手法を比較

	イメージ形態学演算	ブロブ検出	Cannyエッジ検出	ヒストグラム均等化
分野	コンピュータビジョン	コンピュータビジョン	コンピュータビジョン	コンピュータビジョン
系統	Machine learning	Machine learning	Machine learning	Machine learning
提唱年	1982	1998	1986	1970s
提唱者	Jean Serra	Tony Lindeberg	John Canny	Signal processing community
種類	Set theory and topological image processing	Multi-scale feature detection	Image gradient analysis	Contrast enhancement and preprocessing
原典	Serra, J. (1982). Image Analysis and Mathematical Morphology. Academic Press.	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.	Gonzalez, R. C., & Woods, R. E. (1992). Digital Image Processing. Addison-Wesley, 2nd edition, Chapter 3.
別名	Mathematical morphology, Morphological filtering	Connected component analysis, Region-based detection	Canny operator, Canny edge detector	Histogram stretching, Contrast enhancement
関連	5	5	5	5
概要	Morphological image processing, introduced by Jean Serra in 1982, is a technique based on set theory that reshapes and analyzes image regions using geometric structuring elements. Core operations include erosion and dilation, which can be combined into more complex operations like opening and closing, enabling noise removal, edge detection, and object analysis.	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.	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.