International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
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  1. 1. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 5, May (2014), pp. 64-79 © IAEME 64 COLOR-SEGMENTATION OF FABRIC IMAGES Dr. Ashwin Patani Indus Institute of Technology & Engineering, Ahmadabad, Gujarat ABSTRACT Aims at designing an application (web based real time) for processing client’s input/uploaded images of fabrics. After processing, the separated components of the image could be used as the screen template in block screen printing .Thus; the application can automate the process of taking images from the client and printing with the help of screen printing mechanism. Here, the term processing involves noise removal from the uploaded images and making it suitable for further stages by identifying and applying appropriate file format transformations. Later stages involve development and implementation of the color segmentation, boundary definition, and color editing and pattern recognition algorithms for scanned or software edited fabric images. Finally the code optimization stage will be followed by a server side deployment of the application and analyzing its sustainability as for image processing algorithms and applications it’s hard to manage the time bound response within given bandwidth constraints. Development of this application will facilitate the process of segregating the original image uploaded by the client into various simpler constituting images on the basis of different colors and repeat patterns present in the image. These individual images could then be fed into the blocks of the screen printers which are used for printing the actual fabrics. Thus, a major part of the process could be automated leading to minimized human interaction and a much improved response time for the end client [1, 2]. 1. INTRODUCTION Printed fabrics are high value-added textiles that have richer colors and more variations than other kinds of textiles. Color patterns on printed fabrics are created by printing patterns repeatedly with different color plates and through color register. In manufacturing or analyzing fabrics, how to separate color and identify the repeat patterns are the most important. In conventional printing processes, color separation, identification of repeat patterns, painting of black pictures, and plate making are all conducted manually requiring large amounts of labor. Currently, the computer-aided separation system used for fabrics is to conduct a color reduction on color images of fabrics, and the INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET) ISSN 0976 – 6464(Print) ISSN 0976 – 6472(Online) Volume 5, Issue 5, May (2014), pp. 64-79 © IAEME: www.iaeme.com/ijecet.asp Journal Impact Factor (2014): 7.2836 (Calculated by GISI) www.jifactor.com IJECET © I A E M E
  2. 2. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 5, May (2014), pp. 64-79 © IAEME 65 color correction is carried manually out on the computer; that is, the color and pattern correction is managed on incorrect pixels in the images and the black pictures used for printing are generated according to color categories (1, 2). Although this computer-aided separation system saves time over conventional manual operations, it can only provide this advantage of the supplementary separation, and it Cannot achieve completely automation since it is not able to identify repeat patterns. Thus, a computer visual system for printed fabrics would be a substantial aid to this industry. This project aims at designing an application (web based real time) for processing client’s input/uploaded images of fabrics. After processing, the separated components of the image could be used as the screen template in block screen printing .Thus; the application can automate the process of taking images from the client and printing with the help of screen printing mechanism. Here, the term processing involves noise removal from the uploaded images and making it suitable for further stages by identifying and applying appropriate file format transformations. Later stages involve development and implementation of the color segmentation, boundary definition, and color editing and pattern recognition algorithms for scanned or software edited fabric images. Finally the code optimization stage will be followed by a server side deployment of the application and analyzing its sustainability as for image processing algorithms and applications it’s hard to manage the time bound response within given bandwidth constraints. Other tools require computer-based skills to operate. For a person without proper image editing skills they are hard to operate. This tool will provide the comfort. No specialized/dedicated toolbox for fabric image handling is present. Functions like Auto Repeat recognition is not present which is frequently required in case of fabric images. Interfacing with rotary printer is not possible with already present tools. Here in figure 1 sequence diagram is shown which tells us that how it will proceed [3]. Stage 1 of the project involves reading of all kind of image formats. This step is categorized as a different module because detailed study of formats is required before developing the algorithms which will be used to process all kind of images. Also, the optimization phase will be highly focusing on this aspect of the whole application. Next 4 stages namely Image correction and noise removal, Color segmentation, Boundary definition and color editing are standard image processing activities but require a specialized approach in terms of fabric image processing and hence algorithms are to be designed and implemented for these stages which is the maximum time and efforts consuming part of the whole project completion duration. Last two stages GUI development and Server Integration are to be approached in parallel. Deployment of the application on server is a complicated task as it will present real time constraints. For developing and implementing the image processing algorithms MATLAB 2009 is to be used as a standalone application whereas MATLAB server 200x will be used for server side deployment of the application along with the web server capable of processing JSP/Servlets (planned as Apache- Tomcat). 2. IMAGE FILE FORMATS Image file formats are standardized means of organizing and storing digital images. Image files are composed of either pixel or vector (geometric) data that are raster zed to pixels when displayed (with few exceptions) in a vector graphic display. The pixels that constitute an image are ordered as a grid (columns and rows); each pixel consists of numbers representing magnitudes of brightness and color. Including proprietary types, there are hundreds of image file types. The PNG, JPEG, and GIF formats are most often used to display images on the Internet. These graphic formats are listed and briery described below, separated into the two main families of graphics: raster and vector. . In addition to straight image formats, Metafile formats are portable formats which can include both raster and vector information. Examples are application-independent formats such as
  3. 3. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 5, May (2014), pp. 64-79 © IAEME 66 WMF and EMF. The metafile format is an intermediate format. Most Windows applications open metafiles and then save them in their own native format. Page description language refers to formats used to describe the layout of a printed page containing text, objects and images. Examples are PostScript, PDF and PCL. Figure 1: Sequence Diagram 3. COLOR SEGREGATION There are basically two steps in color segmentation. 1). find the no. of color in image 2). color segmentation 1). finds the no. of color in image In order to start our process to segment the images we initially need develop an algorithm to find no. of colors in an image so that user can decide how much color he want to be segmented according to his application. Figure-2. 2). color segmentation This module aims at segmenting the input image in various constituting images where each image consists of single color pixels from the original image.
