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An edge detection is important for its reliability and security which delivers a better understanding of object recognition in the applications of computer vision, such as pedestrian detection, face detection, and video surveillance. This paper introduced two fundamental limitations encountered in edge detection: edge connectivity and edge thickness, those have been used by various developments in the state-of-theart. An optimal selection of the threshold for effectual edge detection has constantly been a key challenge in computer vision. Therefore, a robust edge detection algorithm using multiple threshold approaches (B-Edge) is proposed to cover both the limitations. The majorly used canny edge operator focuses on two thresholds selections and still witnesses a few gaps for optimal results. To handle the loopholes of the canny edge operator, our method selects the simulated triple thresholds that target to the prime issues of the edge detection: image contrast, effective edge pixels selection, errors handling, and similarity to the ground truth. The qualitative and quantitative experimental evaluations demonstrate that our edge detection method outperforms competing algorithms for mentioned issues. The proposed approach endeavors an improvement for both grayscale and colored images.INDEX TERMS Edge, edge connectivity, edge detection, edge width uniformity, threshold.SUDIPTA ROY received the Ph.D. degree in computer science and engineering from the Department of Computer Science and Engineering, University of Calcutta. He is currently with the Radiological Chemistry and Imaging Laboratory, Washington University in Saint Louis, USA. He has more than five years of experience in teaching and research. He is an author of more than 30 publications in refereed national and international journals and conferences, including the IEEE, Springer, Elsevier, and many others. He is an author of one book and many book chapters. He holds an U.S. patent in medical image processing and filed an Indian patent in smart agricultural system. His research interests include biomedical image analysis, image processing, steganography, artificial intelligence, big data analysis, machine learning, and big data technologies.

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Communication among several internet users has become more convenient through social networking sites to where each user sharing his own opinions on different matters, such as Healthcare, Education, marketing etc. The Objective of this paper is to present a method to make it easier for even a layman to predict and analyze one's health issues on his own by making use of tweets on the social website twitter.com. As far as methodology or techniques is concerned, an algorithm has been framed for the same to perform the analysis on health care tweets with association rules to classify the ailments and their symptoms using a corpus through fuzzy set and two step approach for Document Term Matrix & Term Document Matrix. The results demonstrate the comparison of different terms over the WordCloud which concludes that in this novel approach of two step authentication the average accuracy of association between the hiv ailments is 98% through correlation table and association between the HIV ailments with 98% correlation.

Digital image watermarking aims to protect the information in an image without significantly affecting visual quality. In this paper, a new image watermarking technique has been proposed that uses Gaussian filters and first-order partial differential matrix to extort the edge surface of a host image. This paper influence on the edge surface curvelet coefficients as human eyes are not equally sensitive to a smooth and an edged surface. To preserve the quality of the artwork and to increase the resistance against attacks, the author utilizes the edge surface area of an image, coarse levels of curvelet transform, and strength parameters. The selection of host coefficients are conforming to the human visual system (HVS) is the uniqueness of the research. The exploitation of the Gaussian filters and first-order partial differential coarse curvelet coefficients and the watermark strength parameter offers robustness against image processing attacks. The standard visual quality perception of HVS evaluation metrics are used to measure the superiority of the presented work.