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| Stereo-Matching× | Histogramm-Entzerrung× | |
|---|---|---|
| Fachgebiet | Maschinelles Sehen | Maschinelles Sehen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1990s | 1970s |
| Urheber≠ | David Scharstein and Richard Szeliski | Signal processing community |
| Typ≠ | Depth estimation and 3D vision | Contrast enhancement and preprocessing |
| Wegweisende Quelle≠ | 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 ↗ | Gonzalez, R. C., & Woods, R. E. (1992). Digital Image Processing. Addison-Wesley, 2nd edition, Chapter 3. link ↗ |
| Aliasnamen | Stereo correspondence, Disparity estimation | Histogram stretching, Contrast enhancement |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | 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. | Histogram equalization is an image preprocessing technique that redistributes pixel intensities to improve contrast and visibility of details. By spreading the histogram of pixel values evenly across the available range, histogram equalization enhances images with poor contrast, making features more visually distinct and easier to process algorithmically. |
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