scholarly journals Sparse Representation and Dictionary Learning Model Incorporating Group Sparsity and Incoherence to Extract Abnormal Brain Regions Associated With Schizophrenia

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 104396-104406
Author(s):  
Peng Peng ◽  
Yongfeng Ju ◽  
Yipu Zhang ◽  
Kaiming Wang ◽  
Suying Jiang ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Dictionary Learning ◽  
Learning Model ◽  
Brain Regions ◽  
Group Sparsity ◽  
Abnormal Brain

Sparse angle CT reconstruction with weighted dictionary learning algorithm based on adaptive group-sparsity regularization

2021 ◽  
pp. 1-18
Author(s):  
Tiejun Yang ◽  
Lu Tang ◽  
Qi Tang ◽  
Lei Li
Keyword(s):  
Sparse Representation ◽  
Dictionary Learning ◽  
Learning Algorithm ◽  
Basic Unit ◽  
Ct Reconstruction ◽  
Group Sparsity ◽  
Sparsity Regularization ◽  
Smoothing Effects ◽  
Structural Details ◽  
Non Local

OBJECTIVE: In order to solve the blurred structural details and over-smoothing effects in sparse representation dictionary learning reconstruction algorithm, this study aims to test sparse angle CT reconstruction with weighted dictionary learning algorithm based on adaptive Group-Sparsity Regularization (AGSR-SART). METHODS: First, a new similarity measure is defined in which Covariance is introduced into Euclidean distance, Non-local image patches are adaptively divided into groups of different sizes as the basic unit of sparse representation. Second, the weight factor of the regular constraint terms is designed through the residuals represented by the dictionary, so that the algorithm takes different smoothing effects on different regions of the image during the iterative process. The sparse reconstructed image is modified according to the difference between the estimated value and the intermediate image. Last, The SBI (Split Bregman Iteration) iterative algorithm is used to solve the objective function. An abdominal image, a pelvic image and a thoracic image are employed to evaluate performance of the proposed method. RESULTS: In terms of quantitative evaluations, experimental results show that new algorithm yields PSNR of 48.20, the maximum SSIM of 99.06% and the minimum MAE of 0.0028. CONCLUSIONS: This study demonstrates that new algorithm can better preserve structural details in reconstructed CT images. It eliminates the effect of excessive smoothing in sparse angle reconstruction, enhances the sparseness and non-local self-similarity of the image, and thus it is superior to several existing reconstruction algorithms.

scholarly journals Shift Invariant Dictionary Learning for Human Action Recognition

2019 ◽  
Vol 9 (2) ◽  
pp. 2107-2111
Keyword(s):  
Sparse Representation ◽  
Action Recognition ◽  
Dictionary Learning ◽  
Human Action Recognition ◽  
Human Action ◽  
Signal Denoising ◽  
Learning Approaches ◽  
Training Time ◽  
Initial Stage ◽  
Shift Invariance

Sparse representation is an emerging topic among researchers. The method to represent the huge volume of dense data as sparse data is much needed for various fields such as classification, compression and signal denoising. The base of the sparse representation is dictionary learning. In most of the dictionary learning approaches, the dictionary is learnt based on the input training signals which consumes more time. To solve this issue, the shift-invariant dictionary is used for action recognition in this work. Shift-Invariant Dictionary (SID) is that the dictionary is constructed in the initial stage with shift-invariance of initial atoms. The advantage of the proposed SID based action recognition method is that it requires minimum training time and achieves highest accuracy.

scholarly journals Using Partial Differential Equation Face Recognition Model to Evaluate Students’ Attention in a College Chinese Classroom

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xia Miao ◽  
Ziyao Yu ◽  
Ming Liu
Keyword(s):  
Differential Equation ◽  
Partial Differential Equation ◽  
Face Recognition ◽  
Sparse Representation ◽  
Nearest Neighbor ◽  
Learning Model ◽  
Local Geometry ◽  
Partial Differential ◽  
The Face ◽  
Expression Score

