Block Histogram-Based Neural Fuzzy Approach to the Segmentation of Skin Colors *

Transcription

1 JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 23, (2007) Block Histogram-Based Neural Fuzzy Approach to the Segmentation of Skin Colors * Department of Electrical Engineering National Chung Hsing University Taichung, 402 Taiwan cfjuang@dragon.nchu.edu.tw Skin color segmentation by a block histogram-based neural fuzzy network is proposed in this paper. The Hue-Saturation (HS) color model is used. Color information is represented by a block histogram in an HS space image. Several non-uniform quantization approaches on HS space are proposed to represent histogram information as accurately as possible. The neural fuzzy network used is the self-constructing neural fuzzy inference network (SONFIN). Block histogram information from images under different environments is used to train SONFIN to make the method as robust as possible. Experiments on skin color segmentation are performed to verify the performance of the proposed method. For comparison, three other segmentation methods, including principal component transformation (PCT), histogram-based skin classifier (HSC), and mixture of Gaussian classifier (MGC) are applied to the same problem. Comparisons show that the proposed approach achieves the best segmentation results. In addition, the proposed non-uniform HS partition approach also improves segmentation performance. Keywords: neural networks, fuzzy systems, fuzzy neural network, histogram-based model, mixture of Gaussian model, irregular space partition. INTRODUCTION Many studies have proposed image segmentation algorithms in recent years regarding motion [], region edge [2], and color information [3-9]. Color is a visual feature which is immediately evident in an image, and it is an important aspect that human senses use to cluster desired objects. Color image segmentation has been extensively applied in pattern recognition, image analysis and computer vision. This technique divides an image into different regions such that each region is homogeneous [6-8]. Experts have proposed many image segmentation techniques based on color information, including principal component transformation (PCT) [0, ], fuzzy C-means (FCM) [2-4], neural networks [5, 5], color histogram [6-20], and a mixture of Gaussian classifiers [20-22]. The fuzzy C-means (FCM) method has considerable difficulty under noisy environments in the feature space. The FCM iterative clustering process also wastes a lot of time on each image, and is not suitable for real-time operations. In the histogram-based approach, color similarities are computed according to allocations on histogram bins. The possibility of using color histograms for color image indexing was previously demonstrated in [6]. Ahmad [7] segmented human hands by tracking image Received September 5, 2005; revised June 30, 2006; accepted August 9, Communicated by Chung-Yu Wu. * A preliminary version has been presented in IEEE & INNS International Joint Conference on Neural Network, Montreal, Canada, Aug.,

2 738 patches to the stored histogram using a histogram intersection algorithm. Since only one hand histogram patch is used as template histogram and background patches are not considered, this approach may be very sensitive to environment. Another study [20] proposes a histogram-based classifier, where the histogram represents the probability density function in a likelihood ratio calculation. A major drawback of this approach is that a considerable amount of training data is required. In addition, the total number of histogram bins is usually very large. The mixture of Gaussian classifier is a statistical method, like the histogram-based models above, widely used in skin-color segmentation [20-22]. This paper proposes classifier design using a neural fuzzy network. Instead of the aforementioned statistical classifiers which perform segmentation directly on the color model of each pixel, this paper proposes segmentation based on block pixel histograms. A block histogram in an image is the feature vector in this paper. Segmentation occurs based on a neural fuzzy model instead of a statistical model. The histogram feature vector is calculated on an image block instead of all of image pixels, considerably reducing the total number of histogram bins. Several non-uniform partition approaches are proposed to represent block color information as accurately as possible, instead of using the general uniform partition for a color space. In the general histogram matching method [8], two colors are totally different if they fall into two different bins even though they might be very similar to each other. This makes the color histogram segmentation method sensitive to noise interference. Segmentation by neural fuzzy network helps solve this problem, and the neural fuzzy network used in this paper is the self-constructing neural fuzzy inference network (SONFIN) [23]. The remaining sections of this paper are organized as follows. Section 2 describes color space partition approaches and histogram computation. Section 3 describes SON- FIN structure and its application to skin color image segmentation. Section 4 experiments with the proposed skin-color image segmentation method and several other methods. Section 5 presents conclusions and summary. 2. COLOR SPACE PARTITION AND HISTOGRAM COMPUTATION Several color spaces for skin segmentation have been proposed [24, 25]. The Hue and Saturation (HS) color space from the Hue, Saturation, and Value (HSV) color space are used in this paper. The two following equations can transform traditional RGB color spaces into H and S color spaces [8, 24] Max( RGB,, ) Min( RGB,, ) S =, () Max( RGB,, ) 0.5[( R G) + ( R B)] H, B G H = = ( R G) + ( R B)( G B) cos, H H, B > G HS color space has three main advantages: (i) the brightness or lightness component is irrelevant to the chromatic information of images; (ii) the chromatic component consisting of hue and saturation is intuitive; (iii) the use of only two-dimensional color space reduces the number of space bins required for histogram computation. (2)

