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A version of watershed algorithm for color image segmentation
 

A version of watershed algorithm for color image segmentation

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A Master's Thesis Defense and Presentation

A Master's Thesis Defense and Presentation

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    A version of watershed algorithm for color image segmentation A version of watershed algorithm for color image segmentation Presentation Transcript

    • A version of watershed algorithm for color image segmentation Md. Habibur Rahman (11-94853-2) Master’s Thesis Presentation and Defense Thesis Committee : American International University-Bangladesh June, 2013 1 Prof. Dr. Md. Rafiqul Islam (Advisor) Dr. Md. Saiful Azad (External) Dr. Dip Nandi (Head of Graduate Program)
    •  Problem Definition  Thesis Contributions  Introduction  Proposed Watershed Algorithm  Image Quality Assessment (IQA) Metrics  Results Analysis  Conclusions  List of publication  References Outline 2
    •  Over-segmentation problem in the existing watershed algorithm  Sensitive to noise  High computational complexity  Performance varies in different classes of images Problem Definition 3
    •  An adaptive masking and a thresholding mechanism over each color channel before combining the segmentation from each channel into the final one  Overcome over-segmentation problem  Computationally inexpensive  Perform well in case of noisy image  Perform better with respect to five IQA metrics in 20 different classes of images Thesis Contributions 4
    •  What is digital image?  Digital image processing  How image is stored?  Image Segmentation  Why Image Segmentation?  Color Image Segmentation Algorithms Introduction 5
    • What is a digital image? • A numeric representation of a two-dimensional image as a finite set of digital values • Pixel values usually represent intensity levels or gray levels, colors, heights, and opacities [11]. 611. R C Gonzalez and R E Woods, Digital Image Processing, 3rd Edition, Pearson, pp. 51
    •  An image can be defined as a two- dimensional function, p (x, y)  Where x and y are spatial (plane) coordinated  The amplitude of p at any pair of coordinates (x, y) is called the intensity or gray level of the image at that point Digital Image Processing 7
    • How image is stored? • In image, P (0, 0) represents the top left corner pixel • P (X−1, 0) represents the bottom left corner pixel of the image • In digital image, pixels contain color value and each pixel uses 8 bits or 1 Byte or 256 values [13] 813. H. Vankayalapati, "Evaluation of wavelet based linear subspace techniques for face recognition," Klagenfurt, 2008
    •  It is a process to divide the digital image into homogeneous and different meaningful regions  The main goal of image segmentation is to cluster of pixels in the relevant regions  It is used to recognize similar regions and grouping the similar visual objects  Property like grey level, color, intensity, texture, shape, depth or motion from the digital image Image Segmentation 9
    •  We do image segmentation to separate homogeneous area  It requires everywhere for precise segmentation if we want to analyze what inside the image.  It is separate objects and analyze each object individually to check what it is. Why Image Segmentation? 10
    •  Fuzzy C-Means (FCM) • Partition a finite collection of pixels into a collection of "C" fuzzy clusters [22]  Region Growing (RG) • Group of pixels with similar properties to form a region • For similarity measure, difference between a pixel's intensity value and the region's mean [23] Image Segmentation Algorithms 11 22. M. Singha and K. Hemachandran, "Color Image Segmentation for Satellite Images", IJCSE, vol. 3(12), 2011. 23. M. Edman, "Segmentation Using a Region Growing Algorithm," Rensselaer Polytechnic Institute, 2007.