  4. 4. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 5, May (2014), pp. 64-79 © IAEME 67 3.2.1). RGB model based segmentation Input: Image, Number of colors, processed color values Output: Segmented images In this algorithm the image is scanned pixel by pixel. Color value of the pixel under Consideration is subtracted from the processed color values obtained and its minimum is found. According to the color value index the pixel is put into the image array element where each element is an image with a particular color [4, 5]. These are basic steps in this algorithm. Figure-3 Figure-2: Finds the no. of color in image
  5. 5. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 5, May (2014), pp. 64-79 © IAEME 68 Loop through the whole image performing following actions: • For every pixel take the difference with all color values obtained from find color algorithm. • Find the minimum of the difference and decide the color value index. • According to the index, copy the pixel location color values to the segmented image. • Return the segmented images. Initial results after the implementation of this algorithm were precise but in few cases presence of noise in the images may disrupt the output considerably. Hence a better algorithm was sought. Figure-3: Color Segmentation
  6. 6. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 5, May (2014), pp. 64-79 © IAEME 69 3.2.2). K-means clustering algorithm K-means (MacQueen, 1967) is one of the simplest unsupervised learning algorithms that solve the well known clustering problem. The procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k-clusters) fixed a priori. The main idea is to define k centroids, one for each cluster [6]. These centroids should be placed in a cunning way because of different location causes different result. So, the better choice is to place them as much as possible far away from each other. The next step is to take each point belonging to a given data set and associate it to the nearest centroid. When no point is pending, the first step is completed and an early group age is done. At this point we need to re-calculate k New centroids as bary-centers of the clusters resulting from the previous step. After we have these k new centroids, a new binding has to be done between the same data set points and the nearest new centroid. A loop has been generated. As a result of this loop we may notice that the k centroids change their location step by step until no more changes are done. In other words centroids do not move any more. Finally, this algorithm aims at minimizing an objective function, in this case a squared error function. The algorithm is composed of the following steps: • Place k points into the space represented by the objects that are being clustered. These points represent initial group centroids. • Assign each object to the group that has the closest centroid. • When all objects have been assigned, recalculate the positions of the k centroids. • Repeat Steps 2 and 3 until the centroids no longer move. This produces a separation of the objects into groups from which the metric to be minimized can be calculated. Both the algorithms are successfully implemented and it was found that k-means clustering algorithm produced more reliable and accurate results as compared to the RGB-model based algorithm and hence is chosen to be included as the part of the application. Figure 4.1: Original image Figure 4.2: segmented image 1
  7. 7. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 5, May (2014), pp. 64-79 © IAEME 70 Figure 4.3: segmented image 2 Figure 4.4: segmented image 3 Figure 4.5: segmented image 4 Figure 4.6: segmented image 5 Figure 4.7: segmented image 6
  8. 8. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 5, May (2014), pp. 64-79 © IAEME 71 4. OVERLAPPING Needs of Overlapping While printing patterns on the fabric it may so happen that the vibrations in the mechanical rollers may leave the portions on the boundaries which may lead to unwanted left-outs in between the patterns. For example if there is an image which has two boundaries of red and yellow color which is used to print by rotary screen printing but due to some mechanical vibrations red color roller doesn’t print exactly at its boundary and suppose it prints 3-4 pixel less, That’s why there is gap occur between red and yellow color boundaries and it appears as a white line which was not In original image. Hence overlapping is a common procedure used in the textile industry. Here the designs are extended at the boundaries as a play-safe strategy to counter The left-outs by the rollers. In overlapping we take some margin and we extend the boundaries of an image by 3-4 pixels in each direction and if there is error occur due. To mechanical vibrations then it can be overcome [10, 11]. There are two approaches which we followed in overlapping: 1) Morphological dilation 2) Boundary extensions - the customized approach 4.1). Morphological dilation Overlapping means we are extending the pixels at the boundaries so morphological dilation can be used for overlapping. Morphology is a broad set of image processing Operations that process images based on shapes. Morphological operations apply a structuring element to an input image, creating an output image of the same size. In a morphological operation, the value of