Region Segmentation and Object Extraction Based on Virtual Edge and Global Features

We have developed a robust statistical edge detection method by combining the ideas of Kundus method, in which the region segmentation of local area is used, and Fukuis method, in which a statistic evaluation value separability is used for edge extraction and also have developed a region segmentation method based on the global features like the statistics of the region. A new region segmentation method is developed by combining these two methods, in which the edge extraction method is used instead of the first step of region segmentation method. We obtained the almost same results as the ones of previous region segmentation method. The proposed one has some advantages because we are able to introduce a new conspicuity degree including a clear contrast value with the adjacent regions, a envelopment degree based on clear edge and so on without much difficulty and it will contribute to develop a further unification algorithm and a new feature extraction method for scene recognition.

[1]  Amlan Kundu.  Robust edge detection , 1990, Pattern Recognit..

[2]  Zujun Hou,et al.  Robust edge detection , 2003, Pattern Recognit..

[3]  Yoram Yakimovsky,et al.  Boundary and Object Detection in Real World Images , 1974, JACM.

[4]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[5]  Kazuhiro Fukui,et al.  Edge Extraction Method Based on Separability of Image Features , 1995, MVA.

[6]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  G. S. Robinson.  Edge detection by compass gradient masks , 1977 .

[8]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.

[9]  Andrew J. Davison,et al.  Active Matching , 2008, ECCV.

[10]  Jitendra Malik,et al.  A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[11]  Charless C. Fowlkes,et al.  Contour Detection and Hierarchical Image Segmentation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Irwin Sobel,et al.  An Isotropic 3×3 image gradient operator , 1990 .

[13]  Manfred H. Hueckel.  An Operator Which Locates Edges in Digitized Pictures , 1971, J. ACM.

[14]  Martial Hebert,et al.  Discriminative Sparse Image Models for Class-Specific Edge Detection and Image Interpretation , 2008, ECCV.

[15]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[16]  Jitendra Malik,et al.  Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Hiromitsu Yamada,et al.  Color Image Segmentation Based on Statistics of Location and Feature Similarity , 2011 .

[18]  Stephen M. Smith,et al.  SUSAN—A New Approach to Low Level Image Processing , 1997, International Journal of Computer Vision.