The evolution and testing of a model-based object recognition system

A benchmark evaluation test of a model-based recognition system is discussed. The system was tested on a series of aerial reconnaissance images to evaluate recognition performance on the task of airfield monitoring. The effectiveness of the model pose constraint for recognition is discussed as well as an approach for selection of model features. The use of distance transforms for model hypothesis confirmation is also discussed.<<ETX>>

[1]  H. Whitney.  On Singularities of Mappings of Euclidean Spaces. I. Mappings of the Plane Into the Plane , 1955 .

[2]  Robert C. Bolles,et al.  Parametric Correspondence and Chamfer Matching: Two New Techniques for Image Matching , 1977, IJCAI.

[3]  Dana H. Ballard,et al.  Generalizing the Hough transform to detect arbitrary shapes , 1981, Pattern Recognit..

[4]  D. W. Thompson,et al.  Three-dimensional model matching from an unconstrained viewpoint , 1987, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

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

[6]  David G. Lowe,et al.  Perceptual Organization and Visual Recognition , 2012 .

[7]  Gunilla Borgefors,et al.  Distance transformations in digital images , 1986, Comput. Vis. Graph. Image Process..

[8]  W. Eric L. Grimson,et al.  On the Sensitivity of the Hough Transform for Object Recognition , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Larry S. Davis,et al.  Object recognition using oriented model points , 1986 .

[10]  Lawrence G. Roberts,et al.  Machine Perception of Three-Dimensional Solids , 1963, Outstanding Dissertations in the Computer Sciences.