    •  Hill Climbing with K-Means (HKM) • detects local maxima of clusters in global three- dimensional color histogram of an image [28]  Watershed (WS) • It comes from geography • It is that of a topographic relief which is flooded by water • Watershed lines being the divide lines of the domains of attraction of rain falling over the region [6] Image Segmentation Algorithms 12 28. R. Vijayanandh and G. Balakrishnan, "Hill climbing Segmentation with Fuzzy C-Means Based Human Skin Region Detection using Bayes Rule," EJSR, Vol. 76(1), pp. 95-107, 2012. 6. X. Han, Y. Fu and H. Zhang, "A Fast Two-Step Marker-Controlled Watershed Image Segmentation Method," Proceedings of ICMA, pp. 1375-1380, 2012
    • Proposed Watershed Algorithm • It can quickly calculate the every region of the watershed segmentation • Image normalization operation by Eq. 1 13
    •  Adaptive threshold determined by Eq. 2 and Eq. 3 based on Gray-threshold function  N-dimensional convolution for smoothing image  Adaptive masking operations by Eq. 4 and Eq. 5 Proposed Watershed Algorithm 14
    •  Impose Minima to create morphological process image using Nucleus-masking (M2) on three color channels  Apply Watershed algorithm (Wn) on three color channels  Pixel labeling calculated by Ln = BWLABEL (Wn) Proposed Watershed Algorithm 15
    •  Convert three channels into a RGB image for visualizing labeled regions by Pn = label2rgb (Ln)  R, G and B color channels (Pn) are added to generate segmented image Proposed Watershed Algorithm 16
    •  Applied canny edge detection method to detect enclosed region boundary and remove all small object from the combined three color channels  The enclosed region boundary is superimposed on original image in the final segmentation Proposed Watershed Algorithm 17
    •  Peak Signal to Noise Ratio (PSNR) is calculated between two images by Eq. 6 [40].  Mean Square Error (MSE) is calculated pixel- by-pixel by adding up the squared difference of all the pixels and dividing by the total pixel count using the Eq. 7 [40].  Image Quality Measure (CQM) is based on color transformation from RGB to YUV. Quality Evaluation Metrics 1840. C. Mythili and V. Kavitha, "Color Image Segmentation using ERKFCM," IJCA, Vol. 41(20), pp. 21-28, 2012
    •  Reversible YUV Color Transformation (RCT) that is created from the JPEG2000 standard in Eq. 8  PSNR of each YUV color channel (Y, U and V) is calculated separately  CQM value is calculated using the Eq. 9 [43].  Riesz-transform based Feature Similarity Metric (RFSIM) is based on the human vision system (HVS) perceives an image mainly according to its low-level features Quality Evaluation Metrics 19 43. Y. YALMAN and Đ. ERTÜRK, "A new color image quality measure based on yuv transformation and psnr for human vision system,“ Turkish Journal of Electrical Engineering & Computer Sciences, 2013, in press.
    •  Compute the similarity between two images f and g  M1 and M2 is the result of edge detection performed on f and g  Then, the feature mask is defined as Eq. 10.  Similarity between two feature maps fi (i = 1~5) and gi at the corresponding location (x, y) is defined as the Hilbert transform of a 1-D function in Eq. 11. Quality Evaluation Metrics 20
    •  To define similarity between feature maps fi and gi by considering only key locations marked by mask M and Hilbert transform of a 2-D function by Eq. 12  RFSIM index computes between f and g image as Eq. 13 [42]  RFSIM range between [0, 1), the higher RFSIM value indicates better image quality Quality Evaluation Metrics 21 42. L. Zhang, L. Zhang and X. Mou, "RFSIM: A Feature based image quality assessment metric using Riesz- Transforms," Image Processing (ICIP), 17th IEEE International Conference, pp. 321-324, 2010.
