License Plate Detection Using Neural Networks
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Kernel Based Multi-object Tracking Using Gabor Functions Embedded in a Region Covariance Matrix
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
Robust Car License Plate Localization Using a Novel Texture Descriptor
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
A kernel particle filter multi-object tracking using Gabor-based region covariance matrices
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Target tracking for mobile robot platforms via object matching and background anti-matching
Robotics and Autonomous Systems
Motion compensation algorithm based on color orientation codes and covariance matching
ICIRA'10 Proceedings of the Third international conference on Intelligent robotics and applications - Volume Part II
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We present a license plate detection algorithm that employs a novel image descriptor. Instead of using conventional gradient filters and intensity histograms, we compute a covariance matrix of low-level pixel-wise features within a given image window. Unlike the existing approaches, this matrix effectively captures both statistical and spatial properties within the window. We normalize the covariance matrix using local variance scores and restructure the unique coefficients into a feature vector form. Then, we feed these coefficients into a multi-layer neural network. Since no explicit similarity or distance computation is required in this framework, we are able to keep the computational load of the detection process low. To further accelerate the covariance matrix extraction process, we adapt an integral image based data propagation technique. Our extensive analysis shows that the detection process is robust against noise, illumination distortions, and rotation. In addition, the presented method does not require careful fine tuning of the decision boundaries.