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The environmental concern is on rise in all types of business; however, banking assumes a special niche due to its ability to influence the economic growth and development of the country. The present study proposes conceptual model of Green banking initiatives and studies the impact of three Green banking initiatives, viz. green products development, green corporate social responsibility and green internal process on two possible outcomes, viz. Green brand image and Green trust. The study is qualitative in nature comprising of semistructured in-depth interviews conducted with 36 middle- to senior-level managers of twelve public and private Indian banks. Banking sector can play a crucial role in greening the banking system by enhancing the availability of finance and serve the needs of a “green economy”. The findings of the study revealed that 63% of the total respondents were of view that their bank indulges in development of several green banking products and services, 53% of the bankers said that their bank incorporates green internal processes in their daily activities, and 78% respondents said that their bank undertakes several green corporate social responsibility initiatives. This investigation further highlights that more than 60% respondents believed that Green banking initiatives have positive role in restoring customer trust through enhanced Green brand image. With dearth of studies on green banking in India, the present qualitative study contributes to the body of knowledge and paves way for future research in green banking for sustainable development.

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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