Bandingkan metode

	Ekualisasi Histogram	Deteksi Blob	Deteksi Tepi Canny
Bidang	Visi Komputer	Visi Komputer	Visi Komputer
Keluarga	Machine learning	Machine learning	Machine learning
Tahun asal	1970s	1998	1986
Pencetus	Signal processing community	Tony Lindeberg	John Canny
Tipe	Contrast enhancement and preprocessing	Multi-scale feature detection	Image gradient analysis
Sumber perintis	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.	Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6), 679–698.
Alias	Histogram stretching, Contrast enhancement	Connected component analysis, Region-based detection	Canny operator, Canny edge detector
Terkait	5	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.	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.	The Canny edge detector, introduced by John Canny in 1986, is a multi-stage algorithm for identifying edges in digital images where significant intensity changes occur. Canny's method is optimal for step edges in additive Gaussian noise and remains the gold standard for edge detection in computer vision due to its mathematical elegance and practical effectiveness.