Methoden vergelijken

	Stereo-matching	Histogramegalisatie
Vakgebied	Computer vision	Computer vision
Familie	Machine learning	Machine learning
Jaar van ontstaan	1990s	1970s
Grondlegger	David Scharstein and Richard Szeliski	Signal processing community
Type	Depth estimation and 3D vision	Contrast enhancement and preprocessing
Oorspronkelijke bron	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.	Gonzalez, R. C., & Woods, R. E. (1992). Digital Image Processing. Addison-Wesley, 2nd edition, Chapter 3.
Aliassen	Stereo correspondence, Disparity estimation	Histogram stretching, Contrast enhancement
Verwant	5	5
Samenvatting	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.