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An Autoregressive Model Approach to Two-Dimensional Shape Classification
January 1986 (vol. 8 no. 1)
pp. 55-66
Susan R. Dubois, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139.
Filson H. Glanz, Department of Electrical and Computer Engineering, University of New Hampshire, Durham, NH 03824.
In this paper, a method of classifying objects is reported that is based on the use of autoregressive (AR) model parameters which represent the shapes of boundaries detected in digitized binary images of the objects. The object identification technique is insensitive to object size and orientation. Three pattern recognition algorithms that assign object names to unlabelled sets of AR model parameters were tested and the results compared. Isolated object tests were performed on five sets of shapes, including eight industrial shapes (mostly taken from the recognition literature), and recognition accuracies of 100 percent were obtained for all pattern sets at some model order in the range 1 to 10. Test results indicate the ability of the technique developed in this work to recognize partially occluded objects. Processing-speed measurements show that the method is fast in the recognition mode. The results of a number of object recognition tests are presented. The recognition technique was realized with Fortran programs, Imaging Technology, Inc. image-processing boards, and a PDP 11/60 computer. The computer algorithms are described.
Susan R. Dubois, Filson H. Glanz, "An Autoregressive Model Approach to Two-Dimensional Shape Classification," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 1, pp. 55-66, Jan. 1986, doi:10.1109/TPAMI.1986.4767752
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