METRIC ANALYSIS OF PLANAR OBJECT CONTOURS FOR EFFICIENT SHAPE RECOGNITION IN BINARY IMAGES
Abstract
Abstract: This paper investigates a contour-based metric approach for planar object recognition in binary images [1,2,14]. The proposed method represents each object using contour length as a global geometric descriptor and evaluates its effectiveness under rotation and reconstruction scenarios [1,14,15]. Contours are extracted after preprocessing steps including binarization, noise suppression, and morphological filtering. The contour length is computed using a weighted neighborhood scheme that accounts for horizontal, vertical, and diagonal connections. Experiments were conducted on a dataset consisting of original and reconstructed binary images with full rotational variations. The results demonstrate that contour length is computationally efficient and invariant to rotation, making it suitable for real-time image processing applications [1,2,14]. However, statistical analysis using empirical cumulative distribution functions and histogram distributions reveals significant overlap of contour length values among different objects, which limits discriminative performance for complex shapes [14,15]. The study concludes that contour length alone is insufficient for reliable shape recognition but provides a useful baseline for more advanced local and hybrid contour descriptors [14,15].