Please use this identifier to cite or link to this item:
A Study on Image Segmentation Techniques Using Region Adjacency Property
|關鍵字:||image segmentation;影像切割;region merging;region adjacency graph;minimum spanning tree;區域合併;區域相鄰圖;最小成本擴張樹||出版社:||資訊管理學系所||引用:||References  C. R. Jung, “Unsupervised Multiscale Segmentation of Color Images,” Pattern Recognition Letters, Vol. 28, Issue 4, 2007, pp. 523-533.  T. N. Janakiraman and P. V. S. S. R. Chandra Mouli, “Image Segmentation Using Euler Graphs,” International Journal of Computers Communications & Control, Vol. 5, Issue 3, 2010, pp. 314-324.  K. Hammouche, M. Diaf, and P. Siarry, “A Multilevel Automatic Thresholding Method Based on Genetic Algorithm for a Fast Image Segmentation,” Computer Vision and Image Understanding, Vol. 109, 2008, pp.163-175.  Y. H. Guo and H. D. Cheng, “New Neutrosophic Approach to Image Segmentation,” Pattern Recognition, Vol. 42, 2009, pp. 587-595.  M. M. Mushrif and A. K. Ray, “A-IFS Histon Based Multithresholding Algorithm for Color Image Segmentation,” IEEE Signal Processing Letters, Vol. 16, 2009, pp. 168-171.  M. M. Mushrif and A. K. Ray, “Color Image Segmentation: Rough-set Theoretic Approach,” Pattern Recognition Letters, Vol. 29, Issue 4, 2008, pp. 483-493.  L. Garcia Ugarriza, E. Saber, S. R. Vantaram, V. Amuso, M. Shaw, and R. Bhaskar, “Automatic Image Segmentation by Dynamic Region Growth and Multiresolution Merging,” IEEE Transactions on Image Processing, Vol. 18, Issue 10, 2009, pp. 2275-2288.  Y. S. Pan, J. Douglas Birdwell, and S. M. Djouadi, “Preferential Image Segmentation Using Trees of Shapes,” IEEE Transactions on Image Processing, Vol. 18, Issue 4, 2009, pp. 854-866.  W. Tao and H. Jin, “Unified Mean Shift Segmentation and Graph Region Merging Algorithm for Infrared Ship Target Segmentation,” Optical Engineering, Vol. 46, Issue 12, 2007, pp. 127002.  Z. G. Tan and N. H. C. Yung, “Merging Toward Natural Clusters,” Optical Engineering, Vol. 48, Issue 7, 2009, pp. 077202.  A. V. Wangenheim, R. F. Bertoldi, D. D. Abdala, and M. M. Richter, “Color Image Segmentation Guided by a Color Gradient Network,” Pattern Recognition Letters, Vol. 28, Issue 13, 2007, pp. 1795-1803.  A. V. Wangenheim, R. F. Bertoldi, D. D. Abdala, A. Sobieranski, L. Coser, X. Jiang, M. M. Richter, L. Priese, and F. Schmitt, “Color Image Segmentation Using an Enhanced Gradient Network Method,” Pattern Recognition Letters, Vol. 30, Issue 15, 2009, pp. 1404-1412.  Z. Wang, J. R. Jensen, and J. Im, “An Automatic Region-based Image Segmentation Algorithm for Remote Sensing Applications,” Environmental Modelling and Software, Vol. 25, Issue 10, 2010, pp. 1149-1165.  R. C. Frohn, K. M. Hinkel, and W. R. Eisner, “Satellite Remote Sensing Classification of Thaw Lakes and Drained Thaw Lake Basins on the North Slope of Alaska,” Remote Sensing of Environment, Vol. 97, 2005, pp. 116-126.  J. R. Jensen, M. Garcia-Quijano, B. Hadley, J. Im, and Z. Wang, “Remote Sensing Agricultural Crop Type for Sustainable Development in South Africa,” Geocarto International, Vol. 21, Issue 2, 2006, pp. 5-18.  Y. Deng and B. S. Manjunath, “Unsupervised Segmentation of Color-texture Regions in Images and Video,” IEEE Transactions on Analysis and Machine Intelligence, Vol. 23, Issue 8, 2001, pp. 800-810.  S. Z. Sarker, W. H. Tan, and R. Logeswaran, “Development of a Morphological Technique for Segmentation of Anatomical Objects in Abdominal MRI,” Imaging Science Journal, Vol. 56, Issue 25, 2008, pp. 243-253.  A. Mukhopadhyay and U. Maulik, “A Multiobjective Approach to MR Brain Image Segmentation,” Applied Soft Computing, Vol. 11, Issue 1, 2011, pp. 872-880.  Y. Hata and S. Kobashi, “Fuzzy Segmentation of Endorrhachis in MRI,” Applied Soft Computing, Vol. 9, 2009, pp. 1156-1169.  W. F. Kuo, C. Y. Lin, and Y. N. Suna, “Brain MR Images Segmentation Using Statistical Ratio: Mapping between Watershed and Competitive Hopfield Clustering Network Algorithms,” Computer Methods and Programs in Biomedicine, Vol. 91, Issue 3, 2008, pp. 191-198.  Y. L. Chang and X. Li, “Adaptive Image Region-growing,” IEEE Transactions on Image Processing, Vol. 3, Issue 11, 1994, pp. 868-872.  S. Y. Wan and W. E. Higgins, “Symmetric Region Growing,” IEEE Transactions on Image Processing, Vol. 12, Issue 9, 2003, pp. 1007-1015.  J. Canny, “A Computational Approach to Edge Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 8, Issue 6, 1986, pp. 679-698.  J. M. Gauch, “Image Segmentation and Analysis via Multiscale Gradient Watershed Hierarchies,” IEEE Transactions on Image Processing, Vol. 8, Issue 1, 1999, pp. 69-79.  