R
R. Sudhakar
Researcher at Dr. Mahalingam College of Engineering and Technology
Publications - 57
Citations - 298
R. Sudhakar is an academic researcher from Dr. Mahalingam College of Engineering and Technology. The author has contributed to research in topics: Image compression & Wavelet transform. The author has an hindex of 8, co-authored 52 publications receiving 228 citations. Previous affiliations of R. Sudhakar include PSG College of Technology & Anna University.
Papers
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Journal ArticleDOI
Biometric recognition using finger and palm vein images
S. Bharathi,R. Sudhakar +1 more
TL;DR: A multimodal biometric system using vascular patterns of the hand such as finger vein and palm vein images, which provides lower false acceptance rate, false rejection rate and high accuracy when compared with the existing techniques, indicating the effectiveness of the proposed system.
Book ChapterDOI
Automatic Classification of Liver Diseases from Ultrasound Images Using GLRLM Texture Features
K. Raghesh Krishnan,R. Sudhakar +1 more
TL;DR: In this paper, the effect of various linear, non linear and diffusion filters in improving the quality of the liver ultrasound images before proceeding to the subsequent phases of feature extraction and classification using Gray Level Run Length Matrix Features and Support Vector Machines respectively.
Journal Article
Fingerprint Compression Using Contourlet Transform and Multistage Vector Quantization
TL;DR: This paper presents a new fingerprint coding technique based on contourlet transform and multistage vector quantization, which is a new extension to the wavelet transform in two dimensions using nonseparable and directional filter banks.
Journal Article
Fingerprint Compression Using Contourlet Transform with Modified SPIHT Algorithm
TL;DR: This paper focuses mainly on the new fingerprint compression using contourlet transform (CT), which includes elaborated repositioning algorithm for the CT coefficients, and Modified set partitioning in hierarchical trees (SPIHT) which is applied to get better quality, i.e., high peak signal to noise ratio (PSNR).
Journal ArticleDOI
A modified salp swarm algorithm (SSA) combined with a chaotic coupled map lattices (CML) approach for the secured encryption and compression of medical images during data transmission
TL;DR: In this paper, a combined coupled map lattice (CML) with modified salp swarm algorithm is proposed to encrypt the medical image and the experimental outcomes demonstrate that the proposed technique is more efficient and has capability to resist the different distinct attacks.