margn to closest ponts 6.034 - Sprng 3 Margn of a pont " # y (w $ + b) proportonal
More informationGlobal Sensitivity. Tuesday 20 th February, 2018
Global Senstvty Tuesday 2 th February, 28 ) Local Senstvty Most senstvty analyses [] are based on local estmates of senstvty, typcally by expandng the response n a Taylor seres about some specfc values
More informationSection 8.3 Polar Form of Complex Numbers
80 Chapter 8 Secton 8 Polar Form of Complex Numbers From prevous classes, you may have encountered magnary numbers the square roots of negatve numbers and, more generally, complex numbers whch are the
More information