Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling
International Journal of Computer Vision
Digital Image Processing: PIKS Inside
Digital Image Processing: PIKS Inside
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Handbook of Computer Vision Algorithms in Image Algebra
Handbook of Computer Vision Algorithms in Image Algebra
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
The evaluation of normalized cross correlations for defect detection
Pattern Recognition Letters
Intrinsic Hardware Evolution of Neural Networks in Reconfigurable Analogue and Digital Devices
FCCM '06 Proceedings of the 14th Annual IEEE Symposium on Field-Programmable Custom Computing Machines
Minimax Entropy Principle and Its Application to Texture Modeling
Neural Computation
Improving the Efficiency of Counting Defects by Learning RBF Nets with MAD Loss
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
MAD Loss in Pattern Recognition and RBF Learning
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Local Detection Of Defects From Image Sequences
International Journal of Applied Mathematics and Computer Science - Issues in Fault Diagnosis and Fault Tolerant Control
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The aim of this paper is to propose two methods of detecting defects in industrial products by an analysis of gray level images with low contrast between the defects and their background. An additional difficulty is the high nonuniformity of the background in different parts of the same image. The first method is based on correlating subimages with a nondefective reference subimage and searching for pixels with low correlation. To speed up calculations, correlations are replaced by a map of locally computed inner products. The second approach does not require a reference subimage and is based on estimating local entropies and searching for areas with maximum entropy. A nonparametric estimator of local entropy is also proposed, together with its realization as a bank of RBF neural networks. The performance of both methods is illustrated with an industrial image.