Comparer des méthodes

	Égalisation d'histogramme	Correspondance de modèle
Domaine	Vision par ordinateur	Vision par ordinateur
Famille	Machine learning	Machine learning
Année d'origine	1970s	1980s
Auteur d'origine	Signal processing community	Computer vision community
Type	Contrast enhancement and preprocessing	Pattern matching and detection
Source fondatrice	Gonzalez, R. C., & Woods, R. E. (1992). Digital Image Processing. Addison-Wesley, 2nd edition, Chapter 3.	Lewis, J. P. (2004). Fast normalized cross-correlation. Vision Interface, 120–123.
Alias	Histogram stretching, Contrast enhancement	Correlation-based matching, Similarity matching
Apparentées	5	5
Résumé	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.	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.