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The geometry of signal and image patch-sets
, 2011
"... The final copy of this thesis has been examined by the signatories, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the above mentioned discipline. iii Taylor, K. M. (Ph.D., Applied Mathematics) The geometry of signal and image patch-sets ..."
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The final copy of this thesis has been examined by the signatories, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the above mentioned discipline. iii Taylor, K. M. (Ph.D., Applied Mathematics) The geometry of signal and image patch-sets
Texture Splicing
"... We propose a new texture editing operation called texture splicing. For this operation, we regard a texture as having repetitive elements (textons) seamlessly distributed in a particular pattern. Taking two textures as input, texture splicing generates a new texture by selecting the texton appearanc ..."
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We propose a new texture editing operation called texture splicing. For this operation, we regard a texture as having repetitive elements (textons) seamlessly distributed in a particular pattern. Taking two textures as input, texture splicing generates a new texture by selecting the texton appearance from one texture and distribution from the other. Texture splicing involves self-similarity search to extract the distribution, distribution warping, contextdependent warping, and finally, texture refinement to preserve overall appearance. We show a variety of results to illustrate this operation. Categories and Subject Descriptors (according to ACM CCS): I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism—Texture I.4.7 [Image Processing and Computer Vision]: Feature Measurement—Texture 1.
SIAM J. IMAGING SCIENCES c © 2012 Society for Industrial and Applied Mathematics In press.
"... random walk on image patches ∗ ..."
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Creating Texture Exemplars from Unconstrained Images
, 2013
"... Texture is an essential feature in modeling the appearance of ob-jects and is instrumental in making virtual objects appear interesting and/or realistic. Unfortunately, obtaining textures is a labor intensive task requiring parameter tuning for procedural methods or careful pho-tography and post-pro ..."
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Texture is an essential feature in modeling the appearance of ob-jects and is instrumental in making virtual objects appear interesting and/or realistic. Unfortunately, obtaining textures is a labor intensive task requiring parameter tuning for procedural methods or careful pho-tography and post-processing for natural images. Many texture syn-thesis techniques have been developed to generate textures of arbitrary spatial extent, but these techniques require the user to first produce an exemplar consisting solely of the desired texture. We present a fast method using diffusion manifolds to locate textures in unconstrained photographs, and extract exemplar tiles. The method requires the user to only specify a single point within the image containing the desired texture and the scale of the desired texture. The user may tune the result using simple interactions. The method is non-local, in the sense that the desired texture does not have to appear in a single contiguous region in the source image. Supplemental material and an interactive demonstration is available from the paper’s companion website [1]. Figure 1: A photograph, sample, tiles extracted from the photograph and texture synthesis from the texture tile sets. 1 1
Robust Learning from Ortho-Diffusion Decompositions
"... Abstract. This paper describes a new classification method based on modeling data by embedding diffusions into orthonormal decompositions of graph-based data representations. The training data is represented by an adjacency matrix calculated using either the correlation or the covari-ance of the tra ..."
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Abstract. This paper describes a new classification method based on modeling data by embedding diffusions into orthonormal decompositions of graph-based data representations. The training data is represented by an adjacency matrix calculated using either the correlation or the covari-ance of the training set. The application of the modified Gram-Schmidt orthonormal decomposition alternating with diffusion and data reduction stages, is applied recursively at each scale level. The diffusion process is strengthening the representation pattern of representative features. Meanwhile, noise is removed together with non-essential detail during the data reduction stage. The proposed methodology is shown to be robust when applied to face recognition considering low image resolution and corruption by various types of noise.
