Blurring-invariant Riemannian metrics for comparing signals and images

Zhang, Zhengwu; Klassen, Eric; Srivastava, Anuj; Turaga, Pavan; Chellappa, Rama

doi:10.1109/iccv.2011.6126442

Cited by 8 publications

(1 citation statement)

References 9 publications

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“…However, the calculation of the SIFT algorithm is complicated, which results in a very slow image matching speed and cannot meet the real-time performance of the intelligent assembly. With the increasing requirements for matching speed, E. Rublee proposed the ORB (Oriented FAST and Rotated BRIEF) algorithm in [7]. The literature points out that ORB is at least two orders of magnitude faster than SIFT.…”

Section: Introductionmentioning

confidence: 99%

Vision-Driven Dynamic Texture Recognition for Light Bar Intelligent Assembly Process

et al. 2023

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During the production and assembly process of televisions, the efficiency of assembly is limited by manual assembly methods. Conventional image matching algorithms such as SIFT and ORB fail to meet the real-time accuracy requirements of intelligent assembly processes. To address this issue, a novel algorithm called Adaptive Threshold oFAST and rBRIEF-BRISK (AFK) is proposed. This algorithm utilizes the Hamming distance as a similarity measure and integrates AToFAST and rBRIEF-BRISK fusion feature descriptors. Through a comparison of 12 sets of image matching scenarios, it is demonstrated that the AFK algorithm achieves a matching speed 4.5 times faster than the SIFT algorithm, with an image matching accuracy of up to 90%. Additionally, this paper presents the GLMLBP-TOP (Global-Local and Michelson Contrast LBP-Three Orthogonal Planes) dynamic texture recognition method. Through a comparison of 21 sets experiments, it is shown that the comprehensive classification accuracy of the GLMLBP-TOP method is approximately 94.5%, improving the performance of VLBP (87.71%) and LBP-TOP (91.1%) by 6.79% and 3.4% respectively. Real-world application results demonstrate that this method meets the requirements of intelligent assembly processes and has been successfully applied to the LB intelligent assembly process of a certain company's televisions.INDEX TERMS AFK algorithm, feature descriptor fused with AToFAST and rBRIEF-BRISK, GLMLBP-TOP dynamic texture recognition, LB intelligent assembly.

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Section: Introductionmentioning

confidence: 99%

Vision-Driven Dynamic Texture Recognition for Light Bar Intelligent Assembly Process

et al. 2023

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Elastic Shape Analysis of Functions, Curves and Trajectories

Joshi¹,

Su²,

Zhang³

et al. 2016

Riemannian Computing in Computer Vision

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International audienceWe present a Riemannian framework for geometric shape analysis of curves, functions, and trajectories on nonlinear manifolds. Since scalar functions and trajectories can also have important geometric features, we use shape as an all-encompassing term for the descriptors of curves, scalar functions and trajectories. Our framework relies on functional representation and analysis of curves and scalar functions, by square-root velocity fields (SRVF) under the Fisher–Rao metric, and of trajectories by transported square-root vector fields (TSRVF). SRVFs are general functional representations that jointly capture both the shape (geometry) and the reparameterization (sampling speed) of curves, whereas TSRVFs also capture temporal reparameterizations of time-indexed shapes. The space of SRVFs for shapes of curves becomes a subset of a spherical Riemannian manifold under certain special constraints. A fundamental tool in shape analysis is the construction and implementation of geodesic paths between shapes. This is used to accomplish a variety of tasks, including the definition of a metric to compare shapes, the computation of intrinsic statistics for a set of shapes, and the definition of probability models on shape spaces. We demonstrate our approach using several applications from computer vision and medical imaging including the analysis of (1) curves, (2) human growth, (3) bird migration patterns, and (4) human actions from video surveillance images and skeletons from depth images

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Exposing Blur Kernel from Retouch Image

Guo

2013

2013 International Conference on Computer-Aided Design and Computer Graphics

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Blurring-invariant Riemannian metrics for comparing signals and images

Abstract: We propose a novel Riemannian

Cited by 8 publications

References 9 publications

Vision-Driven Dynamic Texture Recognition for Light Bar Intelligent Assembly Process

Vision-Driven Dynamic Texture Recognition for Light Bar Intelligent Assembly Process

Elastic Shape Analysis of Functions, Curves and Trajectories

Exposing Blur Kernel from Retouch Image

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