3 NEURAL FUZZY APPROACH TO SKIN SEGMENTATION 739 Color histograms are a commonly-used and highly efficient way to describe image color properties. A color histogram is obtained by counting the number of pixels that fall into each bin. Color quantization plays an important role in representing color content in histograms. Quantization must be fine enough that distant colors are not placed in the same bin. In this paper, quantization is based on four HS space partition types. The primary goal of these non-uniform partitions is to equally distribute image pixels into each histogram bin. For example, suppose there are nine pixels. The following describes the partition process of each approach. Type A This is a basic uniform partition, where the division interval on H and S is equal, as Fig. (a) illustrates. Most histogram methods, e.g. the histogram-based skin classifier [20], use this type of partition. (a) (b) (c) (d) Fig.. (a) Type A division; (b) Type B division; (c) Type C division; (d) Type D division. Type B For each new partition, the bin with the maximum number of pixels is partitioned into four equal bins so that pixels are distributed into each bin as equally as possible. The design process is as follows. To perform a new partition, the number of pixels in each bin

4 740 is calculated. The bin with the highest number of pixels is equally partitioned into four smaller bins. This partition process is shown in Fig. (b), where Di denotes the ith partition. Type C For each new partition, the original bin is equally partitioned, vertically or horizontally, into two bins so that the pixels are roughly equally distributed in each bin. Type B partitions may have a large variation in pixel distribution in each of the four newly generated bins. Type C solves this problem by performing a horizontal partition followed by a vertical partition in each partition process. As in Type B partitioning, the bin with the highest number of pixels is partitioned first. A horizontal line partitions the bin into two equally sized bins at the beginning of each partition process. Then, the bin with the highest number of pixels is vertically partitioned into two equally sized bins. One partition process ends after the vertical partition is completed. Thus, n partition processes will produce a total of n + bins. An illustration of the partition process is shown in Fig. (c). Type D Types B and C methods partition each original bin into two or four bins of equal size without considering pixel distribution in the original bin. The Type D method partitions each original bin into two bins, but for each new vertical or horizontal partition, the partition position should distribute the pixels equally into the two new bins. Since pixel distribution in the original bin determines the new partition positions, this method can achieve a more uniform pixel distribution in each bin than Types C and D. Specifically, it is similar to Type C, except that in each horizontal or vertical partition, the partition criterion is that the number of pixels in each newly generated bin should be the same. An illustration of this partition process is shown in Fig. (d). Section 4 discusses the performance of the above four types of partitions using neural fuzzy network classifiers. 3. SONFIN Structure 3. SKIN COLOR SEGMENTATION USING SONFIN The neural fuzzy network used for skin color image segmentation is the self-contructing neural fuzzy inference network (SONFIN) [23]. This subsection describes network structure and learning of the SONFIN. The structure of the SONFIN is shown in Fig. 2. This six-layered network realizes a fuzzy model of the following form: i i Rule i: IF x is A and and x n is A n n n i i i i THEN y is a 0 + a j xj and y 2 is a20 + a2 j xj, (3) j= where A i j is the fuzzy set of the jth linguistic term of input variable x j, and a i kj s are the consequent parameters. j=

5 NEURAL FUZZY APPROACH TO SKIN SEGMENTATION 74 y y2 Layer 6 Σ Σ Layer 5 Layer 4 Layer 3 a a 2 x a2 22 x R R 2 a Layer 2 Layer x Fig. 2. SONFIN structure. ( k) ( k) Let u i and o i denote the input and output of the ith node in layer k, respectively. We shall describe the functions of the nodes in each of the six layers of the SONFIN as follows. Layer : No computation is done in this layer. Each node in this layer, which corresponds to one input variable, only transmits input values to the next layer directly. That is o () = u (). (4) Note that in enhanced SONFIN [23], a linear transformation of input invariables may be performed in the layer. Thus, although no function is performed in Layer of the used SONFIN, we keep it for consistency with the original six-layered structure of SON- FIN. Layer 2: Each node in this layer corresponds to a fuzzy set of one of the input variables in Layer. With the choice of Gaussian membership function, the operation performed in this layer is o (2) (2) 2 i mij 2 σij ( u ) = (5) where m ij and σ ij are, respectively, the center and width of the Gaussian membership

6 742 function of the jth partition for the ith input variable u i. Layer 3: A node in this layer represents one fuzzy logic rule. We use the following AND operation for each Layer-3 node: o q (3) (3) ui i= = (6) where q is the number of Layer-2 nodes participating in the IF part of the rule. Layer 4: The firing strength calculated in Layer 3 is normalized in this layer by r (4) (4) (4) i / j j= o = u u (7) where r is the number of rule nodes in Layer 3. Layer 5: This layer is called the consequent layer. Two types of nodes are used in this layer and they are denoted as blank and shaded circles in Fig. 2, respectively. The node denoted by a blank circle (blank node) is the essential node representing a Gaussian i fuzzy set of the output variable. Only the center, a k0 in Eq. (3), of each Gaussian set is delivered to the next layer. As to the shaded node, it represents a linear combination of n j input variables, i.e. a i ki x = i in Eq. (3), it is generated only when necessary. Here, both of the two nodes are used and each rule has a corresponding blank node as well as a shaded node. The whole function performed by this layer is n (5) i i (5) oki = akj xj + ak0 ui. (8) j= Layer 6: Each node in this layer corresponds to one output variable. The node integrates all the actions recommended by Layer 5 and acts as a defuzzifier with y k i (5) ki. = o (9) For SONFIN learning, there are no rules initially in the SONFIN. They are created and adapted as learning proceeds via simultaneous structure and parameter learning. For structure learning, the number of rules generated is influenced by a pre-specified threshold F.For in parameter learning, the consequent part parameter is tuned by recursive least square with learning rate controlled by forgetting factor λ, and the antecedent part parameters are tuned based on gradient descent controlled by learning rate η. Detailed learning algorithms can be found in [23]. 3.2 Skin Color Segmentation by SONFIN This subsection introduces how to train SONFIN as a skin classifier regardless of

7 NEURAL FUZZY APPROACH TO SKIN SEGMENTATION 743 the color feature vector type. SONFIN training inputs are color features and the outputs show the category, skin or non-skin color, to which the input feature belongs. There are two outputs in SONFIN. When the inputs are skin color feature vectors, the desired output is (, 0); otherwise, the desired output is (0, ). During a test, when an unknown feature vector is input in SONFIN, it computes the two outputs (y, y2). If y θ + y2, (0) the corresponding pixel(s) is(are) classified as skin color, where θ is a threshold which can adjust correct detections and false positives. This paper proposes SONFIN segmentation using histogram values as feature vectors. For a histogram-based SONFIN classifier, an original image is divided into nonoverlapping blocks, where each block size is 0 0 pixels. For an image measuring pixels, there will be a total of 3,072 non-overlapping blocks. Image blocks from different people and backgrounds supply training data, increasing the robustness of the segmentation system. This training data provides a basis for partitioning HS space using one of the four partition methods introduced in section 2. According to the HS space partition, block image histograms are computed and used as network inputs. The following section describes the testing of the four partition types on the HS space introduced in section EXPERIMENTS Each image in the experiment measured pixels. Training patterns were taken from,000 blocks from 50 images, and 0 skin blocks and 0 non-skin blocks were randomly selected from each training image. As stated in section 3, each block measured 0 0 pixels. Some examples of the training images and corresponding sampled blocks are shown in Fig. 3. Another 500 images were used to test segmentation performance. Fig. 3. (a) Original training images and corresponding sampled blocks (denoted by white blocks).

