Asymmetric Gaussian and Its Application to Pattern Recognition

Kato, Tsuyoshi; Omachi, Shinichiro; Aso, Hirotomo

doi:10.1007/3-540-70659-3_42

Cited by 63 publications

(36 citation statements)

References 9 publications

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“…Our method includes three main aspects: for the point set representation, we use the mixture of asymmetric Gaussian model (MoAG) which is more accuracy than Mixture of Gaussian model [1]. For robust estimation of non-rigid transformation, we formulate point set registration as an optimization problem by the regularized least square.…”

Section: Resultsmentioning

confidence: 99%

“…However, they do not always adapt and fit any distribution of patterns, Kato et al [1] introduced a new probability model named asymmetric Gaussian model (AG) which can capture spatially asymmetric distributions. Asymmetric Gaussian model is another form extending from Gaussian model.…”

Section: Asymmetric Gaussian Representation Methodsmentioning

confidence: 99%

“…where 1 is an 1 × D row vector of all ones, γ = 1 for t j ≤ (KH) j , and γ = 0 for t j > (KH) j . • denotes the Hadamard product and ⊗ denotes the tensor product.…”

Section: Optimization and Deterministic Annealingmentioning

confidence: 99%

“…1 Point set registration algorithms usually contain iteration process, we can estimate the non-rigid transformation parameters to update and move the Model point set iteratively, until reach a given termination condition. In the deterministic annealing framework, the termination condition σ 2 f inal is set to 0.001 according to the initial scale σ 2 .…”

Section: Ensurementioning

confidence: 99%

“…In this paper, we present a novel robust method for non-rigid point set registration, and it includes four important parts are as follows: First, we used a mixture of asymmetric Gaussian model (MoAG) Kato et al (2002) [1], a new probability model which can capture spatially asymmetric distributions, to represent each point set. Second, based on the representation of point set by MoAG, we used soft assignment technique to recover the correspondences, and correlation-based method to estimate the transformation parameters between two point sets.…”

mentioning

confidence: 99%

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A robust non-rigid point set registration method based on asymmetric gaussian representation

Wang

Chen

et al. 2015

Computer Vision and Image Understanding

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Section: Resultsmentioning

confidence: 99%

Section: Asymmetric Gaussian Representation Methodsmentioning

confidence: 99%

“…where 1 is an 1 × D row vector of all ones, γ = 1 for t j ≤ (KH) j , and γ = 0 for t j > (KH) j . • denotes the Hadamard product and ⊗ denotes the tensor product.…”

Section: Optimization and Deterministic Annealingmentioning

confidence: 99%

Section: Ensurementioning

confidence: 99%

mentioning

confidence: 99%

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A robust non-rigid point set registration method based on asymmetric gaussian representation

Wang

Chen

et al. 2015

Computer Vision and Image Understanding

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Deconvolution model to resolve cytometric microbial community patterns in flowing waters

et al. 2017

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Flow cytometry is suitable to discriminate and quantify aquatic microbial cells within a spectrum of fluorescence and light scatter signals. Using fixed gating and operational settings, we developed a finite distribution mixture model, followed by the Voronoi tessellation, to resolve bivariate cytometric profiles into cohesive subgroups of events. This procedure was applied to outline recurrent patterns and quantitative changes of the aquatic microbial community along a river hydrologic continuum. We found five major subgroups within each of the commonly retrieved populations of cells with Low and High content of Nucleic Acids (namely, LNA, and HNA cells). Moreover, the advanced analysis allowed assessing changes of community patterns perturbed by a wastewater feed. Our approach for cytometric data deconvolution confirmed that flow cytometry could represent a prime candidate technology for assessing microbial community patterns in flowing waters. © 2017 International Society for Advancement of Cytometry.

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Retinal fundus image registration framework using Bayesian integration and asymmetric Gaussian mixture model

Zhang

Jia

Zhuo

et al. 2022

Int J Imaging Syst Tech

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Retinal image registration, which is applied in diagnosing and treating eye diseases, plays an important role in medical image analysis. Existing methods suffer from problems due to different imaging viewpoints, times, quality, modalities, and retinal disasters. In this paper, we propose an efficient retinal images registration framework that overcomes these challenges without supervision. We present a layer-wise matching method to achieve a uniform distribution of features in both image-space and scale-space. Then, a novel method called Bayesian integration is generated to accumulate more meaningful inputs. We use the results of different matches as priors, assign a score to each match, and categorize them using a dynamic threshold. Finally, in accordance with previous work, we transform the problem into a probabilistic model, with the asymmetric Gaussian mixture model representing the distribution. A robust estimation is performed on a non-rigid transformation. The experimental results demonstrate that our proposed framework is robust to kinds of retinal image degradation and produces a more stable and accurate result than state-of-the-art methods.

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Asymmetric Gaussian and Its Application to Pattern Recognition

Cited by 63 publications

References 9 publications

A robust non-rigid point set registration method based on asymmetric gaussian representation

A robust non-rigid point set registration method based on asymmetric gaussian representation

Deconvolution model to resolve cytometric microbial community patterns in flowing waters

Retinal fundus image registration framework using Bayesian integration and asymmetric Gaussian mixture model

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