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| Sablonillesztés× | Kontúrelemzés× | |
|---|---|---|
| Tudományterület | Számítógépes látás | Számítógépes látás |
| Módszercsalád | Machine learning | Machine learning |
| Keletkezés éve≠ | 1980s | 1985 |
| Megalkotó≠ | Computer vision community | Satoshi Suzuki and Keiichi Abe |
| Típus≠ | Pattern matching and detection | Shape and contour analysis |
| Alapmű≠ | Lewis, J. P. (2004). Fast normalized cross-correlation. Vision Interface, 120–123. link ↗ | Suzuki, S., & Abe, K. (1985). Topological structural analysis of digitized binary images by border following. Computer Vision, Graphics, and Image Processing, 30(1), 32–46. DOI ↗ |
| Alternatív nevek | Correlation-based matching, Similarity matching | Edge-based contours, Boundary analysis |
| Kapcsolódó | 5 | 5 |
| Összefoglaló≠ | Template matching is a straightforward technique for locating a known pattern (template) within a larger image. By sliding a template image across the target image and computing a similarity measure at each position, template matching identifies locations where the template appears. It is effective for simple object detection when templates are well-defined and appearance variation is limited. | Contour analysis is the process of detecting and analyzing the boundaries of objects in images by identifying connected edges and extracting shape information. The Suzuki-Abe algorithm provides an efficient method for finding contours in binary images, enabling shape-based object classification and segmentation. |
| ScholarGateAdatkészlet ↗ |
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