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robust-principal-component-analysis
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Method as a preprocessing stage for tracking sperms progressive motility
, Article IEEE International Symposium on Signal Processing and Information Technology, IEEE ISSPIT 2013 ; 2013 , Pages 170-174 ; Lashgari, E ; Aghdam, A. A ; Khalaj, B. H ; Sharif University of Technology
IEEE Computer Society
2013
Abstract
Methods of human semen assessment are quite wide ranging. In this paper, we use background subtraction methods in order to detect progressive sperms whose quality of movement strongly influence fertility. Robust Principal Component Analysis (RPCA) is a powerful algorithm which has been used recently for background subtraction purposes. Sperm tracking problem can also be defined as a background subtraction problem. In RPCA algorithm, data is represented by a low rank plus sparse matrix. In our approach, the foreground data is recovered through such matrix decomposition. We compare the RPCA approach with four other background subtraction methods in order to check accuracy of algorithm as a...
Intensity estimation of spontaneous facial action units based on their sparsity properties
, Article IEEE Transactions on Cybernetics ; Volume 46, Issue 3 , 2016 , Pages 817-826 ; 21682267 (ISSN) ; Fatemizadeh, E ; Mahoor, M. H ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2016
Abstract
Automatic measurement of spontaneous facial action units (AUs) defined by the facial action coding system (FACS) is a challenging problem. The recent FACS user manual defines 33 AUs to describe different facial activities and expressions. In spontaneous facial expressions, a subset of AUs are often occurred or activated at a time. Given this fact that AUs occurred sparsely over time, we propose a novel method to detect the absence and presence of AUs and estimate their intensity levels via sparse representation (SR). We use the robust principal component analysis to decompose expression from facial identity and then estimate the intensity of multiple AUs jointly using a regression model...
Functional Connectivity Network in Rest-State fMRI Baseline in High Functioning Autism Disorder
, M.Sc. Thesis Sharif University of Technology ; Fatemizadeh, Emad (Supervisor)
Abstract
Autism spectrum disorders (ASD) have been defined as developmental disorders characterized by abnormalities in social interaction, communication skills, and behavioral flexibility. Over the past decades, studies using various genetic, neurobiological, cognitive and behavioral approaches have sought a single explanation for the heterogeneous manifestations of ASD, but no consensus on the etiology of ASD has emerged. Further studies aim to clarify the mechanism of disease.
Functional Magnetic Resonance Imaging (fMRI) is a new way of imaging which evaluates activity of brain by measuring magnetic difference caused by oscillation in blood oxygen level. fMRI has been widely used in recent...
Functional Magnetic Resonance Imaging (fMRI) is a new way of imaging which evaluates activity of brain by measuring magnetic difference caused by oscillation in blood oxygen level. fMRI has been widely used in recent...
Pain level estimation in video sequences of face using incorporation of statistical features of frames
, Article 9th Iranian Conference on Machine Vision and Image Processing, 18 November 2015 through 19 November 2015 ; Volume 2016-February , 2015 , Pages 172-175 ; 21666776 (ISSN) ; 9781467385398 (ISBN) ; Fatemizadeh, E ; Sharif University of Technology
IEEE Computer Society
Abstract
Pain level estimation from videos of face has many benefits for clinical applications. Most of the previous works focused only on pain detection task. However, pain level estimation of video sequences has been discussed fewer. In this work, we have proposed a new regression-based approach to estimate the pain level of video sequences. As the first step, facial expression-related features were extracted from each frame, this task was done by reducing identity-related features using the robust principal component analysis decomposition. Then, we used the minimum, maximum, and mean of the features of frames in a sequence to represent that sequence by a fixed-length feature vector. After this,...