GRoM &amp;#x2014; Generalized robust multichannel featur detector

Smirnov, Pavel; Semenov, P. O.; Lyakh, Mikhail; Chun, Anthony; Gusev, Dmitry; Redkin, Alexander; Srinivasan, Sadagopan

doi:10.1109/icsipa.2011.6144155

Cited by 1 publication

(3 citation statements)

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“…It coincides with the method [2], adapted to the case of 2D surface; 4. The method of adaptive neighbourhood projection [3]. It is adapted by us to the case of 2D surface.…”

Section: The Experimentsmentioning

confidence: 99%

“…Blobs are sought as local extremums of a blob response function. Several color blob detection algorithms were proposed in [2,3]. Blob detection has applications in 3D face recognition, object recognition, panorama stitching, 3D scene modeling, tracking, action recognition, medical images processing, etc.…”

Section: Introductionmentioning

confidence: 99%

“…In [9] blob detection was generalized to 2D surfaces. Several approaches to generalization of blob detection to color case were proposed in [2,3]. However, these approaches are based on global or local conversion of a color image to the grayscale, so they can't be used for manifold-valued images.…”

Section: The Problem Introductionmentioning

confidence: 99%

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A Riemanian Approach to Blob Detection in Manifold-Valued Images

Shestov

Kumskov

2017

Lecture Notes in Computer Science

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This paper is devoted to the problem of blob detection in manifold-valued images. Our solution is based on new definitions of blob response functions. We define the blob response functions by means of curvatures of an image graph, considered as a submanifold. We call the proposed framework Riemannian blob detection. We prove that our approach can be viewed as a generalization of the grayscale blob detection technique. An expression of the Riemannian blob response functions through the image Hessian is derived. We provide experiments for the case of vector-valued images on 2D surfaces: the proposed framework is tested on the task of chemical compounds classification.

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“…It coincides with the method [2], adapted to the case of 2D surface; 4. The method of adaptive neighbourhood projection [3]. It is adapted by us to the case of 2D surface.…”

Section: The Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%