each pixel in the output image is based on a comparison of the corresponding pixel in the input image with its neighbors. By choosing the size and shape of the neighborhood, you can construct a morphological operation that is sensitive to specific shapes in the input image. The most basic morphological operations are dilation. Dilation adds pixels to the boundaries of objects in an image. The number of pixels added from the objects in an image depends on the size and shape of the structuring element used to process the image. In the morphological dilation operations, the state of any given pixel in the output image is determined by applying a rule to the corresponding pixel and its neighbors in the input image. The rule used to process the pixels defines the operation as dilation. The value of the output pixel is the maximum value of all the Pixels in the input pixel’s neighborhood. In a binary image, if any of the pixels is set to the value 1, the output pixel is set to 1. Drawbacks of this approach As we discussed above that in dilation we need a structuring element as an input which is used to dilate an input image. But it is difficult to select a structuring element every time because there are some common structuring element like Rectangle, square which can be used for simple images but if image is complex then it is very difficult to select structuring element. So we need another approach which is customized approach where there is no need to select structuring element every time.
  9. 9. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 5, May (2014), pp. 64-79 © IAEME 72 Figure 5.1: Flow graph for Overlapping 4.2) Boundary extensions This algorithm provides the desired results. In this algorithm we take input as a segmented image which has only two colors, one is background and other is segmented color. Other input is pixel extension value by which boundaries of image will be extended. There are following steps which are used in boundary extension process. • Initially we take the size of image and scan through the whole image to save the co-ordinates of pixels where color is found (store in say CXi and CYi arrays) • Run a loop from 1 to Pixel extension value. Use the CXi and CYi arrays and check for background color in four neighborhoods d of the pixels If back ground pixel is found in any of these directions we assign a constant value to the pixels (say rgb (i,i,i) ) Save the co- ordinates where above changes are made and update in CXi and CYi • Loop again to step 2 • At last we scan whole image and wherever color is found that pixel is given initial color of object which was saved during first scanning. • In this way boundary of any given object can be extended according to its shape and in the normal direction of each pixel.
  10. 10. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 5, May (2014), pp. 64-79 © IAEME 73 Figure 5.2: Input image Figure 5.3: Extension neighborhood by 1 pixels Figure 5.4: Extension neighborhood by 2 pixels Figure 5.5: Extension neighborhood by 3 pixels 5. AUTO REPEAT MODULE Repeat is a common property inherent to the fabric images. Client uploaded images may be scanned or clicked images containing a repeated pattern on the cloth. While feeding images to rotary screens only single repeat is printed on the template and it’s successively printed on the cloth. If we find repeat in an image then it will increased the speed of printing through rotary screen printing because that repeat pattern will repeated on an image and it will give the original image. This is an exclusive feature of the project as it is not available in the image editing software already in the market. For finding the repeat in an image we applied Replicate and correlation based approach. Figure 6.
  11. 11. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 5, May (2014), pp. 64-79 © IAEME 74 Figure 6: Flow graph For Auto-Repeat Issues with this approach There are some issues with this approach. We get a square which will contain repeat. But in few cases repeat could not be included in square rather it requires to be a rectangle to cover it. We take an example. Here one image is shown in figure 7.
  12. 12. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 5, May (2014), pp. 64-79 © IAEME 75 If we find repeat on this image then it will find repeat as shown below in figure 7.2.But if we see carefully then we can find that it is not a original repeat because if we replicate it in all the corner of image then we should get original image but here some part will cut and we could not find the original repeat which is shown in figure 7.3.To overcome this problem we approached modified level based repeat finding. Figure 7.1: Input image Figure 7.2: pseudo Repeated pattern in an image Figure 7.3: original repeated pattern in an image Modified level based repeat finding In this approach we will find the repeat in two levels. In level 1 we find repeat with length of the repeat window fixed to image length. Now level 2 is optional. If client Wishes to use more refined repeat, he/she selects for level-2 repeat which is the same as the original Replicate and correlation based algorithm except the input is taken as the output from Level 1. we take one example where repeated pattern from level 2 is useful rather than level 1.here input image is shown in figure 8.
