S
S Sumam David
Researcher at National Institute of Technology, Karnataka
Publications - 51
Citations - 307
S Sumam David is an academic researcher from National Institute of Technology, Karnataka. The author has contributed to research in topics: Context-adaptive binary arithmetic coding & Distributed source coding. The author has an hindex of 9, co-authored 51 publications receiving 251 citations. Previous affiliations of S Sumam David include Indian Institute of Technology Madras.
Papers
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Journal ArticleDOI
Human Face Detection and Tracking using Skin Color Modeling and Connected Component Operators
TL;DR: A method to detect and track human faces in color image sequences is described, using skin color classification and morphological segmentation to detect face(s) in the first frame.
Journal ArticleDOI
Two-sided filters for frame-based prediction
S Sumam David,Bhaskar Ramamurthi +1 more
TL;DR: A linear prediction model, based on a two-sided predictor which predicts on the basis of past and future samples within a frame, is presented and showed at least 5-dB improvement over one-sided prediction in simulations on speech data.
Journal ArticleDOI
Early diagnosis of osteoporosis using radiogrammetry and texture analysis from hand and wrist radiographs in Indian population
Anu Shaju Areeckal,Jayasheelan N,Jagannath B Kamath,S Zawadynski,Michel Kocher,S Sumam David +5 more
TL;DR: The work shows that a combination of cortical and cancellous features improves the diagnostic ability and is a promising low cost tool for early diagnosis of increased risk of osteoporosis.
Journal ArticleDOI
Current and Emerging Diagnostic Imaging-Based Techniques for Assessment of Osteoporosis and Fracture Risk
TL;DR: Early diagnosis of osteoporosis and prediction of fracture risk require the development of highly precise and accurate low-cost diagnostic techniques that would help the elderly population in low economies.
Proceedings ArticleDOI
Comparison of OMP and SOMP in the reconstruction of compressively sensed hyperspectral images
TL;DR: A novel method for the acquisition and compression of hyperspectral images based on two concepts - distributed source coding and compressive sensing is presented, showing that the Distributed Compressive Sensing method that exploits the joint sparsity of the hyperspectrals image is much better than individual recovery.