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A machine cannot easily understand and interpret three-dimensional (3D) data. In this study, we propose the use of graph matching (GM) to enable 3D motion capture for Indian sign language recognition. The sign classification and recognition problem for interpreting 3D motion signs is considered an adaptive GM (AGM) problem. However, the current models for solving an AGM problem have two major drawbacks...
This works objective is to bring sign language closer to real time implementation on mobile platforms with a video database of Indian sign language created with a mobile front camera in selfie mode. Pre-filtering, segmentation and feature extraction on video frames creates a sign language feature space. Artificial Neural Network classifier on the sign feature space are trained with feed forward nets...
Medical ultrasound imaging has transformed the disease identification in the human body in the last few decades. The major setback for ultrasound medical images is speckle noise. Speckle noise is created in ultrasound images due to numerous reflections of ultrasound signals from hard tissues of human body. Speckle noise corrupts the medical ultrasound images dropping the detectable quality of the...
Hyperspectral face images present productive information captured using a Hyperspectral camera compared to normal RGB camera capturing face images. Hyper spectral imaging is the collecting and processing of information from across the visible electromagnetic spectrum. Hyper spectral imaging deals with the imaging of narrow spectral bands over a continuous visible spectral range, and produces the spectra...
This research paper is an attempt to create a video background independent sign language recognition (SLR) system. SLR acts as a Machine Interpreter (MI) between a mute person and normal person. One of the key difficulties in sign language recognition is video background of a sign video in which signer is located. Signer is extracted from cluttered back video backgrounds using boundary and prior shape...
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