Unsupervised approach o color video hresholding Yingzi Du, MEMBER SPIE Elecrical Engineering Deparmen U.S. Naval Academy Annapolis, Maryland 21402 Chein-I Chang, FELLOW SPIE Universiy of Maryland Balimore Couny Remoe Sensing Signal and Image Processing Laboraory Deparmen of Compuer Science and Elecrical Engineering 1000 Hillop Circle Balimore, Maryland 21250 Paul David Thouin Deparmen of Defense For Meade, Maryland 20755 Absrac. Thresholding of video images is a grea challenge because of heir low spaial resoluion and complex background. We invesigae he issue of hresholding hese images by reducing he number of colors o improve auomaed ex deecion and recogniion. We develop an unsupervised approach o video images, which can be considered as an RGB color hresholding mehod. I applies a gray-level hresholding mehod o a video image in he (R, G, B) color space o produce a single hreshold value for each domain. The hree (R, G, B)-generaed values will be subsequenly processed by an effecive unsupervised clusering algorihm ha is based on a beween-class/wihin-class crierion suggesed by Osu s mehod. Since hresholding mehods designed for documen images may no work effecively for video images in many applicaions, our proposed RGB color hresholding mehod has shown o be paricularly effecive in improvemen on ex deecion and recogniion, because i can reduce he background complexiy while reaining he imporan ex characer pixels. Experimens also show ha hresholding video images is far more difficul han hresholding documen images, and he RGB color hresholding presened performs significanly beer han simple hisogram-based mehods, which generally do no produce saisfacory resuls. 2004 Sociey of Phoo-Opical Insrumenaion Engineers. [DOI: 10.1117/1.1637364] Subjec erms: color hresholding; enropy hresholding; Osu s mehod; RGB color hresholding; relaive enropy hresholding. Paper 020565 received Dec. 31, 2002; revised manuscrip received Jun. 20, 2003; acceped for publicaion Sep. 8, 2003. 1 Inroducion Informaion rerieval from video images has become an increasingly imporan research area in recen years. The rapid growh of digiized video collecions is due o he widespread use of digial cameras and video recorders combined wih inexpensive disk sorage echnology. Texual informaion conained in video images can provide one of he mos useful keys for successful informaion indexing and rerieval. Keyword searches for ex of ineres wihin video images can provide addiional capabiliies o he search engines. In video images, ex characers generally have much lower resoluion and dimmer inensiy han documen characers. In addiion, videoex characers may also have various colors, sizes, and orienaions wihin he same images. Furhermore, he video background is generally much more complex han ha of documen images. A combinaion of his complex background and a large variey of low-qualiy characers cause hresholding algorihms designed for documen image processing o perform poorly on video images. 1 6 We invesigae his issue and develop an RGBbased approach ha hresholds a video image in he color domain and fuses he obained hreshold values ino a se of muliple hreshold values via a clusering process. These values are hen used o segmen ex characers from he video image background. There are many color segmenaion echniques repored in he lieraure, 1 3,5 26 such as exure analysis, 11,15,20,23 hisogram hresholding or clusering, 3 6,8,9,19 edge deecion, 7 region spli and merging, 13,18 and feaure analysis. 1,13,16 Hisogram hresholding is probably one of he mos widely used echniques. 