Journal ArticleDOI
A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
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TLDR
A universal statistical model for texture images in the context of an overcomplete complex wavelet transform is presented, demonstrating the necessity of subgroups of the parameter set by showing examples of texture synthesis that fail when those parameters are removed from the set.Abstract:
We present a universal statistical model for texture images in the context of an overcomplete complex wavelet transform. The model is parameterized by a set of statistics computed on pairs of coefficients corresponding to basis functions at adjacent spatial locations, orientations, and scales. We develop an efficient algorithm for synthesizing random images subject to these constraints, by iteratively projecting onto the set of images satisfying each constraint, and we use this to test the perceptual validity of the model. In particular, we demonstrate the necessity of subgroups of the parameter set by showing examples of texture synthesis that fail when those parameters are removed from the set. We also demonstrate the power of our model by successfully synthesizing examples drawn from a diverse collection of artificial and natural textures.read more
Citations
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Patent
System and method for encoding and decoding using texture replacement
TL;DR: In this article, the authors proposed a method to synthesize texture based on the received texture information, which is then decoded to obtain a decoded image, and then map the synthesized texture onto the decoded images.
Journal ArticleDOI
Depth-image-based rendering with spatial and temporal texture synthesis for 3DTV
TL;DR: A depth-image-based rendering (DIBR) method with spatial and temporal texture synthesis is presented, which combines the temporally stationary scene information extracted from the input video and spatial texture in the current frame to fill the disoccluded areas in the virtual views.
Proceedings ArticleDOI
Locally adaptive multiscale contrast optimization
TL;DR: A method for automatically and adaptively boosting the visibility of local features in an image using a scale-invariant spectral model and a spatial mask is applied in the pixel domain to ensure that the enhancements are applied only in the vicinity of image features.
Book ChapterDOI
Auto Localization and Segmentation of Occluded Vessels in Robot-Assisted Partial Nephrectomy
Alborz Amir-Khalili,Jean-Marc Peyrat,Julien Abinahed,Osama Al-Alao,Abdulla Al-Ansari,Abdulla Al-Ansari,Ghassan Hamarneh,Rafeef Abugharbieh +7 more
TL;DR: This work proposes an automatic method to localize and label occluded vasculature and assigns segmentation labels based on identifying responses of regions exhibiting temporal local phase changes matching the heart rate frequency from phase-based video magnification.
Proceedings ArticleDOI
Learning to Warp for Style Transfer
TL;DR: In this article, a neural network is proposed to learn a mapping from a 4D array of inter-feature distances to a non-parametric 2D warp field, which can be used with a single style exemplar.
References
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Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
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Information Theory and Statistical Mechanics. II
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Journal ArticleDOI
Orthonormal bases of compactly supported wavelets
TL;DR: This work construct orthonormal bases of compactly supported wavelets, with arbitrarily high regularity, by reviewing the concept of multiresolution analysis as well as several algorithms in vision decomposition and reconstruction.
Journal ArticleDOI
Relations between the statistics of natural images and the response properties of cortical cells.
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