Browsing Department of Computer Science and Engineering by Title
Now showing items 52-57 of 57
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Telescopic Vector Composition and Polar Accumulated Motion Residuals for Feature Extraction in Arabic Sign Language Recognition
(Springer, 2007)This work introduces two novel approaches for feature extraction applied to video-based Arabic sign language recognition, namely, motion representation through motion estimation and motion representation through motion ... -
Two-Stage Deep Learning Solution for Continuous Arabic Sign Language Recognition Using Word Count Prediction and Motion Images
(IEEE, 2023)Recognition of continuous sign language is challenging as the number of words is a sentence and their boundaries are unknown during the recognition stage. This work proposes a two-stage solution in which the number of words ... -
User-independent recognition of Arabic sign language for facilitating communication with the deaf community
(Elsevier, 2011)This paper presents a solution for user-independent recognition of isolated Arabic Sign language gestures. The video based gestures are preprocessed to segment out the hands of the signer based on color segmentation of the ... -
Using C++ to Calculate SO(10) Tensor Couplings
(MDPI, 2021-10-04)Model building in SO(10), which is the leading grand unification framework, often involves large Higgs representations and their couplings. Explicit calculations of such couplings is a multi-step process that involves ... -
Using Linear Regression and Back Propagation Neural Networks to Predict Performance of Soiled PV Modules
(Elsevier, 2019)This paper presents a study on neural network-based modeling techniques and sensor data to estimate the power output of photovoltaic systems under soiling conditions. Predicting maximum power output under soiling conditions ... -
Video-Based Recognition of Human Activity Using Novel Feature Extraction Techniques
(MDPI, 2023-06-05)This paper proposes a novel approach to activity recognition where videos are compressed using video coding to generate feature vectors based on compression variables. We propose to eliminate the temporal domain of feature ...