  13. 13. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 5, May (2014), pp. 64-79 © IAEME 76 Figure 8.1: Input image Figure 8.2: Repeated pattern after level 1 Figure 8.3: Repeated pattern after level 2 6. GUI DEVELOPMENT A graphical user interface (GUI) is a graphical display in one or more windows containing controls, called components that enable a user to perform interactive tasks. The user of the GUI does not have to create a script or type commands at the command line to accomplish the tasks. Unlike coding programs to accomplish tasks, the user of a GUI need not understand the details of how the tasks are performed. The GUI components can be menus, toolbars, push buttons, radio buttons, list boxes, and sliders just to name a few. GUIs created in MATLAB software can group related components together, read and write data files, and display data as tables or as plots. 7. MISCELLANEOUS FEATURES Merging After color segmentation we find several images which have different color. But some time it happens that two colors suppose light blue and dark blue which we get after Segmentation and user wants to combine these colors into a single color which is darker. It can be done by merging operation. There are following steps in this approach [11]. • Take source segmented image, find the color in it. • Take Target segmented image; replace color value of pixels obtained in step1 in the Destination image. • Add both the images.
  14. 14. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 5, May (2014), pp. 64-79 © IAEME 77 By helping of merging no. of segmented color can be reduced. Here one example is shown. We take source image (figure 9.1) and destination image (figure 9.2).After merging we get an image which is combination of both the image and we replaced target image by source image. Figure 9.1: Source image Figure 9.2: Target & Merged image Priority based Reading Image Priority Reading code is basically aimed to arrange the image in the order they received after receiving from user and consists of following steps: 1) This is the first step of the Image Processing task, Where Unprocessed image is received from end user by emailing or uploading on given web address of server. Those received unprocessed images are dumped in a folder of drive d:/input After getting dumped in the destined folder, Image information like Image Name, image copied instant in terms of date and time, is extracted and saved in two different array named ’Stack Name’ and Stack DN 2) Second Part comprises the Sorting of Images based on their copied instant In Mat-lab, from given image and its properties, it is not possible to get and process the Date and Time detail of any image from its properties which is in any of the format DD/MM/YYYY, MM/DD/YY, DD-MMM-YY and so on. Instead, Mat-lab wants the Date and Time information in the format given below ’YYYY MM DD HH MI SS’ YYYY = gives the year information from image properties MM = enable the month detail during which it is copied to destined folder i.e. d:/input’ DD = indicates the Date information of copied image And HH = Hours, MI = Minutes, SS = Seconds respectively. And For any given image, its date and time information is stored in variable whose name designation is given according to above decomposition of date and time like ’YYYY’ to store year information, MM = for Month and so on. There are two reasons to get the image date and time information in decomposed form, which are as under: • The decomposed information of date and time facilitates us to process the same in MATLAB. In other words, to process Date and Time, MATLAB require that detail like Date, Month, Year, Hour, Minute and Seconds information separately. • The most significant benefit of doing so is to compare or sort the image based on their date and time to arrange them in the order they received is quite cumbersome which requires to compare Date and time in the order of Year, Month, Date ,Hour, Minute and Second with Another Image’s same piece of information. Rather, we adopt an approach which gives out an equivalent unique number (through Mat-lab function ’datenum’) for any image’s date and time. Thus it becomes quite handy to compare any two images copied instant in number rather than a tedious spate of comparison of its decomposed components which consumes more time.
