A proposed PCNN features quality optimization technique for pose-invariant 3D Arabic sign language recognition

Authors:
A. Samir Elons;Magdy Abull-Ela;M. F. Tolba
Affiliations:
Scientific Computing Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt;Sadat Academy for Management Sciences, Cairo, Egypt;Scientific Computing Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt
Venue:
Applied Soft Computing
Year:
2013

Citing 23
Cited 0

Recognition by Linear Combinations of Models

IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Space and Time Bounds on Indexing 3D Models from 2D Images

IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Wrappers for feature subset selection

Artificial Intelligence - Special issue on relevance
A dynamical theory of spike train transitions in networks of integrate-and-fire oscillators

SIAM Journal on Applied Mathematics
Probability Models for Clutter in Natural Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
Active Appearance Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of gestures in Arabic sign language using neuro-fuzzy systems

Artificial Intelligence
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

Journal of Global Optimization
Learning the Topology of Object Views

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Relevant Features for Video-Based Continuous Sign Language Recognition

FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Hidden Markov Model Based Continuous Online Gesture Recognition

ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy

IEEE Transactions on Pattern Analysis and Machine Intelligence
Sign Recognition using Depth Image Streams

FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Pose-Invariant 3D Object Recognition Using Linear Combination of 2D Views and Evolutionary Optimisation

ICCTA '07 Proceedings of the International Conference on Computing: Theory and Applications
Recognition of Arabic sign language alphabet using polynomial classifiers

EURASIP Journal on Applied Signal Processing
Eigenfaces for recognition

Journal of Cognitive Neuroscience
Arabic sign language recognition using neural network and graph matching techniques

AIC'06 Proceedings of the 6th WSEAS International Conference on Applied Informatics and Communications
A modified pulse coupled neural network for shortest-path problem

Neurocomputing
Depth-encoded hough voting for joint object detection and shape recovery

ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
3D Object recognition using a voting algorithm in a real-world environment

WACV '11 Proceedings of the 2011 IEEE Workshop on Applications of Computer Vision (WACV)
Robust person-independent visual sign language recognition

IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
Pulse-coupled neural networks for contour and motion matchings

IEEE Transactions on Neural Networks
Cortical 3D Face and Object Recognition Using 2D Projections

International Journal of Creative Interfaces and Computer Graphics

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a novel technique to deal with pose variations in 3D object recognition. This technique uses pulse-coupled neural network (PCNN) for image features generation from two different viewing angles. These signatures qualities are then evaluated, using a proposed fitness function. The features evaluation step is taken before any classification steps are performed. The evaluation results dynamic weighting factors for each camera express the features quality from the current viewing angles. The proposed technique uses the two 2D image features and their corresponding calculated weighting factors to construct optimized quality 3D features. An experiment was conducted in Arabic sign language recognition application which multiple views are necessary to distinguish some signs. The proposed technique obtained a 96% recognition accuracy for pose-invariant restrictions with a degree of freedom from 0 to 90.