Comparer des méthodes

	Égalisation d'histogramme	Détection de blobs
Domaine	Vision par ordinateur	Vision par ordinateur
Famille	Machine learning	Machine learning
Année d'origine	1970s	1998
Auteur d'origine	Signal processing community	Tony Lindeberg
Type	Contrast enhancement and preprocessing	Multi-scale feature detection
Source fondatrice	Gonzalez, R. C., & Woods, R. E. (1992). Digital Image Processing. Addison-Wesley, 2nd edition, Chapter 3.	Lindeberg, T. (1998). Feature detection with automatic scale selection. International Journal of Computer Vision, 30(2), 79–116.
Alias	Histogram stretching, Contrast enhancement	Connected component analysis, Region-based detection
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.	Blob detection is a technique for identifying regions of interest (blobs)—connected, homogeneous areas that differ from their surroundings—at multiple scales. Introduced by Lindeberg in the context of scale-space theory, blob detection automatically finds and characterizes circular or elliptical objects without requiring a priori knowledge of their size.