Palm Extraction in American Sign Language Gestures Using Segmentation and Skin Region Detection
Shivashankara S1, Srinath S2
1Shivashankara S, Department of Computer Science & Engineering, Sri Jayachamarajendra College of Engineering, Mysuru (Karnataka), India.
2Srinath, Department of Computer Science & Engineering, Sri Jayachamarajendra College of Engineering, Mysuru (Karnataka), India.
Manuscript received on 01 May 2019 | Revised Manuscript received on 15 May 2019 | Manuscript published on 30 May 2019 | PP: 2409-2418 | Volume-8 Issue-7, May 2019 | Retrieval Number: G1011058719/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: An outstanding to the revolution of science and technology, image-processing techniques are becoming very significant in an extensive range of Computer and Medical Applications. The Image Segmentation is a significant procedure of image processing operations. This research paper presents, an innovative framework, whose key aim is to achieve the extraction of Human Hand and Palm from the both plain and uniform background as well as complex and non-uniform background with various dissimilar lighting conditions of indoor and outdoor locations. Segmentation of an input gesture to extract the human hand and palm will be carried out by detecting upper body parts and face of an input gestures using Viola-Jones Algorithm and skin region detection technique. For segmentation, we will consider the 24 ASL Alphabets gestures and achieved 97.62% segmentation results. This paper also delivers the Mega Pixelwise (5, 8, and 13 MP) average segmentation accuracy with respect to various background, location, and time of gestures captures.
Keyword: Palm Extraction, Segmentation, Skin Region Detection, Threshold Value Viola-jones Algorithm.
Scope of the Article: DAgent Architectures, Ontologies, Languages and Protocols