Informatics Publishing Limited

N. Venkatram ¹, L. S. S. Reddy ², P. V. V. Kishore ³

Affiliations
1 KL University, ECM Dept., Green Fields, Vaddeswaram, Guntur DT, IN
2 KL University, CSE Department, IN
3 ECE Department, KL University, IN

Abstract

Medical image watermarking (MIW) has challenged researchers for over a decade now. Medical images are difficult to watermark as they contain sensitive information of a patient’s disease. During transportation through internet the medical images are subjected to various attacks. For telemedicine applications it becomes vital to prevent the authenticity regarding patient’s records. A two dimensional discrete wavelet transform based approach is designed and tested to embed a patient passport photograph into his or her medical image. This method computes 2D DWT at various levels and mixes the approximate coefficients using a wave coupling coefficient. The proposed watermarking algorithm is tested for available mother wavelets and around five levels of decomposition with multiple coupling coefficients. Peak Signal-to-Noise ratio, Minimum Mean Square Error and Normalized cross correlation coefficient are computed to identify the best wavelet and the level of decomposition that can significantly be applied to medical image watermarking. The results demonstrate that db2 at level 4 is the top performing wavelet.

Keywords

Medical Image Watermarking, 2D Discrete Wavelet Transform, Wave Coupling Coefficient, Peak Signal-To-Noise Ratio, and Minimum Mean Square Error.

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P. V. V. Kishore ¹, P. Rajesh Kumar ², A. Arjuna Rao ³

Affiliations
1 Andhra University, College of Engineering, Visakhapatnam, Andhra Pradesh, IN
2 Department of ECE, Andhra University, College of Engineering, Visakhapatnam, AP, IN
3 Miracle Educational Society Group of Institutions, Boghapuram, Vizianagaram, Andhra Pradesh, IN

Abstract

The paper presents a system developed for recognizing gestures of Indian sign language from images of gestures. The proposed system is based on Elliptical Fourier descriptors and neural networks used for gesture pattern recognition. Unlike the systems proposed by other researchers such as using a radio frequency or colored gloves to achieve the recognition our system does not impose any such constraints. Features are extracted from the videos of signers using elliptical Fourier descriptors and principal component analysis which greatly reduces the size of the feature vector. Neural networks error back propagation algorithm is used to recognize gestures Indian sign language. The system converts the recognized gesture in to voice and text messages. The system was implemented with 440 sample videos of gestures of alphanumeric characters and words with a maximum of 5 videos per gesture. Experimental results show that the neural network is able to recognize gestures and convert them to voice messages with an accuracy of 92.52%.

Keywords

Sign Language Recognition, Artificial Neural Networks, Elliptical Fourier Descriptors, Canny Edge Detector.

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M. V. D. Prasad ¹, P. V. V. Kishore ¹, D. Anil Kumar ¹, Ch. Raghava Prasad ¹

Affiliations
1 Electronic and Communication Department, K L University, Guntur, Andra Pradesh, IN

Abstract

Objectives: Fuzzy classifying of continuous sign language videos with simple backgrounds with tracking and shape combined features is the focus of this work. Methods/Analysis: Tracking and capturing hand position vectors is the artwork of horn schunck optical flow algorithm. Active contours extract shape features from sign frames in the video sequence. The two most dominant features of sign language are combined to build sign features. This feature matrix is the training vector for Fuzzy Inference Engine (FIS). The classifier is tested with 50 signs in a video sequence. Ten different signers created 50 signs. Different instances of FIS are tested with different combination of feature vectors. The results are compared to our previous work using no tracking and with discrete sign language database. Findings: A Word Matching Scores (WMS) gauges the performance of the classifiers. A 92.5% average matching score is reported in this work. A through comparison for FIS gesture classifier between Discrete Cosine. Novelty/Improvement: Transform features, Elliptical Fourier descriptor features and the proposed hybrid features for continuous sign language videos show a 40% jump in word matching score.

Keywords

Active Contour Shape Analysis, Continuous Sign Language, Fuzzy Inference Engine, Hybrid Feature Vector, Optical Flow Tracking.

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K. Kade Mahesh ¹, P. V. V. Kishore ¹, J. Karande Kailash ²

Affiliations
1 Department of Electronics and Communication Engineering, KL University, Vijaywada – 522502, Andhra Pradesh, IN
2 Department of Electronics and Telecommunication Engineering, SKNSCOE, Korti, Solapur Univeristy, Solapur – 413304, Maharashtra, IN

Abstract

Objectives: Iris recognition is one of biometric identification methods adopted over worldwide. In this paper we intend to update the previous survey and cover the survey over the period of roughly 2010 to 2015. Methods: We focus on the paper that appeared in Springer, IEEE Xplore and International Conferences, National and International journals covering Image Processing, Signal Processing, Pattern recognition and Bioinformatics. This paper primarily focuses on the survey of Iris camera for Iris acquisition, Methods adopted for iris segmentation, feature extraction, matching and public Iris database. Iris segmentation and feature extraction are important steps in Iris recognition. As there are several publications on the segmentation and feature extraction separately in literature, we have selected and summarized only prominent work in our paper. Findings: We have compared the algorithm used by various researchers with the performance parameter obtained by other researchers. We have found that there is scope for improvement in algorithms and need to understand the Iris Code in detail. Application: This comparative analysis will help researcher to get view on present scenario related to Iris recognition system.

Keywords

Acquisition Segmentation, Database, Features, Iris, Matching.

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