Single Frame Image super Resolution using Learned Directionlets

In this paper, a new directionally adaptive, learning based, single image super resolution method using multiple direction wavelet transform, called Directionlets is presented. This method uses directionlets to effectively capture directional features and to extract edge information along different directions of a set of available high resolution images .This information is used as the training set for super resolving a low resolution input image and the Directionlet coefficients at finer scales of its high-resolution image are learned locally from this training set and the inverse Directionlet transform recovers the super-resolved high resolution image. The simulation results showed that the proposed approach outperforms standard interpolation techniques like Cubic spline interpolation as well as standard Wavelet-based learning, both visually and in terms of the mean squared error (mse) values. This method gives good result with aliased images also.Comment: 14 pages,6 figure

Fast space-variant elliptical filtering using box splines

The efficient realization of linear space-variant (non-convolution) filters is a challenging computational problem in image processing. In this paper, we demonstrate that it is possible to filter an image with a Gaussian-like elliptic window of varying size, elongation and orientation using a fixed number of computations per pixel. The associated algorithm, which is based on a family of smooth compactly supported piecewise polynomials, the radially-uniform box splines, is realized using pre-integration and local finite-differences. The radially-uniform box splines are constructed through the repeated convolution of a fixed number of box distributions, which have been suitably scaled and distributed radially in an uniform fashion. The attractive features of these box splines are their asymptotic behavior, their simple covariance structure, and their quasi-separability. They converge to Gaussians with the increase of their order, and are used to approximate anisotropic Gaussians of varying covariance simply by controlling the scales of the constituent box distributions. Based on the second feature, we develop a technique for continuously controlling the size, elongation and orientation of these Gaussian-like functions. Finally, the quasi-separable structure, along with a certain scaling property of box distributions, is used to efficiently realize the associated space-variant elliptical filtering, which requires O(1) computations per pixel irrespective of the shape and size of the filter.Comment: 12 figures; IEEE Transactions on Image Processing, vol. 19, 201

Métodos matemáticos e computacionais para modelagem e edição de deformações

Orientador: Jorge StolfiTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Nesta tese, descrevemos primeiramente o algoritmo ECLES (Editing by Constrained LEast Squares), um método geral para edição interativa de objetos definidos por parâmetros sujeitos a restrições lineares ou afins. Neste método, as restrições e as ações de edição do usuário são combinadas usando mínimos quadrados restritos, ao invés da abordagem mais comum de elementos finitos. Usamos aritmética exata para detectar e eliminar redundâncias no conjunto de restrições e evitar falhas devido a erros de arredondamento. O algoritmo ECLES tem diversas aplicações. Entre elas, podemos citar a edição de deformações spline com continuidade C¹. Nesta tese, descrevemos um método interativo de edição de deformações do plano, o algoritmo 2DSD (2D Spline Deformation). As deformações são definidas por splines de grau 5 sobre uma malha triangular arbitrária. Estas deformações são editadas alterando-se as posições dos pontos de controle da malha. O algoritmo ECLES é usado em cada ação de edição do usuário para detectar, de forma robusta e eficiente, o conjunto de restrições de continuidade C¹ que são relevantes, garantindo que não existam redundâncias. Em seguida, como os parâmetros são modificados pelo usuário, o ECLES é chamado para calcular as novas posições dos pontos de controle satisfazendo as restrições e as posições especificadas pelo usuário. A fim de validar nosso método 2DSD, ele foi utilizado como parte de um editor interativo para deformações do espaço 2.5D, o editor PrisMystic. Este editor foi utilizado, principalmente, para deformar modelos tridimensionais de organismos microscópicos não-rígidos de modo a coincidir com imagens reais de microscopia ótica. Também utilizamos o editor para editar modelos de terrenosAbstract: In this thesis, we present the ECLES algorithm (Editing by Constrained LEast Squares), a general method for interactive editing of objects that are defined by parameters subject to linear or affine constraints. In this method, the constraints and the user editing actions are combined using constrained least squares instead of the usual finite element approach. We use exact integer arithmetic in order to detect and eliminate redundancies in the set of constraints and to avoid failures due to rounding errors. The ECLES algorithm has various applications. Among them, we can cite the editing of C¹-continuous spline deformations. In this thesis, we describe an interactive editing method for deformations of the plane, the 2DSD algorithm (2D Spline Deformation). The deformations are defined by splines of degree 5 on an arbitrary triangular mesh. The deformations are edited by changing the positions of its control points. The ECLES algorithm is first used in each user editing action in order to detect, in a robust and efficient way, the set of relevant constraints of C¹ continuity, ensuring that there are no redundancies. Then, as the parameters are changed by the user, ECLES is called to compute the new positions of the control points satisfying the constraints and the positions specified by the user. To validate our 2DSD algorithm, we used it as part of an interactive editor for 2.5D space deformations, the PrisMystic editor. This editor has been used, mainly, to deform 3D models of non-rigid living microscopic organisms as seen in actual optical microscope images. We also used the editor to edit terrain modelsDoutoradoCiência da ComputaçãoDoutora em Ciência da Computação140780/2013-001-P-04554-2013CNPQCAPE

Project SEMACODE : a scale-invariant object recognition system for content-based queries in image databases

For the efficient management of large image databases, the automated characterization of images and the usage of that characterization for searching and ordering tasks is highly desirable. The purpose of the project SEMACODE is to combine the still unsolved problem of content-oriented characterization of images with scale-invariant object recognition and modelbased compression methods. To achieve this goal, existing techniques as well as new concepts related to pattern matching, image encoding, and image compression are examined. The resulting methods are integrated in a common framework with the aid of a content-oriented conception. For the application, an image database at the library of the university of Frankfurt/Main (StUB; about 60000 images), the required operations are developed. The search and query interfaces are defined in close cooperation with the StUB project “Digitized Colonial Picture Library”. This report describes the fundamentals and first results of the image encoding and object recognition algorithms developed within the scope of the project

An Energy-Efficient Hardware Implementation of HOG-Based Object Detection at 1080HD 60 fps with Multi-Scale Support

A real-time and energy-efficient multi-scale object detector hardware implementation is presented in this paper. Detection is done using Histogram of Oriented Gradients (HOG) features and Support Vector Machine (SVM) classification. Multi-scale detection is essential for robust and practical applications to detect objects of different sizes. Parallel detectors with balanced workload are used to increase the throughput, enabling voltage scaling and energy consumption reduction. Image pre-processing is also introduced to further reduce power and area costs of the image scales generation. This design can operate on high definition 1080HD video at 60 fps in real-time with a clock rate of 270 MHz, and consumes 45.3 mW (0.36 nJ/pixel) based on post-layout simulations. The ASIC has an area of 490 kgates and 0.538 Mbit on-chip memory in a 45 nm SOI CMOS process.Texas Instruments IncorporatedUnited States. Defense Advanced Research Projects Agency (Young Faculty Award Grant N66001-14-1-4039