Comparar métodos

	Equalização de Histograma	Correspondência de Modelos
Área	Visão computacional	Visão computacional
Família	Machine learning	Machine learning
Ano de origem	1970s	1980s
Autor original	Signal processing community	Computer vision community
Tipo	Contrast enhancement and preprocessing	Pattern matching and detection
Fonte seminal	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.
Outros nomes	Histogram stretching, Contrast enhancement	Correlation-based matching, Similarity matching
Relacionados	5	5
Resumo	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.