W. Y. Ma and B. S. Manjunath, “Edge Flow: A Technique for Boundary Detection and Image Segmentation,” IEEE Transactions on Image Processing, Vol. 9, Issue 8, 2000, pp. 1375-7388.  K. Haris, S. N. Efstratiadis, N. Maglaveras, and A. K. Katsaggelos, “Hybrid Image Segmentation Using Watersheds and Fast Region Merging,” IEEE Transactions on Image Processing, Vol. 7, Issue 12, 1998, pp. 1684-1699.  A. Tremeau and P. Colantoni, “Regions Adjacency Graph Applied to Color Image Segmentation,” IEEE Transactions on Image Processing, Vol. 9, Issue 4, 2000, pp. 735-744.  A. Tremeau and N. Borel, “A Region Growing and Merging Algorithm to Color Segmentation,” Pattern Recognition, Vol. 30, Issue 7, 1997, pp. 1191-1203.  S. Y. Wan and W. E. Higgins, “Symmetric Region Growing,” IEEE Transactions on Image Processing, Vol. 12, Issue 9, 2003, pp. 1007-1015.  O. Lézoray and C. Charrier, “Color Image Segmentation Using Morphological Clustering and Fusion with Automatic Scale Selection,” Pattern Recognition Letters, Vol. 30, Issue 4, 2009, pp. 397-406.  Y. H. Kuan, C. M. Kuo, and N. C. Yang, “Color-based Image Salient Region Segmentation Using Novel Region Merging Strategy,” IEEE Transactions on Multimedia, Vol. 10, Issue 5, 2008, pp. 832-845.  RGB-cube, http://imageprocessing.files.wordpress.com/2008/03/rgb-cube.gif available on 2011/6/13  CIE L*a*b*-cube, http://www.sform.biz/ColorTxt/LabSys.jpg available on 2011/6/13  EDISON Software, http://coewww.rutgers.edu/riul/research/code/EDISON/ available on 2011/6/13  D. Comanicu and P. Meer, “Mean Shift: A Robust Approach toward Feature Space Analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, 2002, pp. 603-619.  P. Meer and B. Georgescu, “Edge Detection with Embedded Confidence,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, 2001, pp. 1351-1365.  Berkeley Image Database, http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/segbench/ available on 2011/6/13  R. C. Prim, “Shortest Connection Networks and Some Generalizations,” Bell System Technical Journal, Vol. 36, 1957, pp. 1389-1401.||摘要:||
Image segmentation is an important technique in image content understanding and pattern recognition. Nowadays, many of image segmentation techniques have been proposed, but none can segment all kinds of images well enough. Traditional image segmentation methods usually cause the results to be to be over-segmentation. For example, if the sky shows strong continuous variations of color, it is easily segmented into many regions. The purpose of this thesis aims to solve this problem.
Two region merging methods are proposed to improve the over-segmentation problem produced by traditional image segmentation algorithms. By applying region merging, the regions of over-segmentation are reduced gradually to export final segmentation. In the first method, over-segmentation structure is built as region adjacency graph. A special vertex selection algorithm is employed to choose the vertex with the smallest region size, thus there may exist two or more vertices that satisfy the condition. These vertices and their neighbors are being examined to determine their similarity. The vertices are merged at the end of each iteration if they are found similar. The verification method is to calculate inter-class difference and extra-class difference between two vertices in L*, a*, b* channels. If the inter-class difference of two vertices is smaller than the extra-class difference in all three channels, two regions should be merged. There may occurs regions merged at each iteration. If merging criterion is reached, the algorithm would find the iteration number which gives the best segmentation, and then reruns and stops at that iteration. Finally, the final segmentation is exported. In the second method, the algorithm sifts some smaller edges by using minimum spanning tree. Histograms of three color channels are divided into different parts, respectively; each part is labeled as a number, and each vertex has a set of color code formed by these label numbers. If the color codes of two regions satisfy certain conditions, two regions would merge at the end of iteration. The algorithm stops when the number of regions in the image does not reduce. Experimental results showed that the proposed methods can improve the problem produced by traditional algorithms.
|Appears in Collections:||資訊管理學系|
Show full item record
TAIR Related Article
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.