8,27 These segmenaion mehods basically perform pariions of an image ino consiuen pars or objecs. However, for our applicaions in ex deecion and recogniion, he only regions of ineres are hose ha may conain poenial ex, and hese regions can be more effecively segmened by hresholding raher han segmenaion. Therefore, his work primarily invesigaes an applicaion of color image hresholding o ex deecion and recogniion in video images. A general approach o color image hresholding is o ransfer a color image o a monochrome image, such as he hue-sauraion-inensiy HSI model 4 6 ha ransfers a color image ino an inensiy image. Then, various grayscale hresholding mehods can be applied o he monochrome image. 4 6,8 Unforunaely, such an approach encouners a problem, since he color informaion is decoupled during he color-o-monochrome image conversion. In many cases, he color informaion proves o be useful in hresholding. As an example, 3 Fig. 1 a shows an original color image ha has pure blue background and pure red ex characers wih he same grayscale. Figure 1 b shows an HSI-convered grayscale inensiy image ha has only one gray level. As we can see in Fig. 1 b, he ex characers disappeared because hese ex characers can be only recognized by heir red color in Fig. 1 a, no by heir 282 Op. Eng. 43(2) 282 289 (February 2004) 0091-3286/2004/$15.00 2004 Sociey of Phoo-Opical Insrumenaion Engineers
Du, Chang, and Thouin: Unsupervised approach o color... Fig. 1 An example of color image hresholding using HSI: (a) color image, (b) HSI conversion, and (c) desired resul. Fig. 3 A TV commercial: (a) original, (b) RGB/Osu, (c) RGB/JRE, (d) RGB/LE, (e) RGB/JE, (f) Osu s mehod, (g) JRE, (h) LE, and (i) JE. Fig. 2 Sep-by-sep implemenaion of RGB color hresholding mehod: (a) a video frame image, (b) red-dimension image, (c) Osu hresholded red-dimension image, (d) green-dimension image, (e) Osu hresholded green-dimension image, (f) blue-dimension image, (g) Osu hresholded blue-dimension image, (h) pseudo-color image, and (i) RGB/Osu. Fig. 4 A woman wih he name Denise Oliver: (a) original, (b) RGB/ Osu, (c) RGB/JRE, (d) RGB/LE, (e) RGB/JE, (f) Osu s mehod, (g) JRE, (h) LE, and (i) JE. Opical Engineering, Vol. 43 No. 2, February 2004 283
Du, Chang, and Thouin: Unsupervised approach o color... Fig. 5 A newscas video image: (a) original, (b) RGB/Osu, (c) RGB/ JRE, (d) RGB/LE, (e) RGB/JE, (f) Osu s mehod, (g) JRE, (h) LE, and (i) JE. Fig. 6 A caroon scene: (a) original, (b) RGB/Osu, (c) RGB/JRE, (d) RGB/LE, (e) RGB/JE, (f) Osu s mehod, (g) JRE, (h) LE, and (i) JE. Fig. 7 A counryside scene: (a) original, (b) RGB/Osu, (c) RGB/ JRE, (d) RGB/LE, (e) RGB/JE, (f) Osu s mehod, (g) JRE, (h) LE, and (i) JE. 284 Opical Engineering, Vol. 43 No. 2, February 2004
Du, Chang, and Thouin: Unsupervised approach o color... grayscale in Fig. 1 b. Figure 1 c shows he desired hresholded resul, which exracs he red ex characers from he blue background. This simple experimen demonsraes a necessiy of inclusion of color informaion in image hresholding. A 3-D color hisogram 19 may be a soluion. However, i requires remendous ime and memory o generae a 3-D color hisogram. In addiion, more ime and memory is furher required for image manipulaion. In his work, a novel approach is proposed, called an RGB-based hresholding, which can be used for hresholding video and oher color images. I firs obains hreshold values from each of he R,G,B color domains by a graylevel hresholding mehod, hen implemens an unsupervised wihin-class/beween-class clusering process o fuse hese R,G,B hreshold values o produce a se of muliple hresholds ha can exrac ex effecively while reaining original colors in he image background. Our proposed unsupervised clusering algorihm is derived from he concep of wihin-class and beween-class variances as suggesed by Osu. 28 One major advanage of his algorihm is ha i does no need o know how many classes are required o be clusered in advance, as is required for mos clusering processes. The algorihm is auomaically erminaed once he wihin-class variance is less han he beween-class variance for each clusered class. Addiionally, he number of classes o be clusered is bounded above by 2 3 8. Surprisingly, as demonsraed in our experimens, his simple mulihresholding mehod performs very well in ex deecion and exracion in various video images. The remainder of his work is organized as follows. Secion 2 briefly reviews Osu s mehod. Secion 3 develops an RGB hresholding mehod for video images. Secion 4 conducs a series of experimens o demonsrae he effeciveness of he proposed RGB hresholding mehod in a wide variey of video images. Finally, Sec. 5 concludes some remarks. 