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3D object recognition technique using multiple 2D views for Arabic sign language |
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| Authors: | A Samir Elons Magdy Aboul-Ela MF Tolba |
| Affiliation: | 1. Scientific Computing Department, Faculty of Computer and Information Sciences , Ain Shams University , Cairo , Egypt ahmed.new80@hotmail.com;3. Sadat Academy for Management Sciences , Cairo , Egypt;4. Scientific Computing Department, Faculty of Computer and Information Sciences , Ain Shams University , Cairo , Egypt |
| Abstract: | Some objects in specific poses cannot be distinguished using a single view. A model is proposed and developed for 3D object recognition based on multiple-views; it was applied on hand postures recognition. A pulse-coupled neural network is used to generate features vector for single view. Two views with different view angles are used; each view generates its features’ vector. The two 2D-vectors are then linearly combined into one 3D vector. The hand postures are then combined to construct a dynamic gesture (word). The reconstruction is performed using best-match search algorithm. The experiment was conducted on 50 words and the result was 96% recognition accuracy confirming objects dataset offline extendibility. |
| Keywords: | pulse-coupled neural network (PCNN) static posture dynamic gesture Arabic sign language (ASL) 3D object recognition |