In computer vision, maximally stable extremal regions (MSER) are used as a method of blob detection in images. This technique was proposed by Matas et al.[1] to find correspondences between image elements from two images with different viewpoints. This method of extracting a comprehensive number of corresponding image elements contributes to the wide-baseline matching, and it has led to better stereo matching and object recognition algorithms.
^J. Matas, O. Chum, M. Urban, and T. Pajdla. "Robust wide baseline stereo from maximally stable extremal regions." Proc. of British Machine Vision Conference, pages 384-396, 2002.
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measured how stable these were along the intensity dimension. Based on this idea, they defined a notion of maximallystableextremalregions and showed...
CPU). It is based on a fast Hough-transform voting strategy for planar regions, inspired by the Kernel-based Hough transform (KHT). This 3D kernel-based...
gradients or edge directions. The image is divided into small connected regions called cells, and for the pixels within each cell, a histogram of gradient...
region detector, maximallystableextremalregions, Kadir–Brady saliency detector, edge-based regions (EBR) and intensity-extrema-based regions (IBR). Mikolajczyk...
change around the point and points are chosen where this determinant is maximal. In contrast to the Hessian-Laplacian detector by Mikolajczyk and Schmid...
MSER may refer to Maximallystableextremalregions, a method of blob detection in images Materials Science and Engineering R: Reports, a journal in materials...
minimum, line endings, or a point on a curve where the curvature is locally maximal. In practice, most so-called corner detection methods detect interest points...
{\displaystyle e_{1}} (or − e 1 {\displaystyle -e_{1}} ) is the direction that is maximally aligned with the gradient within the window. In particular, if λ 1 > 0...
valleys can be seen as a complement to natural interest points or local extremal points. With appropriately defined concepts, ridges and valleys in the...
features are appropriate for matching: the paper detects ellipse-shaped regions of interest using both edge-like and blob-like features, and as per [Lowe...
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