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Omkar, S. N.
- Automatic Human Face Recognition Using Multivariate Gaussian Model and Fisher Linear Discriminative Analysis
Abstract Views :179 |
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Authors
Affiliations
1 Department of Electronics and Communication Engineering, Marri Laxman Reddy Institute of Technology and Management, IN
2 Department of Aerospace Engineering, Indian Institute of Science, IN
1 Department of Electronics and Communication Engineering, Marri Laxman Reddy Institute of Technology and Management, IN
2 Department of Aerospace Engineering, Indian Institute of Science, IN
Source
ICTACT Journal on Image and Video Processing, Vol 5, No 1 (2014), Pagination: 899-902Abstract
Face recognition plays an important role in surveillance, biometrics and is a popular application of computer vision. In this paper, color based skin segmentation is proposed to detect faces and is matched with faces from the dataset. The proposed color based segmentation method is tested in different color spaces to identify suitable color space for identification of faces. Based on the sample skin distribution a Multivariate Gaussian Model is fitted to identify skin regions from which face regions are detected using connected components. The detected face is match with a template and verified. The proposed method Multivariate Gaussian Model - Fisher Linear Discriminative Analysis (MGM - FLDA) is compared with machine learning - Viola&Jones algorithm and it gives better results in terms of time.Keywords
Face Recognition, Multivariate Gaussian Model, Image Segmentation, Connected Component, Color Spaces.- Analysis of Wrist Extension Using Digital Image Correlation
Abstract Views :167 |
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Authors
S. N. Omkar
1,
Amarjot Singh
2
Affiliations
1 Department of Aerospace Engineering, Indian Institute of Science, Bangalore, IN
2 Department of Electrical and Electronics Engineering, National Institute of Technology, Warangal, IN
1 Department of Aerospace Engineering, Indian Institute of Science, Bangalore, IN
2 Department of Electrical and Electronics Engineering, National Institute of Technology, Warangal, IN
Source
ICTACT Journal on Image and Video Processing, Vol 2, No 3 (2012), Pagination: 343-351Abstract
Carpal Tunnel Syndrome also known as median neuropathy is a painful disorder of the wrist and hand. It is a medical condition arising due to the abbreviation of the median nerve in the vicinity of carpal tunnel, resulting in numbness, parenthesis and muscle weakness in hand. We present an effective optical approach for the measurement of strain on superficial muscles and tendons due to wrist extension, a remedy for carpal tunnel syndrome. The DIC code developed computes the in-plane strain with a correlation function using pictures taken, using a CCD camera, before and after extension. The shift between the initial picture and subsequent one is performed by computation of cross-correlation using FFT. This study can assist practitioners working in the field of applied anthropology to develop advanced diagnosis methodologies.Keywords
Digital Image Correlation, Carpal Tunnel Syndrome, Wrist Extension.- Sign Language Recognition Using Thinning Algorithm
Abstract Views :155 |
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Authors
S. N. Omkar
1,
M. Monisha
2
Affiliations
1 Department of Aerospace Engineering, Indian Institute of Science, Bangalore, Karnataka, IN
2 Department of Information Technology, National Institute of Technology Karnataka, Surathkal, IN
1 Department of Aerospace Engineering, Indian Institute of Science, Bangalore, Karnataka, IN
2 Department of Information Technology, National Institute of Technology Karnataka, Surathkal, IN
Source
ICTACT Journal on Image and Video Processing, Vol 2, No 1 (2011), Pagination: 241-245Abstract
In the recent years many approaches have been made that uses computer vision algorithms to interpret sign language. This endeavour is yet another approach to accomplish interpretation of human hand gestures. The first step of this work is background subtraction which achieved by the Euclidean distance threshold method. Thinning algorithm is then applied to obtain a thinned image of the human hand for further analysis. The different feature points which include terminating points and curved edges are extracted for the recognition of the different signs. The input for the project is taken from video data of a human hand gesturing all the signs of the American Sign Language.Keywords
Hand Gesture Recognition, Sign Language, Pre-Processing, Thinning Algorithm, Feature Points Extraction.- Analysis of Tarsal Tunnel Syndrome Using Image Correlation
Abstract Views :168 |
PDF Views:0
Authors
Amarjot Singh
1,
S. N. Omkar
2
Affiliations
1 Department of Electrical and Electronics Engineering, National Institute of Technology, Warangal, IN
2 Department of Aerospace Engineering, Indian Institute of Science, Bangalore, IN
1 Department of Electrical and Electronics Engineering, National Institute of Technology, Warangal, IN
2 Department of Aerospace Engineering, Indian Institute of Science, Bangalore, IN