Notice of Violation of IEEE Publication Principles: Hand gesture recognition framework for recognizing sign gestures and handling movement epenthesis using Level Building nested dynamic programming approach

Abstract

Notice of Violation of IEEE Publication Principles<br><br>“Hand Gesture Recognition Framework for Recognizing Sign Gestures and Handling Movement Epenthesis Using Level Building Nested Dynamic Programming Approach”<br>by Elakkiya A., Selvamani K., Kanimozhi S.<br>in the Proceedings of the IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE), May 2014<br><br>After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles.<br><br>This paper duplicated extensive amounts of text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. A. Elakkiya was solely responsible for the copied material.<br><br>Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:<br><br>“Enhanced Level Building Algorithm for the Movement Epenthesis Problem in Sign Language Recognition”<br>by Ruiduo Yang, Sudeep Sarkar, Barbara Loeding<br>in the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2007 <br/> In this research paper, two crucial problems in continuous sign language recognition from unaided video sequences are considered. At the feature level, the problem of hand segmentation and grouping is considered and at the sentence level, the movement epenthesis problem is considered. A framework that can handle both of these problems based on an enhanced, nested version of the dynamic programming approach is constructed. To handle movement epenthesis problem, a nested version of a dynamic programming framework, called Level Building is used to simultaneously segment and to match signs from continuous sign language sentences. This approach is then coupled with a trigram grammar model to optimally segment and label sign language sentences. This approach will show improvement over past approaches in terms of the frame labeling rate and also our approach shows the flexibility when handling a changing context. The proposed approach is novel since it does not need explicit any models for movement epenthesis.