The partial differential equation learning model is applied to another high-level visual-processing problem: face recognition. A novel feature selection method based on partial differential equation learning model is proposed. The extracted features are invariant to rotation and translation and more robust to illumination changes. In the evaluation of students’ concentration in class, this paper firstly uses the face detection algorithm in face recognition technology to detect the face and intercept the expression data, and calculates the rise rate. Then, the improved model of concentration analysis and evaluation of a college Chinese class is used to recognize facial expression, and the corresponding weight is given to calculate the expression score. Finally, the head-up rate calculated at the same time is multiplied by the expression score as the final concentration score. Through the experiment and analysis of the experimental results in the actual classroom, the corresponding conclusions are drawn and teaching suggestions are provided for teachers. For each face, a large neighborhood set is firstly selected by the k -nearest neighbor method, and then, the sparse representation of sample points in the neighborhood is obtained, which effectively combines the locality of k -nearest neighbor and the robustness of sparse representation. In the sparse preserving nonnegative block alignment algorithm, a discriminant partial optimization model is constructed by using sparse reconstruction coefficients to describe local geometry and weighted distance to describe class separability. The two algorithms obtain good clustering and recognition results in various cases of real and simulated occlusion, which shows the effectiveness and robustness of the algorithm. In order to verify the reliability of the model, this paper verified the model through in-class practice tests, teachers’ questions, and interviews with students and teachers. The results show that the proposed joint evaluation method based on expression and head-up rate has high accuracy and reliability.

scholarly journals Screening of sleep movement disorders via EMG signal analysis

2021 ◽  
Author(s):  
Mehrnaz Shokrollahi
Keyword(s):  
Feature Extraction ◽  
Sparse Representation ◽  
Movement Disorders ◽  
Dictionary Learning ◽  
Signal Analysis ◽  
Compact Set ◽  
Emg Signal ◽  
Increased Risk ◽  
Non Stationary Signal ◽  
Representation Techniques

It is estimated that 50 to 70 million Americans suffer from a chronic sleep disorder, which hinders their daily life, affects their health, and incurs a significant economic burden to society. Untreated Periodic Leg Movement (PLM) or Rapid Eye Movement Behaviour Disorder (RBD) could lead to a three to four-fold increased risk of stroke and Parkinson’s disease respectively. These risks bring about the need for less costly and more available diagnostic tools that will have great potential for detection and prevention. The goal of this study is to investigate the potentially clinically relevant but under-explored relationship of the sleep-related movement disorders of PLMs and RBD with cerebrovascular diseases. Our objective is to introduce a unique and efficient way of performing non-stationary signal analysis using sparse representation techniques. To fulfill this objective, at first, we develop a novel algorithm for Electromyogram (EMG) signals in sleep based on sparse representation, and we use a generalized method based on Leave-One-Out (LOO) to perform classification for small size datasets. In the second objective, due to the long-length of these EMG signals, the need for feature extraction algorithms that can localize to events of interest increases. To fulfill this objective, we propose to use the Non-Negative Matrix Factorization (NMF) algorithm by means of sparsity and dictionary learning. This allows us to represent a variety of EMG phenomena efficiently using a very compact set of spectrum bases. Yet EMG signals pose severe challenges in terms of the analysis and extraction of discriminant features. To achieve a balance between robustness and classification performance, we aim to exploit deep learning and study the discriminant features of the EMG signals by means of dictionary learning, kernels, and sparse representation for classification. The classification performances that were achieved for detection of RBD and PLM by means of implicating these properties were 90% and 97% respectively. The theoretical properties of the proposed approaches pertaining to pattern recognition and detection are examined in this dissertation. The multi-layer feature extraction provide strong and successful characterization and classification for the EMG non-stationary signals and the proposed sparse representation techniques facilitate the adaptation to EMG signal quantification in automating the identification process.

scholarly journals Motion Estimation in Echocardiography Using Sparse Representation and Dictionary Learning

2018 ◽  
Vol 27 (1) ◽  
pp. 64-77 ◽  
Author(s):  
Nora Ouzir ◽  
Adrian Basarab ◽  
Herve Liebgott ◽  
Brahim Harbaoui ◽  
Jean-Yves Tourneret
Keyword(s):  
Motion Estimation ◽  
Sparse Representation ◽  
Dictionary Learning
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