Object detection based on gray level cooccurrence

Previous investigations indicate that edge information, gray level histogram information, texture information, and shape information are all useful in detecting objects. Gray level cooccurrence matrices contain a form of each of these types of information. Hence applying measures defined on cooccurrence matrices to the object detection problem would seem to be an approach which should be investigated. This paper presents a formulation of such an approach. The decision logic employed assumes that measures computed from regions containing the object form a cluster defined by N (μ 0 , Σ 0 ) in measurement space, while measures computed from regions not containing the object lie some distance away from this cluster. To help assure that this is the case, a measurement selection algorithm was formulated. Studies are reported which show testing detection accuracies of better than 90% on a number of experiments.

[1]  G.B. Coleman,et al.  Image segmentation by clustering , 1979, Proceedings of the IEEE.

[2]  S. Grinaker,et al.  Discrimination and classification of vehicles in natural scenes from thermal imagery , 1983, Comput. Vis. Graph. Image Process..

[3]  Richard W. Conners,et al.  A Theoretical Comparison of Texture Algorithms , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  David L. Milgram,et al.  Region extraction using convergent evidence , 1979 .

[5]  Robert B. McGhee,et al.  Aircraft Identification by Moment Invariants , 1977, IEEE Transactions on Computers.

[6]  Ramakant Nevatia,et al.  Locating Object Boundaries in Textured Environments , 1976, IEEE Transactions on Computers.

[7]  Mohan M. Trivedi,et al.  Segmentation of a high-resolution urban scene using texture operators , 1984, Comput. Vis. Graph. Image Process..

[8]  Thomas S. Huang,et al.  Using the creation machine to locate airplanes on aerial photos , 1980, Pattern Recognit..

[9]  P. Wintz,et al.  An efficient three-dimensional aircraft recognition algorithm using normalized fourier descriptors , 1980 .

[10]  Azriel Rosenfeld,et al.  Threshold Evaluation Techniques , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[11]  Richard W. Conners,et al.  Toward a Structural Textural Analyzer Based on Statistical Methods , 1980 .

[12]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[13]  Azriel Rosenfeld,et al.  Image Segmentation by Pixel Classification in (Gray Level, Edge Value) Space , 1978, IEEE Transactions on Computers.

[14]  F.W. Smith,et al.  Automatic Ship Photo Interpretation by the Method of Moments , 1971, IEEE Transactions on Computers.

[15]  Owen Robert Mitchell,et al.  Image segmentation using a local extrema texture measure , 1978, Pattern Recognit..

[16]  Shin-Yi Hsu,et al.  The Mahalanobis classifier with the generalized inverse approach for automated analysis of imagery texture data , 1979 .

[17]  A. Rosenfeld,et al.  A Note on the Use of Second-Order Gray Level Statistics for Threshold Selection. , 1977 .

[18]  O. Robert Mitchell,et al.  Adaptive Segmentation of Unique Objects , 1980 .

[19]  Joan S. Weszka,et al.  A survey of threshold selection techniques , 1978 .

[20]  Makoto Nagao,et al.  A Structural Analysis of Complex Aerial Photographs , 1980, Advanced Applications in Pattern Recognition.

[21]  Erica M. Rounds,et al.  Segmentation Based On Second-Order Statistics , 1980, Optics & Photonics.

[22]  O. Robert Mitchell,et al.  Segmentation And Classification Of Targets In Flir Imagery , 1979, Optics & Photonics.

[23]  Béla Julesz,et al.  Visual Pattern Discrimination , 1962, IRE Trans. Inf. Theory.

[24]  André Gagalowicz,et al.  A New Method for Texture Fields Synthesis: Some Applications to the Study of Human Vision , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.