8 744 Fig. 3. (b) The segmented SONFIN results using Type C partition. Each image contained skin-pixels, and the percentage of skin-color pixels in the 50 training and 500 test images was 9.20% and 3.35%, respectively. These images differed in skin-colors, backgrounds and lighting conditions. To verify performance of the proposed histogram feature, SONFIN design by histogram and HS values as feature vectors are performed. Color image segmentation using principal component transformation (PCT), histogram-based skin classifier (HSC) [20], and mixture of Gaussian classifier (MGC) [20-22] were tested and compared. 4. Histogram-based SONFIN Classifier The 00,000 pixels from the,000 training blocks were transformed to HS space, and their HS space distributions are shown in Fig. 4. The four types of partitions introduced in section 2 are tested here. Figs. 4 (a)-(d) show partition results for the 00,000 pixels by Types A-D, where each HS space was partitioned into 49 bins. To use the histogram-based SONFIN segmentation method, we first had to train SONFIN. Histograms of the,000 training blocks were used as SONFIN inputs. SONFIN performance was tested by histograms from the four types of divisions that partitioned the HS space into 49 bins. The SONFIN input and output dimensions were 49 and 2, respectively. The parameter learning parameters were set at λ =.0 and η = 0.02, and training took place for only 5 iterations. The structure learning parameter F in varied from to 0.02, so that after training, 9 input clusters (rules) were generated for each partition approach. The correct classification rate was used for evaluating training performance. This rate is defined as the number of correctly classified training pixels divided by the number of training pixels. For training performance, the correct classification rates on the 00,000 training pixels by the four partition approaches with θ = 0 were similar and all approximately %. For segmentation, each original image was divided into non-overlapping blocks. As mentioned previously, each block measured 0 0 pixels. Thus, each image comprised 3,072 blocks. When a block was classified as skin, all 00 pixels in that block were labeled

9 NEURAL FUZZY APPROACH TO SKIN SEGMENTATION 745 (a) (b) (c) (d) Fig. 4. Distributions of the training pixels on HS space and partition results by Types A-D. as skin. Block size is related to the bin number in the HS color space. The number of pixels in a block should generally be larger than the bin number, so that on average there is at least one pixel in each bin. However, if the block is too large, segmentation resolution will be reduced and performance will be degraded. The 0 0-pixel block size was selected in this paper under the above considerations. To achieve a higher segmentation resolution using the same block size, overlapping blocks may be segmented at the expense of computation time. A receiver operating characteristic (ROC) curve [26] was used to measure segmentation performance. The ROC curve indicates the relationship between Detection rate and False positive rate as a function of the detection threshold, where the detection rate gives the fraction of skin pixels classified correctly and the false positive rate gives the fraction of non-skin pixels mistakenly classified as skin. Here, the detection threshold is the θ in Eq. (0). A large area under the ROC curve indicates a better segmentation performance. The alternative ROC evaluation method is to compare the detection rates of different methods under the same false positive rate, for example, 0%. Figs. 5 and 6 show the performance of histogram-based SONFIN classifiers for the 50 training images and 500 test images, respectively. In these two figures and the following ROC figures, the vertical axis is labeled as Detection rate and the horizontal axis is labeled as False positive rate. These illustrations show that the proposed non-uniform

10 746 Fig. 5. The ROC curves of histogram-based SONFIN classifiers with Types A-D partitions for training images. Fig. 6. The ROC curves of histogram-based SONFIN classifiers with Types A-D partitions for test images. partition achieves better results than uniform partition, and among the three non-uniform partition methods, Type C achieved the best test results. Segmentation results for the training images shown in Fig. 3 (a) and illustrative test images using SONFIN with Type C partition are shown in Fig. 3 (b) and Fig. 7, respectively. Different lighting conditions and backgrounds are evident in these images, and good results are achieved. The experiments above show segmentation results using four color space partition methods. In fact, Fig. 4 clearly indicates that the distribution of the interested pixels is non-uniform. Thus, the three non-uniform partition methods (Types B, C, and D) perform better than the uniform partition method (Type A) when the same number of bins is used. The Type A method is best suited to applications where the interested image pixels are uniformly distributed in a color space. This is simply illustrated by the problem of retrieving an image from an image database, where pixel distribution of the sought image is uniform in the color space. Between Types B, C, and D, the Type D method has the highest ability to distribute training pixels equally to each bin. However, the Type D method designates each new partition position according to the training pixel number, and partition results are highly dependent on training data. Thus, Type D may perform well in training at the expense of generalization performance. This explains why Type D segmentation results are better than Type C for training data but worse than Type C for test data. For problems where the collected training image pixels are known to be representative of the whole image set, Type D would be a good choice. Types B and C are also suitable for problems where the interested pixels are non-uniformly partitioned. Types B and C create four and two bins at each new partition, respectively. Thus, Type B is more likely to generate bins without pixels located in them than Type C, as Fig. illustrates. Thus, Type C generally performs better than Type B for problems with non-uniformly partitioned pixels, such as the skin color segmentation in this paper. 4.2 HS Value-based SONFIN Classifier SONFIN segmentation using the two HS values as inputs was simulated to illustrate the histogram feature effect, and two sets of simulations were performed. The block unit was the basis for segmentation in the first simulation. The HS values of the 00 pixels in

11 NEURAL FUZZY APPROACH TO SKIN SEGMENTATION 747 Fig. 7. Segmentation results for test images using a histogram-based SONFIN classifier with Type C partition.

