Ten lectures on wavelets
Image Representation Using 2D Gabor Wavelets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wood inspection with non-supervised clustering
Machine Vision and Applications
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 1)-Volume 1 - Volume 1
Example Based Learning for View-Based Human Face Detection
Example Based Learning for View-Based Human Face Detection
Visual recognition of fastening bolts for railroad maintenance
Pattern Recognition Letters
Filter-based feature selection for rail defect detection
Machine Vision and Applications
Deformation visual inspection of industrial parts with image sequence
Machine Vision and Applications
Matching pursuit filters applied to face identification
IEEE Transactions on Image Processing
Video compression using matching pursuits
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper we focus on the problem of automatically detecting the absence of the fastening bolts that secure the rails to the sleepers. The proposed visual inspection system uses images acquired from a digital line scan camera installed under a train. The general performances of the system, in terms of speed and detection rate, are mainly influenced by the adopted features for representing images and by their number. In this paper we use overcomplete dictionaries of waveforms, called frames, which allow dense and sparse representations of images and analyze the performances of the system with respect to the sparsity of the representation. Sparse means a representation with only few no vanishing components. In particular we show that, in the case of Gabor dictionaries, dense representations provide the highest detection rate. Moreover, the number of no vanishing components of 1% of the total reduces of 10% the detection rate of the system, indicating that very sparse representations do not heavily influence the performances. We show the adopted techniques by using images acquired in real experimental conditions.