2 Osu s Mehod Osu s mehod 28 is one of he mos widely used hresholding echniques in image analysis, which has shown grea success in image enhancemen and segmenaion. Suppose L ha p i i 0 is he gray-level image hisogram of an image, and is he seleced hreshold gray-level value. We can hen calculae he probabiliies of background and foreground of he -hresholded image by P B i 0 p i L 1 and P F 1 P B i 1 p i. By virue of Eq. 1, he means and variances associaed wih he background and he foreground can be furher calculaed by B 1/P B ip i i 0 and 1 var B 1/P B i B 2 p i i 0 and L 1 var F 1/P F i 1 i F 2 p i, var beween-class P B B 2 P F F 2 where i 0 P B P F B F 2, L 1 ip i is he global mean of he image and var wihin-class P B var B P F var F. 5 A hreshold value Osu developed by Osu is he one ha maximizes var beween-class, or equivalenly minimizes, i.e., var wihin-class Osu arg max 1 L var beween-class arg min 1 L var wihin-class. 3 Unsupervised RGB Thresholding Mehod Color images are composed of 3-D informaion commonly represened by red, green, and blue. Our proposed RGB hresholding mehod firs applies a gray-level hresholding mehod o each of he R,G,B color domains, respecively. A number of differen gray-level hresholding mehods can be used for his purpose, including Osu s mehod, 28 or a 2-D hresholding mehod such as an enropy-based mehod 29 or a relaive enropy mehod. 4 6,30 The selecion of an appropriae gray-level hresholding mehod can be based on he image properies and applicaions. In his work, Osu s mehod is used for he following wo reasons. One is ha i has been shown o be an effecive 1-D gray-level hresholding mehod in mos images. Anoher is ha he proposed unsupervised cluser algorihm is also based on he crierion of wihin-class/beween-class variance used in Osu s mehod. Assume ha he hreshold values obained for each of hree colors, red (r), green (g), and blue (b) are specified by r, g, and b, respecively, via Eq. 6. Le each pixel in a video image be denoed by p i, j (r i, j,g i, j,b i, j ) T wih (i, j) being he spaial coordinae of he pixel. Using r, g, b as preliminary hreshold values in each color domain, he pixel p i, j (r i, j,g i, j,b i, j ) T can be hresholded as p i, j (r i, j,g i, j,b i, j ) T, according o r i, j 1 r i, j r, g 0 r i, j i, j r 1 g i, j g, 0 g i, j g 3 4 6 L 1 F 1/P F i 1 ip i, 2 b i, j 1 b i, j b. 7 0 b i, j b Opical Engineering, Vol. 43 No. 2, February 2004 285
Du, Chang, and Thouin: Unsupervised approach o color... As a resul, each (r,g,b)-color pixel in a video image can be encoded by a 3-bi binary codeword (c 1,c 2,c 3 ), where c i for 1 i 3 is a binary value aking eiher 0 or 1. Then, we cluser all image pixels ino eigh classes C k k 1 according o heir associaed codewords, where each codeword represens a clusered class. For example, all pixels encoded by 1,0,0 will be clusered ino one class. Nex, he mean of each clusered class, say he k h class C k, denoed by k (r k,g k,b k ) T is calculaed by r k ri, j C k r i, j b k bi, j C k b i, j ri, j C k 1, g k bi, 1. j C k gi, j C k g i, j 8 gi, 1, j C k Following he definiions of wihin-class and beween-class variances given in Osu s mehod, 28 we furher calculae he 8 wihin-class variance k for each class of C k k 1 as k 1 N k i, j Ck r i, j r k 2 g i, j g k 2 1/2 b i, j b k 2, 9 where N k is he number of pixels in class C k. The beweenclass variance kj beween wo classes C k and C j wih k j can be calculaed as: ij