12 748 a block were averaged and fed as SONFIN input. The 000 average HS values from the,000 training blocks were used to train SONFIN, developing forty rules in the process. The correct classification rate on the 00,000 training pixels was 99.94%. For segmentation, as in histogram-based segmentation, each original image was divided into non-overlapping blocks. The average HS values of each block were fed as SONFIN input, and when a block was classified as skin, all 00 pixels in the block were labeled as skin. The ROC curves for training and test images are shown in Figs. 8 and 9, respectively, and are denoted as SONFIN (HS-block). For the second simulation experiment, segmentation is performed directly on each pixel. The two HS values of the 00,000 pixels from the,000 training blocks were used for SONFIN training. After training, there were also 40 rules in SONFIN. The correct classification rate on the 00,000 training pixels was 99.86%. The 307,200 pixels in each image were classified independently in segmentation. The ROC curves for training and test images are shown in Figs. 5 and 6, respectively, and are denoted as SONFIN (HSpixel). Results show that using the HS values of a single pixel (HS-pixel) or the average HS values of pixels in a block (HS-block) produced similar performance. Overall, Figs. 8 and 9 clearly illustrate that using histogram feature in a SONFIN classifier produces better results than using HS values. Fig. 8. The ROC curves of different types of classifiers for training images. Fig. 9. The ROC curves of different types of classifiers for test images. 4.3 Other Compared Methods The first compared method was PCT [27] color space segmentation. First, average H and S values of the 500 training skin blocks were computed. The average HS value for the kth sample is represented by x k = [H S] T. For the 500 skin blocks, T T xx k k = λiww i i k= i=, where λ λ 2 are eigenvalues and w s are the corresponding eigenvectors. The eigenvector w corresponding to the largest eigenvalue is called the principal eigenspace. The i projection of x ave, the average of the 500 x k s, onto w is denoted as p d. For each im-

13 NEURAL FUZZY APPROACH TO SKIN SEGMENTATION 749 age, x of each non-overlapping block was found, and its projection onto w, is denoted by p. A block was classified as skin color if 0.5p d p θp d ; otherwise, it was classified as non-skin color, where θ > 0.5 is the threshold. The correct classification rate for the 00,000 training pixels with θ = 2.2 was 99.68%. The ROC curves for PCT training and test images are shown in Figs. 8 and 9, respectively. HSC method comparison involved skin and non-skin histogram models using 50,000 skin pixels and 50,000 non-skin pixels, respectively, from the 00,000 training pixels. The HS space contained 32 bins per channel in the two histogram models, equally partitioning the HS into bins. Skin and non-skin histogram models help compute the probability that a given color value belongs in the skin or non-skin classes [20]. After training, the classification rate for the 00,000 training pixels was 99.80%. The ROC curves for HSC training and test images are shown in Figs. 8 and 9, respectively. The MGC method uses a mixture density function expressed as the sum of Gaussian kernels: N Px ( ) = π exp( ( x μ ) ( x μ )), i= T i /2 i i i 2 π 2 i (2) where x = [H S] T, π i is mixing parameter, μ i is mean vector, and i is diagonal covariance matrix. Two separate mixture models were trained for the skin and non-skin classes, and 20 Gaussians were used in each model. The total of 40 Gaussian kernels corresponded to the 40 rules in SONFIN. The average HS values of each block image were used as feature vectors. The standard Expectation-maximization (EM) algorithm [28] trained skin and non-skin models using the 5000 skin and 5000 non-skin average training block HS values. An average HS vector x is classified as skin if Pskin ( x) θ, (3) P ( x) non skin where θ is the threshold. The correct classification rate for the 00,000 pixels with θ = was 99.85%. Segmentation was also performed on non-overlapping blocks. The ROC curves for MGC training and test images are shown in Figs. 8 and 9, respectively. These results indicated that using the same HS values as feature vectors, the classification rates of the 00,000 training pixels for all the classifiers are about the same. However, the SONFIN classifier achieved a better segmentation performance for the 500 test images. This is verified from Figs. 5, 6, 8, and 9, where the area under the SONFIN ROC curve is larger than the other classifiers. Furthermore, using the proposed histogram feature vector further improves SONFIN classifier performance. SONFIN classifier learning is based on a neural learning method which minimizes training errors. This is in contrast to the compared two statistical learning methods, HSC and MGC. The SONFIN classifier achieves good performance with only 0 5 training pixels because of its powerful learning abilities. Statistical learning methods typically require a huge amount of training date to achieve a good performance. For HSC to achieve a good performance, a huge amount of training data is required, as shown in [20]. This requires a lot of effort. Compared with HSC, an advantage of MGC is that it can be made to generalize well on a smaller amount of training data. However, results show that

