Shape feature extraction from object corners

A method to extract shape features based on corners is described. Corners contain most of the shape information. Extraction of shape features which are invariant to scaling, rotation and translation is an important problem in computer vision and automatic target recognition systems. A Canny (1986) edge detector which is capable of producing single pixel wide edges is used for obtaining the contour from an image. Using this closed contour as input, the arch height function is computed at each point. The local maxima's correspond to the corner points in the shape. A set of efficient one dimensional moments which are invariant under rotation, translation and scale change is computed. These are the corresponding shape features. Classification is achieved by comparing the extracted features with the shape feature library. In order to validate the concept the following experiments were performed. Ten dissimilar aircrafts and ten similar aircrafts were used as inputs. Contour based moments performed better than the geometric moments in both the data sets. Rotation invariance of two very similar aircrafts showed that contour based moments performed better. The procedure described provides an elegant approach for extracting shape features. These features can also be used as inputs for training and recognizing shapes using neural networks.<
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