Contours Extraction Using Line Detection and Zernike Moment

Abstract

Most of the contour detection methods suffers from some drawbacks such as noise, occlusion of objects, shifting, scaling and rotation of objects in image which they suppress the recognition accuracy. To solve the problem, this paper utilizes Zernike Moment (ZM) and Pseudo Zernike Moment (PZM) to extract object contour features in all situations such as rotation, scaling and shifting of object inside the image. The proposed method consist of three steps: first step employs Line Detection with Contours (LDC) in order to find the object region based on the connected components objects inside the image. In the second step, PZM is applied on the detected object regions to extract feature vector. Regarding to investigate the effectiveness of classifier at the final stage, the SVM and KNN classifiers are employed to extract final object contours. Experimental results on Caltech-101 dataset shows that classification rate is improved to 96.46%. In comparison to the former contour detectors, that proves the ability of the proposed method to detect object boundary in the most of the contour's changes such as rotation or scaling.