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| Correspondance stéréoscopique× | Détection de coins de Harris× | |
|---|---|---|
| Domaine | Vision par ordinateur | Vision par ordinateur |
| Famille | Machine learning | Machine learning |
| Année d'origine≠ | 1990s | 1988 |
| Auteur d'origine≠ | David Scharstein and Richard Szeliski | Chris Harris and Mike Stephens |
| Type≠ | Depth estimation and 3D vision | Interest point detector |
| Source fondatrice≠ | Scharstein, D., & Szeliski, R. (2002). A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision, 47(1), 7–42. DOI ↗ | Harris, C., & Stephens, M. (1988). A combined corner and edge detector. Alvey Vision Conference, 147–152. link ↗ |
| Alias≠ | Stereo correspondence, Disparity estimation | Harris Corner Detector, Harris-Stephens Detector, Plessey Operator |
| Apparentées | 5 | 5 |
| Résumé≠ | Stereo matching is a computer vision technique for recovering depth information by finding corresponding points between a pair of stereo images (taken from slightly different viewpoints). By locating the same scene feature in both images and measuring the disparity (horizontal shift), stereo matching reconstructs 3D structure using the principles of triangulation. | The Harris corner detector, introduced by Chris Harris and Mike Stephens in 1988, is a foundational method for identifying corners and interest points in digital images. Harris corners are points where two edges meet at a significant angle, making them stable and repeatable features for image analysis, matching, and 3D reconstruction. |
| ScholarGateJeu de données ↗ |
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