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领域计算机视觉计算机视觉
方法族Machine learningMachine learning
起源年份19981985
提出者Tony LindebergSatoshi Suzuki and Keiichi Abe
类型Multi-scale feature detectionShape and contour analysis
开创性文献Lindeberg, T. (1998). Feature detection with automatic scale selection. International Journal of Computer Vision, 30(2), 79–116. DOI ↗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. DOI ↗
别名Connected component analysis, Region-based detectionEdge-based contours, Boundary analysis
相关55
摘要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.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.
ScholarGate数据集
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  1. v1
  2. 2 来源
  3. PUBLISHED

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ScholarGate方法对比: Blob Detection · Contour Analysis. 于 2026-06-17 检索自 https://scholargate.app/zh/compare