r k r j 2 g k g j 2 b k b j 2 ) 1/2, 8 10 where (r k, g k, b k ), (r j, g j, b j ) are defined by Eq. 8 and mean values of classes C k and C j correspond o he R,G,B domains, respecively. Now, we use he wihin-class variances k, j, and he beween-class variance kj obained earlier as crieria o reshuffle pixels o form a new se of clusers. If wo classes C k and C j for k j wih eiher k kj or j kj, hese wo classes mus be merged o one class. This is because he beween-class variance beween wo classes kj mus be greaer han heir individual wih-in class variances k and j. This reclusering process is repeaed unil all he beween-class variances are greaer han heir corresponding wihin-class variances, in which case no classes will be reshuffled. Figure 2 shows a video broadcas news example o demonsrae a sep-by-sep implemenaion of our proposed unsupervised RGB hresholding mehod. Figure 2 a is he original color video image. Figures 2 b, 2 d, and 2 f are red, green, and blue images of he color image in Fig. 2 a, respecively. Figures 2 c, 2 e, and 2 g are heir respecive hresholded images wih hresholds given by 149, 115, and 115, respecively. Figure 2 h is a pseudo-colored image o show he 149,115,115 -hresholded color image wih eigh differen colors before a reclusering process ook place. Figure 2 i is he 149,115,115 -hresholded color image wih six differen colors afer he image in Fig. 2 h was reclusered. This idea is very similar o ha of ISODATA, 31 excep for hree suble differences. One is ha he crieria used in our process are no neares neighboring rule NNR as commonly used in ISODATA. A second difference is ha if class A is merged wih class B, and class B is also merged o a hird, class C, hen hese hree classes, A, B, and C, mus be merged ino one class. As a resul, he number of classes is herefore reduced. This yields a hird difference, which is ha here is no need of predeermining he number of classes o be clusered as required by ISODATA. The deails of implemening our proposed RGB hresholding mehod can be summarized in he following sep-by-sep procedure. 3.1 RGB Color Thresholding Algorihm 1. Use a gray-level hresholding mehod, such as Osu s mehod, o hreshold video images in hree color domains individually. Le r, g, and b denoe he resuling hreshold values. 2. Use Eq. 7 o encode all he RGB -pixel vecors ino 3-bi binary codewords. All pixels wih he same codeword will be clusered ino a single class. 3. Use Eq. 8 o calculae he mean for each class, i.e., he gray-level inensiy average of pixels in each class along he R,G,B color domains. 4. Use Eq. 9 o calculae he wihin-class variance and he beween-class variance for each class. 5. For any wo classes C k and C j wih k j, compare he wihin-class variance k and j agains he beween-class variance kj o see if k kj or j kj. If no, erminae he reclusering process. Go o sep 7. Oherwise, coninue. 6. Merge he classes C k and C j ino one class and go o sep 3. I should be noed ha if one of he classes, C k and C j, is merged wih a hird class, C l, hese hree classes C k, C j, and C l mus be merged ino one class. Go o sep 3. 7. A his sep, no pixel vecors will be reshuffled and all he RGB -pixel vecors in each of he resuling classes will be assigned by he same color as he mean pixel vecor of ha paricular class. In oher words, he color of he cenroid of a class will be assigned o all he RGB -pixel vecors in ha paricular class. 4 Experimenal Resuls In his secion, we conduc a series of experimens o demonsrae he effeciveness of our proposed RGB hresholding mehod. Two observaions were winessed and ineresing. Firs, simple global gray-level hresholding mehods did no generally work for color hresholding. Second, he proposed RGB hresholding approach coupled wih simple gray-level hresholding mehods could significanly improve resuls, specifically in ex deecion. Four gray-level hresholding mehods were used in he RGB hresholding for comparison, which were Osu s mehod, 28 Pal and Pal s local enropy LE and join enropy JE, 29 and he join relaive enropy JRE mehod. 30 The selecion of Osu s mehod is based on: 1. ha he proposed clusering process 286 Opical Engineering, Vol. 43 No. 2, February 2004
