218 research outputs found

    3D Shape Modeling Using High Level Descriptors

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    Deformation analysis and its application in image editing.

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    Jiang, Lei.Thesis (M.Phil.)--Chinese University of Hong Kong, 2011.Includes bibliographical references (p. 68-75).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 2 --- Background and Motivation --- p.5Chapter 2.1 --- Foreshortening --- p.5Chapter 2.1.1 --- Vanishing Point --- p.6Chapter 2.1.2 --- Metric Rectification --- p.8Chapter 2.2 --- Content Aware Image Resizing --- p.11Chapter 2.3 --- Texture Deformation --- p.15Chapter 2.3.1 --- Shape from texture --- p.16Chapter 2.3.2 --- Shape from lattice --- p.18Chapter 3 --- Resizing on Facade --- p.21Chapter 3.1 --- Introduction --- p.21Chapter 3.2 --- Related Work --- p.23Chapter 3.3 --- Algorithm --- p.24Chapter 3.3.1 --- Facade Detection --- p.25Chapter 3.3.2 --- Facade Resizing --- p.32Chapter 3.4 --- Results --- p.34Chapter 4 --- Cell Texture Editing --- p.42Chapter 4.1 --- Introduction --- p.42Chapter 4.2 --- Related Work --- p.44Chapter 4.3 --- Our Approach --- p.46Chapter 4.3.1 --- Cell Detection --- p.47Chapter 4.3.2 --- Local Affine Estimation --- p.49Chapter 4.3.3 --- Affine Transformation Field --- p.52Chapter 4.4 --- Photo Editing Applications --- p.55Chapter 4.5 --- Discussion --- p.58Chapter 5 --- Conclusion --- p.65Bibliography --- p.6

    Image interpolation using Shearlet based iterative refinement

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    This paper proposes an image interpolation algorithm exploiting sparse representation for natural images. It involves three main steps: (a) obtaining an initial estimate of the high resolution image using linear methods like FIR filtering, (b) promoting sparsity in a selected dictionary through iterative thresholding, and (c) extracting high frequency information from the approximation to refine the initial estimate. For the sparse modeling, a shearlet dictionary is chosen to yield a multiscale directional representation. The proposed algorithm is compared to several state-of-the-art methods to assess its objective as well as subjective performance. Compared to the cubic spline interpolation method, an average PSNR gain of around 0.8 dB is observed over a dataset of 200 images

    Anisotropic Diffusion Partial Differential Equations in Multi-Channel Image Processing : Framework and Applications

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    We review recent methods based on diffusion PDE's (Partial Differential Equations) for the purpose of multi-channel image regularization. Such methods have the ability to smooth multi-channel images anisotropically and can preserve then image contours while removing noise or other undesired local artifacts. We point out the pros and cons of the existing equations, providing at each time a local geometric interpretation of the corresponding processes. We focus then on an alternate and generic tensor-driven formulation, able to regularize images while specifically taking the curvatures of local image structures into account. This particular diffusion PDE variant is actually well suited for the preservation of thin structures and gives regularization results where important image features can be particularly well preserved compared to its competitors. A direct link between this curvature-preserving equation and a continuous formulation of the Line Integral Convolution technique (Cabral and Leedom, 1993) is demonstrated. It allows the design of a very fast and stable numerical scheme which implements the multi-valued regularization method by successive integrations of the pixel values along curved integral lines. Besides, the proposed implementation, based on a fourth-order Runge Kutta numerical integration, can be applied with a subpixel accuracy and preserves then thin image structures much better than classical finite-differences discretizations, usually chosen to implement PDE-based diffusions. We finally illustrate the efficiency of this diffusion PDE's for multi-channel image regularization - in terms of speed and visual quality - with various applications and results on color images, including image denoising, inpainting and edge-preserving interpolation

    Graph Spectral Image Processing

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    Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks). Though a digital image contains pixels that reside on a regularly sampled 2D grid, if one can design an appropriate underlying graph connecting pixels with weights that reflect the image structure, then one can interpret the image (or image patch) as a signal on a graph, and apply GSP tools for processing and analysis of the signal in graph spectral domain. In this article, we overview recent graph spectral techniques in GSP specifically for image / video processing. The topics covered include image compression, image restoration, image filtering and image segmentation

    HPatches: A benchmark and evaluation of handcrafted and learned local descriptors

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    In this paper, we propose a novel benchmark for evaluating local image descriptors. We demonstrate that the existing datasets and evaluation protocols do not specify unambiguously all aspects of evaluation, leading to ambiguities and inconsistencies in results reported in the literature. Furthermore, these datasets are nearly saturated due to the recent improvements in local descriptors obtained by learning them from large annotated datasets. Therefore, we introduce a new large dataset suitable for training and testing modern descriptors, together with strictly defined evaluation protocols in several tasks such as matching, retrieval and classification. This allows for more realistic, and thus more reliable comparisons in different application scenarios. We evaluate the performance of several state-of-the-art descriptors and analyse their properties. We show that a simple normalisation of traditional hand-crafted descriptors can boost their performance to the level of deep learning based descriptors within a realistic benchmarks evaluation
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