This Article 
 Bibliographic References 
 Add to: 
Silhouette-Based Isolated Object Recognition through Curvature Scale Space
May 1995 (vol. 17 no. 5)
pp. 539-544

Abstract—A complete, fast and practical isolated object recognition system has been developed which is very robust with respect to scale, position and orientation changes of the objects as well as noise and local deformations of shape (due to perspective projection, segmentation errors and non-rigid material used in some objects). The system has been tested on a wide variety of three-dimensional objects with different shapes and material and surface properties. A light-box setup is used to obtain silhouette images which are segmented to obtain the physical boundaries of the objects which are classified as either convex or concave. Convex curves are recognized using their four high-scale curvature extrema points. Curvature Scale Space (CSS) Representations are computed for concave curves. The CSS representation is a multi-scale organization of the natural, invariant features of a curve (curvature zero-crossings or extrema) and useful for very reliable recognition of the correct model since it places no constraints on the shape of objects. A three-stage, coarse-to-fine matching algorithm prunes the search space in stage one by applying the CSS aspect ratio test. The maxima of contours in CSS representations of the surviving models are used for fast CSS matching in stage two. Finally, stage three verifies the best match and resolves any ambiguities by determining the distance between the image and model curves. Transformation parameter optimization is then used to find the best fit of the input object to the correct model.

[1] Y. Hsu,H.H. Arsenault,, and G. April,“Rotation-invariant digital pattern recognition using circular harmonic expansion,” Applied Optics, vol. 21, no. 22, pp. 4012-4015, 1982.
[2] M.K. Hu,“Pattern recognition by moments invariants,” Proc. IRE, vol. 49, 1961.
[3] A. Kontanzad and Y.H. Hong, “Invariant Image Recognition by Zernike Moments,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, no. 5, pp. 489-497, May 1990.
[4] D. Marr and E. Hildreth,“Theory of edge detection,” Proc. Royal Soc. London B, vol. 207, pp. 187-217, 1980.
[5] F. Mokhtarian,“Fingerprint theorems for curvature and torsion zero-crossings,” Proc. IEEE Conf. CVPR, pp. 269-275,San Diego, Calif., 1989.
[6] F. Mokhtarian and A. Mackworth,“Scale-based description and recognition of planar curbes and two-dimensional shapes,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 8, no. 1, pp. 34-43, 1986.
[7] F. Mokhtarian and A.K. Mackworth, “A Theory of Multiscale, Curvature-Based Shape Representation for Planar Curves,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 14, no. 8, pp. 789-805, Aug. 1992.
[8] F. Mokhtarian and H. Murase,“Silhouette-based object recognition through curvature scale space,” Proc. Int’l. Conf. Computer Vision, pp. 269-274,Berlin, Germany, 1993.
[9] E. Persoon and K.S. Fu,“Shape discrimination using Fourier descriptors,” IEEE Trans. Systems, Man, and Cybernetics, vol. 7, pp. 170-179, 1977.
[10] J.L. Stansfield,“Conclusions from the commodity expert project,” AI Memo No. 601, MIT AI Lab, Cambridge, Mass., 1980.
[11] C.H. Teh and R.T. Chin, "On image analysis by the methods of moments," IEEE Trans. Pattern Anal. Machine Intell., vol. 10, pp. 496-512, July 1988.
[12] T.P. Wallace and O.R. Mitchell,“Analysis of three-dimensional movement using Fourier descriptors,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 2, pp. 583-588, 1980.
[13] D.H. Lee and S.M. Reddy, On Determining Scan Flip-Flops in Partial Scan Designs Proc. IEEE/ACM Int'l Conf. Computer-Aided Design, pp. 322-325, 1990.
[14] A.P. Witkin,“Scale space filtering,” Proc. IJCAI,Karlsruhe, Germany, 1983.

Index Terms:
Object recognition system, light-box setup, boundary contours, curvature scale space representation, maxima of curvature zero-crossing contours, coarse-to-fine matching strategy, transformation parameter optimization.
Farzin Mokhtarian, "Silhouette-Based Isolated Object Recognition through Curvature Scale Space," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 5, pp. 539-544, May 1995, doi:10.1109/34.391387
Usage of this product signifies your acceptance of the Terms of Use.