Du, Chang, and Thouin: Unsupervised approach o color... Table 1 Threshold values generaed by he Osu, JRE, LE, and JE mehods. RGB/Osu (R,G,B) RGB/JRE (R,G,B) RGB/LE (R,G,B) RGB/JE (R,G,B) TV commercial (141,138,122) (188,14,121) (140,133,150) (185,167,121) Woman (111,89,81) (4,6,94) (74,82,66) (155,130,95) Newscas (115,119,111) (116,115,108) (135,132,135) (116,117,106) Caroon scene (127,111,105) (131,117,93) (119,109,129) (128,124,99) Counryside scene (95,100,106) (10,11,86) (161,81,76) (94,92,85) adops he same crierion used in Osu s mehod, i.e., wihin-class and beween-class variances; and 2. ha Osu s mehod is a widely used hresholding echnique, which has been shown o be very effecive for gray-level images. On he oher hand, enropy-based hresholding mehods have shown o be promising and effecive in image hresholding. In paricular, a recen repor in Ref. 5 showed ha JRE was effecive in color hresholding when a single hreshold value was used. These four mehods were implemened in sep 1 in he RGB hresholding mehod, referred o as RGB/Osu, RGB/LE, RGB/JE and RGB/JRE, respecively. Their resuls were hen compared o he resuls obained by single gray-level hresholding mehods. 1209 single-frame video images were used for he experimens. These 1209 video images are from he Universiy of Maryland, College Park daabase, which covers a broad range of differen kinds of video images from differen TV channels: TV commercial video, caroons, news, soaps, and TV shows. No conclusion can be drawn on which one RGB color hresholding mehod performs beer han he ohers. Neverheless, in all he cases, he single hreshold gray-level hresholding mehods, Osu s mehod, LE, JE, and JRE did no perform well compared o hose using RGB hresholding. Due o limied space, we only include five experimens in his work, which are represenaive for demonsraing he superior performance of our proposed RGB color hresholding approach. Figures 3 7 show he resuls produced by RGB/Osu, RGB/LE, RGB/ JE, and RGB/JRE, where we can see ha RGB/Osu, RGB/ LE, RGB/JE, and RGB/JRE performed slighly differenly, bu all of hem performed subsanially beer han did he single gray-level hresholded mehods. Table 1 abulaes he hreshold values generaed by he Osu, JRE, LE, and JE mehods along each of he R, G, B color domains for comparison. As shown in Table 1, all he hreshold values generaed by he four mehods are very differen for five esed scenes in a wide range. Table 2 also liss he number of colors required for each of he four RGB color hresholding mehods for video image segmenaion resuling from he proposed beween-class/wihin-class clusering process in he RGB color hresholding algorihm. Each class requires one color for all he RGB -pixel vecors in ha class. The color of each class was chosen o be he color of he cenroid of ha paricular class. Figure 3 is ineresing and deserves more explanaion. I is a TV commercial where he scene has a box wih he ex grape-nus on he fron cover and a glove nex o he box. All four of he RGB color hresholded mehods successfully exraced he ex wih wo suble differences, he glove and rainbow background behind he box. Excep for he fac ha he RGB/ JRE exraced mos of he glove including he finger porion, he oher hree could no segmen he glove from he background, as is gray color is close o he background. As for he rainbow background, RGB/Osu pulled i ou wih hree colors, compared o wo colors from he oher hree RGB color hresholding resuls. In erms of numbers of colors used, he RGB/Osu is he bes because i only used hree colors compared o four colors required by he oher hree. Neverheless, i seemed ha RGB/JRE produced he bes resul in he