    •  Visual Verification • Comparative performance of the proposed MWS method with four modified watershed methods • Compared the results of the proposed algorithm with three image segmentation algorithms  Quantitative Verification • Color image segmentation results with 20 different classes of images • Performance of proposed method with three different algorithms with respect to 5 IQA metrics Results Analysis 22
    • 23 33. C. Zhang, S. Zhang, J. Wu, S. Han, "An improved watershed algorithm for color image segmentation,“ I CCSEE, pp. 69-72, 2012. 35. S. Li, J. Xu, J. Ren and T. Xu, "A Color Image Segmentation Algorithm by Integrating Watershed with Region Merging," RSKT, LNAI 7414, pp. 167–173, 2012.
    • 24 7. H. Tan, Z. Hou, X. Li, R. Liu and W. Guo, "Improved watershed algorithm for color image segmentation," Proc. of SPIE Vol. 7495 74952Z-(1-8). 9. L. Gao, S. Yang, J. Xia, S. Wang, J. Liang and Y. Qin, "New Marker-Based Watershed Algorithm," TENCON 2006.
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    •  A novel image segmentation method based on adaptive threshold and masking operation with watershed algorithm  Compared the proposed MWS algorithm with four modified watershed algorithms  The results achieved using my technique ensure accuracy and quality of the image in 20 different classes of images in four segmentation algorithms  Proposed method is less computational complexity, which makes it appropriate for real-time application  In future I am going to develop a robust algorithm for the segmentation of color and video images Conclusions 29
    • 1) "Segmentation of Color Image using Adaptive Thresholding and Masking with Watershed Algorithm," Presented at 2nd International Conference on Informatics, Electronics & Vision (ICIEV), Dhaka University, Bangladesh, ISBN: 978-1-4799-0399-3, May 2013 (To appear in IEEE Xplore). 2) "A version of watershed algorithm for color image segmentation," AIUB Journal of Science and Engineering (AJSE), Bangladesh, Vol. 12(1), 2013 (accepted). List of Publication related to this thesis 30
    • [6] X. Han, Y. Fu and H. Zhang, "A Fast Two-Step Marker-Controlled Watershed Image Segmentation Method," Proceedings of ICMA, pp. 1375-1380, 2012. [7] H. Tan, Z. Hou, X. Li, R. Liu and W. Guo, "Improved watershed algorithm for color image segmentation," Proc. of SPIE Vol. 7495 74952Z-(1-8). [9] L. Gao, S. Yang, J. Xia, S. Wang, J. Liang, and Y. Qin, "New Marker-Based Watershed Algorithm," TENCON 2006. [11] R. Gonzalez and R. Woods, “Digital Image Processing,” 3rd edition, Pearson Prentice Hall, 2007. [13] H. Vankayalapati, "Evaluation of wavelet based linear subspace techniques for face recognition," Alpen- Adria University and Institute for Smart System-Technologies, Klagenfurt, 2008. [22] M. Singha and K. Hemachandran, "Color Image Segmentation for Satellite Images", IJCSE, vol. 3(12), 2011. [23] M. Edman, "Segmentation Using a Region Growing Algorithm," Rensselaer Polytechnic Institute, 2007. [28] R. Vijayanandh and G. Balakrishnan, "Hill climbing Segmentation with Fuzzy C-Means Based Human Skin Region Detection using Bayes Rule," EJSR, Vol. 76(1), pp. 95-107, 2012. [33] C. Zhang, S. Zhang, J. Wu, S. Han, "An improved watershed algorithm for color image segmentation," International Conference on Computer Science and Electronics Engineering (ICCSEE), pp. 69-72, 2012. [35] S. Li, J. Xu, J. Ren, and T. Xu, "A Color Image Segmentation Algorithm by Integrating Watershed with Region Merging," RSKT, LNAI 7414, pp. 167–173, 2012. [40] C. Mythili and V. Kavitha, "Color Image Segmentation using ERKFCM," International Journal of Computer Applications (IJCA), Vol. 41(20), pp. 21-28, 2012. [42] L. Zhang, L. Zhang, and X. Mou, "RFSIM: A Feature based image quality assessment metric using Riesz- Transforms," Image Processing (ICIP), 17th IEEE International Conference, pp. 321-324, 2010. [43] Y. YALMAN and Đ. ERTÜRK, "A new color image quality measure based on yuv transformation and psnr for human vision system," Turkish Journal of Electrical Engineering & Computer Sciences, 2013, in press. Some Important References 31
    • Thank you  32