Bandingkan kaedah

	Penyamaan Histogram	Analisis Kontur
Bidang	Penglihatan Komputer	Penglihatan Komputer
Keluarga	Machine learning	Machine learning
Tahun asal	1970s	1985
Pengasas	Signal processing community	Satoshi Suzuki and Keiichi Abe
Jenis	Contrast enhancement and preprocessing	Shape and contour analysis
Sumber perintis	Gonzalez, R. C., & Woods, R. E. (1992). Digital Image Processing. Addison-Wesley, 2nd edition, Chapter 3.	Suzuki, S., & Abe, K. (1985). Topological structural analysis of digitized binary images by border following. Computer Vision, Graphics, and Image Processing, 30(1), 32–46.
Alias	Histogram stretching, Contrast enhancement	Edge-based contours, Boundary analysis
Berkaitan	5	5
Ringkasan	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.	Contour analysis is the process of detecting and analyzing the boundaries of objects in images by identifying connected edges and extracting shape information. The Suzuki-Abe algorithm provides an efficient method for finding contours in binary images, enabling shape-based object classification and segmentation.