sense ha i exraced he ex and glove ha he ohers could no. Figure 4 shows a woman wih name Denise Oliver in a black background. The resul produced by he RGB/JE seemed bes, because he exraced ex had beer conras and he shadow under he neck was also shown in is hresholded resul. As for single gray-level hresholding mehods, JRE performed he wors, while he oher hree performed very similarly. Figure 5 shows a very busy newscas video image, where all four RGB color hresholding mehods performed very closely. Figure 6 is a caroon scene, where here are wo caroon characers wih ex Life wih Louie 25 on he op. All four RGB color hresholding mehods segmened he ex and he wo caroon characers from he background very well, and here was no visible difference in heir hresholded resuls. Similarly, he resuls produced by he four single gray-level hresholding mehods were also very close, bu cerainly were no good. Figure 7 is a counryside scene wih he ex Hallmark Hall of Fame on he lef corner. The RGB/LE and RGB/JE seemed o perform slighly beer han RGB/Osu and RGB/JRE in erms of conras in he image background and he ex Hall of Fame, which was exraced more visibly. For he resuls produced by he four single gray-level hresholding mehods, hey were all similar, where he ex was barely exraced. These experimens demonsraed ha he proposed RGB color hresholding is promising in he segmenaion of color Table 2 Number of colors required for four RGB color hresholding mehods. RGB/Osu RGB/JRE RGB/LE RGB/JE TV commercial 4 5 5 5 Woman 4 3 4 4 Newscas 5 5 5 5 Caroon scene 7 7 7 7 Counryside scene 4 3 4 4 Opical Engineering, Vol. 43 No. 2, February 2004 287
Du, Chang, and Thouin: Unsupervised approach o color... images, which generally canno be accomplished by a single gray-level hresholding mehod. Addiionally, hese experimens also showed ha he proposed RGB color hresholding could exrac no only scene ex in Fig. 3, bu also superimposed ex in Figs. 4 7. 5 Conclusions An RGB color hresholding approach is developed for segmenaion of video images. I is a simple mulihreshold segmenaion mehod ha implemens a gray-level hresholding mehod in each of he R, G, B domains, hen uses he generaed hreshold values as a base o produce a se of desired muliple hreshold values for video image segmenaion by means of an unsupervised clusering process. Several conribuions are made. One is ha i exends single gray-level hresholding echniques o mulilevel hresholding for video images. Anoher is ha he designed clusering process in he RGB color hresholding is a new approach ha uses an unsupervised beween-class/wihinclass-based algorihm o fuse differen hreshold values obained from he RGB color domain. A hird conribuion is ha he clusering process can be implemened in conjuncion wih any gray-level hresholding echnique o adap o various applicaions. The experimens demonsrae ha he proposed RGB color hresholding mehod performs significanly beer han single gray-level hresholding mehods. Also shown in he experimenal resuls, he performance of he proposed RGB color hresholding mehod is very robus o he selecion of he single gray-level hresholding mehod ha is used o produce hreshold values in he RGB domain. This advanage is very imporan, since we do no have o specify a paricular single graylevel hresholding mehod in he RGB color hresholding. I should be noed ha in he case ha oher color spaces, such as CIE Commission Inernaionale de l Eclairage, YUV Y: luminance, U: Red-Y, V: Blue-Y, and YIQ Y: luminance, I 0.74V 0.27U, Z 0.48V 0.41U are used, hey can be firs ransferred o he RGB color space in he same manner as was done for he HSI color space, and hen our proposed RGB color hresholding mehod follows aferward. Acknowledgmens The auhors would like o hank he Deparmen of Defense for suppor of heir work hrough conrac number MDA- 904-00-C2120. In addiion, he auhors would also like o hank Dr. D. Doermann of he Language and Media Processing Laboraory a he Universiy of Maryland, College Park, for providing he daabase used for hese experimens. References 1. C. M. Tsai and H. J. Lee, Binarizaion of color documen images via luminance and sauraion color feaures, IEEE Trans. Image Process. 