Research on Analytical Solution Tensor Voting
نویسندگان
چکیده
منابع مشابه
A Closed-Form Solution to Tensor Voting: Theory and Applications
We prove a closed-form solution to tensor voting (CFTV): given a point set in any dimensions, our closed-form solution provides an exact, continuous and efficient algorithm for computing a structure-aware tensor that simultaneously achieves salient structure detection and outlier attenuation. Using CFTV, we prove the convergence of tensor voting on a Markov random field (MRF), thus termed as MR...
متن کاملInvestigations of Tensor Voting Modeling
Tensor voting (TV) is a method for inferring geometric structures from sparse, irregular and possibly noisy input. It was initially proposed by Guy and Medioni [Guy96] and has been applied to several computer vision applications. TV generates a dense output field in a domain by dispersing information associated with sparse input tokens. In 3-D this implies that a surface can be generated from a...
متن کاملTensor Voting Based Binary Classifier
We propose two novel Tensor Voting (TV) based supervised binary classification algorithms for N-Dimensional (N-D) data points. (a) The first one finds an approximation to a separating hyper-surface that best separates the given two classes in N-D: this is done by finding a set of candidate decision-surface points (using the training data) and then modeling the decision surface by local planes u...
متن کاملTensor Voting: Theory and Applications
We present a unified computational framework which properly implements the smoothness constraint to generate descriptions in terms of surfaces, regions, curves, and labelled junctions, from sparse, noisy, binary data in 2-D or 3-D. Each input site can be a point, a point with an associated tangent direction, a point with an associated normal direction, or any combination of the above. The metho...
متن کاملA Closed-Form Solution to Tensor Voting for Robust Parameter Estimation via Expectation-Maximization
We prove a closed-form solution to second-order Tensor Voting (TV), and employ the resulting structure-aware tensors in ExpectationMaximization (EM). Our new algorithm, aptly called EM-TV, is an efficient and robust method for parameter estimation. Quantitative comparison shows that our method performs better than the conventional second-order TV and other representative techniques in parameter...
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ژورنال
عنوان ژورنال: IEICE Transactions on Information and Systems
سال: 2018
ISSN: 0916-8532,1745-1361
DOI: 10.1587/transinf.2017edl8181