scholarly journals Two-dimensional semi-nonnegative matrix factorization for clustering

2022 ◽  
Author(s):  
Chong Peng ◽  
Zhilu Zhang ◽  
Chenglizhao Chen ◽  
Zhao Kang ◽  
Qiang Cheng
Keyword(s):  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Two Dimensional

scholarly journals Kernel Two-Dimensional Nonnegative Matrix Factorization: A New Method to Target Detection for UUV Vision System

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Jian Xu ◽  
Pengfei Bi ◽  
Xue Du ◽  
Juan Li ◽  
Tianhao Jiang
Keyword(s):  
Target Detection ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Target Recognition ◽  
Vision System ◽  
Dimensional Space ◽  
Nonnegative Matrix ◽  
Two Dimensional ◽  
Basis Matrix ◽  
Underwater Target

This paper studies an advanced intelligent recognition method of underwater target based on unmanned underwater vehicle (UUV) vision system. This method is called kernel two-dimensional nonnegative matrix factorization (K2DNMF) which can further improve underwater operation capability of the UUV vision system. Our contributions can be summarized as follows: (1) K2DNMF intends to use the kernel method for the matrix factorization both on the column and row directions of the two-dimensional image data in order to transform the original low-dimensional space with nonlinearity into a higher dimensional space with linearity; (2) In the K2DNMF method, a good subspace approximation to the original data can be obtained by the orthogonal constraint on column basis matrix and row basis matrix; (3) The column basis matrix and row basis matrix can extract the feature information of underwater target images, and an effective classifier is designed to perform underwater target recognition; (4) A series of related experiments were performed on three sets of test samples collected by the UUV vision system, the experimental results demonstrate that K2DNMF has higher overall target detection accuracy than the traditional underwater target recognition methods.

scholarly journals Mitral Valve Segmentation Using Robust Nonnegative Matrix Factorization

2021 ◽  
Vol 7 (10) ◽  
pp. 213
Author(s):  
Hannah Dröge ◽  
Baichuan Yuan ◽  
Rafael Llerena ◽  
Jesse T. Yen ◽  
Michael Moeller ◽  
...  
Keyword(s):  
Mitral Valve ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Heart Diseases ◽  
Nonnegative Matrix ◽  
Two Dimensional ◽  
Treatment And Prevention ◽  
Fully Automatic ◽  
Localization Approach ◽  
Non Negative Matrix Factorization

Analyzing and understanding the movement of the mitral valve is of vital importance in cardiology, as the treatment and prevention of several serious heart diseases depend on it. Unfortunately, large amounts of noise as well as a highly varying image quality make the automatic tracking and segmentation of the mitral valve in two-dimensional echocardiographic videos challenging. In this paper, we present a fully automatic and unsupervised method for segmentation of the mitral valve in two-dimensional echocardiographic videos, independently of the echocardiographic view. We propose a bias-free variant of the robust non-negative matrix factorization (RNMF) along with a window-based localization approach, that is able to identify the mitral valve in several challenging situations. We improve the average f1-score on our dataset of 10 echocardiographic videos by 0.18 to a f1-score of 0.56.

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