11 4, 434 451 2002. 2. G. Nagy, Tweny years of documen image analysis in PAMI, IEEE Trans. Paern Anal. Mach. Inell. 22 1, 38 62 2000. 3. Y. Du, C. I. Chang, and P. D. Thouin, An unsupervised approach o color video hresholding, IEEE Inl. Conf. Acous. Speech, Sig. Process. (ICASSP) 3, 373 376 2003. 4. J. Wang, Y. Du, C. Chang, and P. Thouin, Relaive enropy-based mehods for image hresholding, IEEE In. Symp. Circ. Sys. (ISCAS) 2, 265 268 2002. 5. Y. Du, C. Chang, and P. D. Thouin, Thresholding video images for ex deecion, Inl. Conf. Pa. Recog. (ICPR) 3, 919 922 2002. 6. Y. Du, Tex deecion and resoraion of color video images, PhD disseraion, Universiy of Maryland, Balimore Couny 2003. 7. Y. Yuan, D. Goldman, A. Moghadamzadeh, and N. Bourbakis, Segmenaion of colour images wih highlighs and shadows using fuzzylike reasoning, Pa. Anal. Appl. 4, 272 282 2001. 8. H.-D. Cheng and Y. Sun, A hierarchical approach o color image segmenaion using homogeneiy, IEEE Trans. Image Process. 9 12, 2071 2082 2000. 9. S. Wesolkowski, R. D. Dony, and M. E. Jernigan, Global color image segmenaion sraegies: Euclidean disance vs. vecor angle, IEEE Sig. Process. Soc. Workshop Neural Ne. Sig. Process. 9, 419 428 1999. 10. D. Panjwani and G. Healey, Resuls using random field models for he segmenaion images of naural scenes, IEEE 5h Inl. Conf. Compuer Vis., 714 719 1995. 11. L. Shafarenko, M. Perou, and J. Kiler, Auomaic waershed segmenaion of randomly exured color images, IEEE Trans. Image Process. 6 1, 1530 1544 1997. 12. A. Tremeau and P. Colanoni, Regions adjacency graph applied o color image segmenaion, IEEE Trans. Image Process. 9 4, 735 744 2000. 13. T. Uchiyama and M. A. Arbib, Color image segmenaion using compeiive learning, IEEE Trans. Paern Anal. Mach. Inell. 16 12, 1197 1206 1994. 14. J. Liu and Y. H. Yang, Muliresoluion color image segmenaion, IEEE Trans. Paern Anal. Mach. Inell. 16 7, 689 700 1994. 15. D. K. Panjwani and G. Healey, Markov random field models for unsupervised segmenaion of exured color images, IEEE Trans. Paern Anal. Mach. Inell. 17 10, 939 954 1995. 16. B. Bhabu and J. Peng, Adapive inegraed image segmenaion and objec recogniion, IEEE Trans. Sys. Man Cybern. 30 4, 427 441 2000. 17. G. Healey, Segmening images using normalized color, IEEE Trans. Sys. Man Cybern. 22 1, 64 73 1992. 18. S. C. Zhu and A. Yuille, Region compeiion: Unifying snakes, region growing and bayes/mdl for muliband image segmenaion, IEEE Trans. Paern Anal. Mach. Inell. 18 8, 884 900 1996. 19. L. Shafarenko, M. Perou, and J. Kiler, Hisogram-based segmenaion in a percepually uniform color space, IEEE Trans. Image Process. 7 9, 1354 1358 1998. 20. M. Mirmehdi and M. Perou, Segmenaion of color exures, IEEE Trans. Paern Anal. Mach. Inell. 22 2, 142 159 2000. 21. C. S.-Fuh and S. W. Cho, Hierarchical color image region segmenaion for conen-based image rerieval sysem, IEEE Trans. Image Process. 9 1, 156 162 2000. 22. J. Zhang, J. Gao, and W. Liu, Image sequence segmenaion using 3-D srucure ensor and curve evoluion, IEEE Trans. Circuis Sys. Video Technol. 11 5, 629 641 2001. 23. T. Gevers, Image segmenaion and similariy of color exure objecs, IEEE Trans. Mulimedia 4 4, 509 516 2002. 24. N. Li and Y. F. Li, Feaure encoding for unsupervised segmenaion of color images, IEEE Trans. Sys. Man Cybern. 33 3, 438 447 2003. 25. J. P. Wang, Sochasic relaxaion on pariions wih conneced componens and is applicaion o image segmenaion, IEEE Trans. Paern Anal. Mach. Inell. 20 6, 619 636 1998. 26. N. Papamarkos, A. E. Asalakis, and C. P. Srouhopoulos, Adapive color reducion, IEEE Trans. Sys. Man Cybern. 32 1, 44 56 2002. 