Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Object detection is most required task in computer vision to meet the requirement of autonomous processes. The key challenge in object detection is to separate the foreground known as dominant information of the image from background. In this paper, a new model Squeeze and Excitation Region-Convolutional Neural Network (SER-CNN) is proposed which combines a Squeeze and Excitation (SE) embedded CNN with a Region proposal CNN (R-CNN) to detect salient objects. In SER-CNN, SE blocks are embedded in CNN and this extracts features made up of grand pixels which are accompanied with channel information. R-CNN generates salient map by fusing grand pixels with bounding box techniques to detect objects which is similar to the ground truth. DUT-OMRON, Pascal VOC and SOD dataset applied to find the performance of the SER-CNN. MAE and maxF performance evaluation measures are used for comparing the proposed work with the existing ones. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Department of Software and Systems Engineering

School of Information Technology and Engineering

Vellore Campus

Department of Multimedia

School of Design

International Journal of Speech Technology

Salient object detection based on adaptive recalibration technique through deep network

The virtual touch screen framework (VIRTOS) is intended to exertion in the space, whereas surrounding light environments can change. The VIRTOS consistently displays touch area, when a vast front area (non-foundation, a hand/ shadow) protects and breaks on the catch; the shade of anticipated touch catch is changed quickly with the end goal to recognize the shadow from the hand. Likewise VIRTOS bolsters the slider (scrollbar) in light of these virtual touch-catch instruments. The basic detraction procedure exists is utilized to fragment the frontal area. The reference image meant for each catch is utilized for basic detraction, is ceaselessly refreshed to represent changes in encompassing light. The experiments show that the ease of VIRTOS, is in it needs of two applications: for slide introductions by two virtual catches towards to explore the slides, and further is an activity diversion measured by the virtual slider effective. Maintenance of Virtos is really easy. Among all the benefits of Virtos, the most important thing is that we are using a solar panel and we know that solar energy is a truly renewable energy source. © 2020 IEEE.

World University root node

Department of Information Technology

Proceedings of International Conference on Computation, Automation and Knowledge Management, ICCAKM 2020

Augmenting Moving Planar Surfaces and Spatial Control of Interactive Surfaces Application through Virtual Touch Screen (VIRTOS)-A Case Study

Rapid developments in the advancing Deep Learning (DL) made significant progress in the methodologies for Automated Captioning. Automatic captioning for digital images or videos is a great challenge in Artificial intelligence. Though most algorithms used Convolution Neural networks (CNN), this work emphasize the use of Squeeze and Excitation (SE) technique with the Long Short- Term Memory (LSTM). This combination works well to generate the caption from a sequence of words based on the learning. This proposed work bridges the gap between visual and language system by combining the two vital methodologies for image caption. © 2019 Mattingley Publishing. All rights reserved.