14 750 SONFIN s neural training algorithm achieves better results than the EM learning algorithm when using the same small training data set. That is, the EM algorithm requires a larger training data set than that used in the experiment to perform well. Of course, this requires more training data collection and more time for model training. The PCT method uses no training model. Segmentation is simply based on skin pixel distribution regions in principal eigenspace. Thus, PCT achieves the worst performance. 5. CONCLUSIONS This paper proposes skin color image segmentation by histogram-based SONFIN classifier. Color information is represented by a new proposed feature, the histogram of a block image, which shows to achieve a better performance than using the HS values. To represent a color by histogram as accurately as possible, several irregular space partition approaches on the HS plane are tested. The use of two-dimensional HS space instead of three-dimensional color space reduces the histogram bins on color space, which favors reduction of SONFIN input-dimension. For classifier performance, the neural-leaning based SONFIN classifier is shown to be better than the two widely used statistical learning classifiers, the histogram-based classifier and mixture of Gaussian classifier. In application, the proposed method will be applied to human face tracking and detection in the future. REFERENCES. M. M. Chang, A. M. Tekalp, and M. I. Sezan, Simultaneous motion estimation and segmentation, IEEE Transactions on Image Process, Vol. 6, 997, pp C. C. Chu and J. K Aggarwal, The integration of image segmentation maps using region and edge information, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 5, 993, pp D. Chai and K. N. Ngan, Face segmentation using skin-color map in videophone applications, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 9, 999, pp C. Garcia and C. Georgios, Face detection using quantized skin color regions merging and wavelet packet analysis, IEEE Transactions on Multimedia, Vol., 999, pp G. A. Hance, S. E. Umbaugh, R. H. Moss, and W. V. Stoecker, Unsupervised color image segmentation: with application to skin tumor borders, IEEE Engineering in Medicine and Biology Magazine, Vol. 5, 996, pp H. D. Cheng and Y. Sun, A hierarchical approach to color image segmentation using homogeneity, IEEE Transactions on Image Processing, Vol. 9, 2000, pp N. Pal and S. Pal, A review on image segmentation techniques, Pattern Recognition, Vol. 26, 993, pp A. D. Bimbo, Visual Information Retrieval, Chap. 2, Morgan Kaufmann, H. D. Cheng, X. H. Jiang, Y. Sun, and J. Wang, Color image segmentation: ad-

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16 H. L. van Trees, Detection, Estimation, and Modulation Theory, Wiley, New York, R. C. Gonzalez and P. A. Wintz, Digital Image Processing, Addison-Wesley, Reading, MA, R. Redner and H. Walker, Mixture densities, maximum likelihood, and the EM algorithm, SIAM Review, Vol. 26, 994, pp Chia-Feng Juang ( ) received the B.S. and Ph.D. degrees in Control Engineering from the National Chiao Tung University, Hsinchu, Taiwan, R.O.C., in 993 and 997, respectively. From 997 to 999, he was in military service. From 999 to 200, he was a Professor of the Department of Electrical Engineering at the Chung Chou Institute of Technology. In 200, he joined the National Chung Hsing University, Taichung, Taiwan, R.O.C., where he is currently an Associate Professor of Electrical Engineering. His current research interests are computational intelligence, intelligent control, computer vision, speech signal processing, and FPGA chip design. Hwai-Sheng Perng ( ) received the B.S. degree in Electrical Engineering from the National Chung Hsing University, Taichung, Taiwan, R.O.C., in He is currently in military service. His current research interests are image processing and neural fuzzy networks. Shih-Hsuan Chiu ( ) received the B.S. degree in Electrical Engineering from the National Chung Hsing University, Taichung, Taiwan, R.O.C., in In 2005, he joined the Realtek Semiconductor Corp., Hsinchu, Taiwan, where he is currently a R&D engineer. His current research interests are image processing, neural fuzzy networks, and support vector machines.