A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multichannel Texture Analysis Using Localized Spatial Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pyramid-based texture analysis/synthesis
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling
International Journal of Computer Vision
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
A non-parametric multi-scale statistical model for natural images
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Recognition without Correspondence using MultidimensionalReceptive Field Histograms
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Universal Analytical Forms for Modeling Image Probabilities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classifying Images of Materials: Achieving Viewpoint and Illumination Independence
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Texture Features and Learning Similarity
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Histogram Model for 3D Textures
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Recognizing Surfaces Using Three-Dimensional Textons
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Image indexing using moments and wavelets
IEEE Transactions on Consumer Electronics
The curvelet transform for image denoising
IEEE Transactions on Image Processing
Texture classification using spectral histograms
IEEE Transactions on Image Processing
| Hi-index | 0.14 |
A commonly used representation of a visual pattern is a statistical distribution measured from the output of a bank of filters (Gaussian, Laplacian, Gabor, etc.). Both marginal and joint distributions of filter responses have been advocated and effectively used for a variety of vision tasks, including texture classification, texture synthesis, object detection, and image retrieval. This paper examines the ability of these representations to discriminate between an arbitrary pair of visual stimuli. Examples of patterns are derived that provably possess the same marginal and joint statistical properties, yet are "visually distinct.驴 This is accomplished by showing sufficient conditions for matching the first k moments of the marginal distributions of a pair of images. Then, given a set of filters, we show how to match the marginal statistics of the subband images formed through convolution with the filter set. Next, joint statistics are examined and images with similar joint distributions of subband responses are shown. Finally, distinct periodic patterns are derived that possess approximately the same subband statistics for any arbitrary filter set.