Cognitive Contour Detection of Sparse-Structured Objects in the Alpha-Shape Scale Space

• Published in: IEEE transactions on image processing Vol. 34; pp. 4955 - 4967
• Main Authors: Shen, Yuxiang, Zhong, Baojiang, Ma, Kai-Kuang
• Format: Journal Article
• Language: English
• Published: United States IEEE 2025
• Subjects: alpha shape; Biological system modeling; Birds; Cognitive contour; Computational modeling; contour detection; Convex hulls; Data mining; Drones; Robot sensing systems; scale space representation; Shape; sparse-structured objects; Training; Visualization
• ISSN: 1057-7149; 1941-0042; 1941-0042
• DOI: 10.1109/TIP.2025.3592862

In this paper, we introduce cognitive contour , a novel image attribute that encapsulates the global shape perceived from sparsely distributed, identical or similar objects-such as drone swarms or flocks of geese-collectively termed sparse-structured objects . Unlike traditional contour analysis that delineates the boundaries of individual objects, cognitive contours reflect a gestalt-inspired perception of the overall structure formed by the ensemble, capturing higher-level visual organization. Detecting cognitive contours is challenging due to the sparsity and multiplicity of constituent elements. To tackle this, we propose a scale-space method that integrates alpha shapes into a scale-space framework. An alpha-shape scale space is constructed for the sparse-structured object, and the optimal scale is adaptively selected to extract cognitively meaningful contours with appropriate structural detail. Extensive experiments validate the effectiveness and robustness of the proposed method, enhancing visual inference and offering flexibility across diverse image-based applications. Code and data are available at: https://github.com/CookiC/Sparse