27. A. Moghaddamzadeh, D. Goldman, and N. Bourbakis, A fuzzy-like approach for smoohing and edge deecion in color images, Inl. J. Pa. Recogn. Arif. Inell. 12 6, 801 816 1998. 28. N. Osu, A hreshold selecion mehod from gray-level hisograms, IEEE Trans. Sys. Man Cybern. 9 1, 62 66 1979. 29. N. R. Pal and S. K. Pal, Enropic hresholding, Signal Process. 16, 97 108 1989. 30. C. I. Chang, K. Chen, J. Wang, and M. L. G. Alhouse, A relaive enropy-based approach o image hresholding, Paern Recogn. 27 9, 1275 1289 1994. 31. R. O. Duda and P. E. Har, Paern Classificaion and Scene Analysis, John Wiley Inerscience, New York 1973. Yingzi Du received BS and MS degrees in elecrical engineering from Beijing Universiy of Poss and Telecommunicaions in 1996 and 1999, respecively. She received her PhD in elecrical engineering from he Universiy of Maryland, Balimore Couny, in 2003. She is currenly a research assisan professor wih he Deparmen of Elecrical Engineering a he Unied Saes Naval Acadamy. Her research ineress include biomerics, mulispecral/hyperspecral image processing, video image processing, medical imaging, and paern recogniion. She is a member of SPIE and IEEE, a life member of Phi Kappa Phi, and a member of Tau Bea Pi. Chein-I Chang received his BS degree from Soochow Universiy, Taipei, Taiwan, in 1973, he MS degree from he Insiue of Mah- 288 Opical Engineering, Vol. 43 No. 2, February 2004
Du, Chang, and Thouin: Unsupervised approach o color... emaics a Naional Tsing Hua Universiy, Hsinchu, Taiwan, in 1975, and MA degree from he Sae Universiy of New York a Sony Brook, in 1977, all in mahemaics. He also received his MS and MSEE degrees from he Universiy of Illinois a Urbana-Champaign in 1982, and PhD degree in elecrical engineering from he Universiy of Maryland, College Park, in 1987. He has been wih he Universiy of Maryland, Balimore Couny, since 1987, as a visiing assisan professor from January 1987 o Augus 1987, assisan professor from 1987 o 1993, associae professor from 1993 o 2001, and professor in he Deparmen of Compuer Science and Elecrical Engineering since 2001. He was a visiing research specialis in he Insiue of Informaion Engineering a he Naional Cheng Kung Universiy, Tainan, Taiwan, from 1994 o 1995. He received an Naional Research Council senior research associaeship award from 2002 o 2003 a he U.S. Army Soldier and Biological Chemical Command, Edgewood Chemical and Biological Cener, Aberdeen Proving Ground, Maryland. He has a paen on auomaic paern recogniion and several pending paens on image processing echniques for hyperspecral imaging and deecion of microcalcificaions. His research ineress include auomaic arge recogniion, mulispecral/hyperspecral image processing, medical imaging, informaion heory and coding, signal deecion and esimaion, and neural neworks. He is he auhor of he book Hyperspecral Imaging: Techniques for Specral Deecion and Classificaion, published by Kluwer Academic/Plenum Publishers. Dr. Chang is an Associae Edior in he area of hyperspecral signal processing for IEEE Transacions on Geoscience and Remoe Sensing. Heisa senior member of IEEE and a member of Phi Kappa Phi and Ea Kappa Nu. Paul David Thouin received his BS degree in elecrical engineering from he Universiy of Michigan, Ann Arbor, in 1987. In 1993, he obained his MSEE degree from George Washingon Universiy in Washingon D.C. He received his PhD in elecrical engineering from he Universiy of Maryland, Balimore Couny, in 2000. He has been employed by he U.S. Deparmen of Defense since 1987, where he is a senior engineer currenly assigned o he Image Research Branch in he Research and Developmen Group. His research ineress include image enhancemen, saisical modeling, documen analysis, paern recogniion, and muliframe video processing. He is a senior member of IEEE, and a member of Phi Kappa Phi. Opical Engineering, Vol. 43 No. 2, February 2004 289