Model based recognition of specular objects using sensor models

The authors present a model-based object recognition system for specular objects. Objects with specular surfaces present a problem for computer vision. Simulating object appearances by using the sensor model, and the object model allows us to predict specular features, and to analyze the detectability and reliability of each feature. The system generates a set of aspects of the object. By precompiling the aspects with the feature detectability and the feature reliability, the system prepares adaptable matching templates. At the runtime, an input image is first classified into a few candidate aspects. A deformable template matching finds the best match among them. This method is applicable to multiple objects simply by changing object and sensor models. Experimental results using two kinds of objects and sensors are presented: a TV image of a shiny object and a synthetic aperture radar (SAR) image of an airplane. The results show the flexibility of the proposed model based approach.<<ETX>>

[1]  Takeo Kanade,et al.  Modelling sensors: Toward automatic generation of object recognition program , 1989, Comput. Vis. Graph. Image Process..

[2]  Jacques Verly,et al.  Machine Intelligence Technology for Automatic Target Recognition , 1989 .

[3]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[4]  Martin A. Fischler,et al.  The Representation and Matching of Pictorial Structures , 1973, IEEE Transactions on Computers.

[5]  Takeo Kanade,et al.  Model-Based Vision By Cooperative Processing Of Evidence And Hypotheses Using Configuration Spaces , 1988, Defense, Security, and Sensing.

[6]  B. P. Mccune,et al.  Radar with sight and knowledge , 1983 .

[7]  Yoshiaki Shirai,et al.  A Model-based Recognition of Glossy objects using Their Polarizational Properties* , 1985 .

[8]  Robert B. Kelley,et al.  A Robot System Which Acquires Cylindrical Workpieces from Bins , 1982, IEEE Transactions on Systems, Man, and Cybernetics.

[9]  Keith Michael Andress,et al.  Evidence accumulation & flow of control , 1988 .

[10]  Takeo Kanade,et al.  Determining shape and reflectance of hybrid surfaces by photometric sampling , 1989, IEEE Trans. Robotics Autom..

[11]  Youji Fukada,et al.  Relationships-based recognition of structural industrial parts stacked in a bin , 1984, Robotica.

[12]  Martin G. Bello,et al.  Representation and transformation of uncertainty in an evidence theory framework , 1989, Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[13]  Lee E. Weiss,et al.  Structured Highlight Inspection of Specular Surfaces , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Katsushi Ikeuchi,et al.  Determining Surface Orientations of Specular Surfaces by Using the Photometric Stereo Method , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  K. Tomiyasu,et al.  Tutorial review of synthetic-aperture radar (SAR) with applications to imaging of the ocean surface , 1978, Proceedings of the IEEE.