Recognition by Symmetry Derivatives and the Generalized Structure Tensor
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Image Quality Assessment with Application in Biometrics
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Multi-orientation analysis by decomposing the structure tensor and clustering
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
An adaptive level-selecting wavelet transform for texture defect detection
Image and Vision Computing
Bayesian image segmentation using local iso-intensity structural orientation
IEEE Transactions on Image Processing
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We propose a method for the assessment and visualization of high frequency regions of a multiresolution image. We combine both orientation tensor and multiresolution analysis to give a scalar descriptor of high frequency regions. High values of this scalar space indicate regions having coincident detail vectors in multiple scales of a wavelet decomposition. This is useful for finding edges, textures, collinear structures and salient regions for computer vision methods. The image is decomposed into several scales using the Discrete Wavelet Transform (DWT). The resulting detail spaces form vectors indicating intensity variations which are combined using orientation tensors. A high frequency scalar descriptor is then obtained from the resulting tensor for each original image pixel. Our results show that this descriptor indicates areas having relevant intensity variation in multiple scales.