  15. 15. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 5, May (2014), pp. 64-79 © IAEME 78 3) In final step, after sorting of images, they are moved to another folder and deleted from the folder where they placed initially. That Means, at any instant Mat-lab Server, Checks images in particular folder and moved them in another folder after sorting [3]. 8. CONCLUSION We have developed this software for automation in textile industry. In this software we have added various modules like color segmentation, Auto Repeat, overlapping, merging, priority based reading etc. It facilitates image segmentation as per user requirement i.e. User will be prompted for number of colors input and generate that much number of segmented images from given image. Auto repeat algorithm find pattern in images. But In auto repeat algorithm it has a little bit problems when image is complex where finding pattern is not easy. In auto repeat algorithm we have provided option for user to find minimum repeat or not by dividing the algorithm into two levels. In addition to it Overlapping algorithm is used to overcome the problem of mechanical vibration of rollers. Merging function is used if user wants to reduce the number of colors in an image by merging two images into single image. Finally we interfaced these algorithms into GUI so that it can be used by user very easily. It does not require more technical knowledge to user which is needed in various images editing software like Adobe Photoshop. 9. REFERENCES [1] Kearney, Colm and Patton, J. Andrew, “Survey on the image segmentation”, Financial Review, 41: 29-48 (2000). [2] Shankar Rao, Hossein Mobahi, Allen Yang, Shankar Sastry and Yi Ma Natural Image Segmentation with Adaptive Texture and Boundary Encoding, Proceedings of the Asian Conference on Computer Vision (ECCV) 2009, H. Zha, R.-i. Taniguchi, and S. Maybank (Eds.), Part I, LNCS 5994, pp. 135--146, Springer. [3] Digital Image Processing Using MATLAB Author(s): Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins Publisher: Prentice Hall; 1st edition: 2003, Pages-782, ISBN-10: 0130085197 ISBN-13:978-0130085191. [4] L. Yang, F. Albregtsen, T. Lonnestad, and P. Grottum, “A supervised approach to the evaluation of image segmentation methods,” in Computer Analysis of Images and Patterns, 1995, pp. 759C765. [5] W. Yasno and J. Bacus, “Scene segmentation algorithm development using error measures,” AOCH, vol. 6, pp.45C58, 1984. [6] An Efficient k-Means Clustering Algorithm: Analysis and Implementation Tapas Kanungo, Senior Member, IEEE, David M. Mount, Member, IEEE, Nathan S. Netanyahu, Member, IEEE, Christine D. Piatko, Ruth Silverman, and Angela Y. Wu, Senior Member, IEEE transactions on pattern analysis and machine intelligence, VOL. 24, NO. 7, JULY 2002. [7] Erosion, dilation and related operators Mariusz Jankowski Department of Electrical Engineering University of Southern Maine Portland, Maine, USA, 8th International Mathematica Symposium Avignon, June 2006. [8] Understanding boundary extension: normalization and extension errors in picture memory among adults and boys with and without a sperger’s syndrome peter chapman, Danielle ropar, peter Mitchell and Katie ackroyd. School of psychology, university of Nottingham,UK, visual cognition, 2005,12(7), 1265-1290, Psychology press. [9] C. E. Erdem, B. Sanker, and A. M. Tekalp, “Performance measures for video object segmentation and tracking,” IEEE Trans. on Image Processing, vol.13, pp. 937C951, 2004.
  16. 16. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 5, May (2014), pp. 64-79 © IAEME 79 [10] Jung, C., Kim, C., Chae, S., Oh, S.: Unsupervised segmentation of overlapped nuclei using Bayesian classification. IEEE TBE, 57(12), (2010) 2825-2832. [11] Kale, A., Aksoy, S.: Segmentation of cervical cell images. ICPR (2010). [12] Li, C., Xu, C., Gui, C., Fox, M.: Distance regularized level set evolution and its application to image segmentation. IEEE TIP, 19(12) (2010) 3243-3254. [13] Kobe University (Japan) Faculty of Engineering, Department of Computer & Systems Engineering, Kitamura Lab Development of a Segmentation Method for Dermoscopic Images Based on Color Clustering Dissertation by Harald Galda-2003. [14] Ankit Vidyarthi and Ankita Kansal, “A Survey Report on Digital Images Segmentation Algorithms”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 2, 2012, pp. 85 - 91, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [15] Amandeep Singh and A.P Gursimran Singh Sandhu, “Comparative Performance Analysis of Segmentation Techniques”, International Journal of Electronics and Communication Engineering & Technology (IJECET), Volume 3, Issue 2, 2012, pp. 238 - 247, ISSN Print: 0976- 6464, ISSN Online: 0976 –6472. [16] J.Rajarajan and Dr.G.Kalivarathan, “Influence of Local Segmentation in the Context of Digital Image Processing – A Feasibility Study”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 3, 2012, pp. 340 - 347, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [17] Gunwanti S. Mahajan and Kanchan S. Bhagat, “Medical Image Segmentation using Enhanced K-Means and Kernelized Fuzzy C- Means”, International Journal of Electronics and Communication Engineering & Technology (IJECET), Volume 4, Issue 6, 2013, pp. 62 - 70, ISSN Print: 0976- 6464, ISSN Online: 0976 –6472. [18] Gaganpreet Kaur and Dr. Dheerendra Singh, “Pollination Based Optimization for Color Image Segmentation”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 2, 2012, pp. 407 - 414, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.

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