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Abstract
Abstract: This paper investigates a one-dimensional (1D) descriptor model for image recognition based solely on contour length. The proposed approach represents each planar object by a single scalar value obtained through weighted accumulation of local contour segments. The primary objective is to evaluate the computational efficiency and discriminative capability of this simplified descriptor under rotation and reconstruction scenarios. Contours are extracted using Canny edge detection following binarization and noise suppression. Experimental results on a dataset of original and reconstructed images demonstrate that the contour-length descriptor provides high computational speed and rotational invariance for smooth shapes. However, due to its global nature, the model fails to adequately capture local geometric variations, leading to reduced discrimination for complex contours. The findings confirm that contour length alone is insufficient for robust shape recognition and the integration of higher-dimensional local structural descriptors in future research.
References
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