A new approach to image feature detection with applications

Image feature detection is a fundamental issue in many intermediate level vision problems such as stereo, motion correspondence, image registration and object recognition. In this paper we present an approach to feature detection based on a scale-interaction model. This feature detector is responsive to short lines, line endings, corners and other such sharp changes in curvature. We provide extensive experimental results to demonstrate its potential applications to several image analysis problems.

[1]  J. Daugman.  Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[2]  Marsha Jo Hannah,et al.  Bootstrap Stereo , 1980, AAAI.

[3]  Alex Pentland,et al.  View-based and modular eigenspaces for face recognition , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Thomas S. Huang,et al.  Image processing , 1971 .

[5]  R. Chellappa,et al.  Passive Navigation in a partially known environment , 1991, Proceedings of the IEEE Workshop on Visual Motion.

[6]  T. J. Stonham,et al.  Practical Face Recognition and Verification with Wisard , 1986 .

[7]  金出 武雄,et al.  Picture processing system by computer complex and recognition of human faces , 1974 .

[8]  A. Young,et al.  Aspects of face processing , 1986 .

[9]  Vicki Bruce,et al.  COMPUTER RECOGNITION OF FACES , 1989 .

[10]  S. Zucker,et al.  Endstopped neurons in the visual cortex as a substrate for calculating curvature , 1987, Nature.

[11]  Joachim M. Buhmann,et al.  Distortion Invariant Object Recognition in the Dynamic Link Architecture , 1993, IEEE Trans. Computers.

[12]  Michael Brady,et al.  The Curvature Primal Sketch , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Rama Chellappa,et al.  A computational vision approach to image registration , 1993, IEEE Trans. Image Process..

[14]  Ramakant Nevatia,et al.  Matching Images Using Linear Features , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Roland Sauerbrey,et al.  Biography , 1992, Ann. Pure Appl. Log..

[16]  Chuan Yi Tang,et al.  A 2.|E|-Bit Distributed Algorithm for the Directed Euler Trail Problem , 1993, Inf. Process. Lett..

[17]  Ashok Samal,et al.  Automatic recognition and analysis of human faces and facial expressions: a survey , 1992, Pattern Recognit..

[18]  Rama Chellappa,et al.  Estimation of illuminant direction, albedo, and shape from shading , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[19]  Yehoshua Y. Zeevi,et al.  The Generalized Gabor Scheme of Image Representation in Biological and Machine Vision , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  R. von der Heydt,et al.  Mechanisms of contour perception in monkey visual cortex. II. Contours bridging gaps , 1989, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[21]  Takeo Kanade,et al.  Picture Processing System by Computer Complex and Recognition of Human Faces , 1974 .

[22]  Hans P. Morevec.  Towards automatic visual obstacle avoidance , 1977, IJCAI 1977.

[23]  S. Zucker,et al.  Endstopping and curvature , 1989, Vision Research.

[24]  Igor Aleksander,et al.  Emergent intelligent properties of progressively structured pattern recognition nets , 1983, Pattern Recognit. Lett..

[25]  Rama Chellappa,et al.  A feature based approach to face recognition , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[26]  R. von der Heydt,et al.  Mechanisms of contour perception in monkey visual cortex. I. Lines of pattern discontinuity , 1989, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[27]  B. S. Manjunath,et al.  Balloon motion estimation using two frames , 1991, [1991] Conference Record of the Twenty-Fifth Asilomar Conference on Signals, Systems & Computers.

[28]  Wilson S. Geisler,et al.  Multichannel Texture Analysis Using Localized Spatial Filters , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  Hans P. Moravec.  Towards Automatic Visual Obstacle Avoidance , 1977, IJCAI.

[30]  Robert J. Baron,et al.  Mechanisms of Human Facial Recognition , 1981, Int. J. Man Mach. Stud..

[31]  Jun S. Huang,et al.  Human face profile recognition by computer , 1990, Pattern Recognit..

[32]  Rama Chellappa,et al.  A unified approach to boundary perception: edges, textures, and illusory contours , 1993, IEEE Trans. Neural Networks.

[33]  C. von der Malsburg,et al.  Distortion invariant object recognition by matching hierarchically labeled graphs , 1989, International 1989 Joint Conference on Neural Networks.

[34]  M. K. Khan,et al.  Machine identification of human faces , 1981, Pattern Recognition.

[35]  A. Young,et al.  Handbook of Research on Face Processing , 1989 .

[36]  D H HUBEL,et al.  RECEPTIVE FIELDS AND FUNCTIONAL ARCHITECTURE IN TWO NONSTRIATE VISUAL AREAS (18 AND 19) OF THE CAT. , 1965, Journal of neurophysiology.

[37]  J. G. Daugman.  Relaxation neural network for nonorthogonal image transforms , 1988, IEEE 1988 International Conference on Neural Networks.

[38]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .