Emergence Model of Perception With Global-Contour Precedence Based on Gestalt Theory and Primary Visual Cortex
Perceptual edge grouping is a technique for organizing the cluttered edge pixels into meaningful structures and further serves high-level vision tasks, which has long been a basic and critical task in computer vision. Existing methods usually have a poor performance when coping with the junctions ca...
| Publié dans: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 34(2025) vom: 25., Seite 2721-2736 |
|---|---|
| Auteur principal: | |
| Autres auteurs: | |
| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2025
|
| Accès à la collection: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Sujets: | Journal Article |
| Résumé: | Perceptual edge grouping is a technique for organizing the cluttered edge pixels into meaningful structures and further serves high-level vision tasks, which has long been a basic and critical task in computer vision. Existing methods usually have a poor performance when coping with the junctions caused by occlusion and noise in natural images. In this paper, we present GPGrouper, a perceptual edge grouping model based on gestalt theory and the primary visual cortex (V1). Different from the existing methods, GPGrouper leverages the edge representation and grouping matrix (ERGM), a functional structure inspired by V1 mechanisms, to represent edges in a way that can effectively reduce grouping errors caused by occlusion between objects. ERGM is trained with natural image contours and further provides a priori guidance for the construction of the edge connection graph (ECG) that is useful to minimize the impact of noise on grouping. In the experiment, we compared GPGrouper and the state-of-the-art (SOTA) method of perceptual grouping on the visual psychology pathfinder challenge. The results demonstrate that GPGrouper outperforms the SOTA method in grouping performance. Furthermore, in the grouping experiments involving line segments with varying lengths detected by the Line Segment Detector (LSD), as well as those involving superpixel segmentation results with significant levels of interfering noise using the SLIC algorithm, GPGrouper was superior to the existing methods in terms of grouping effect and robustness. Moreover, the results of applying the grouping results to the vision tasks objectness demonstrate that GPGrouper can contribute significantly to high-level visual tasks |
|---|---|
| Description: | Date Completed 10.05.2025 Date Revised 12.05.2025 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1941-0042 |
| DOI: | 10.1109/TIP.2025.3562054 |