Improvement of a Parallel System for Image Processing

Digital images are digital signals captured through different means. Sometimes these captured images contain variations combined with the original signal, this variations are called noise. Echo is a particular kind of noise with characteristics that turns it into a very interesting problem to solve. The echo detection process and its subsequent elimination from a digital Image involves extensive mathematical calculations. Differents Parallel approaches taking advantage of new architectures can be implemented to solve this problem. Nevertheless these approaches have time depending characteristics, so the Processing time is still the critical point. One improved version of a Parallel one may be implemented by using a different algorithm and some other techniques that much more reduce the Processing time. In this work, the authors discuss their earlier work, the present approach and the future directions of this experimental application. Finally the resulting values are sketched.Sistemas Distribuidos - Redes ConcurrenciaRed de Universidades con Carreras en Informática (RedUNCI

Líneas de investigación en computer imagery

En la actualidad los gráficos se han convertido en uno de los medios de comunicación más naturales que existen. Esto se debe a la habilidad inherente de las personas de reconocer patrones en 2D y 3D que les permite percibir y procesar información de datos gráficos en forma rápida y eficiente. Paralelamente el uso de las computadoras ha crecido de manera que permite la creación, almacenamiento y manipulación de modelos e imágenes de objetos. Estos modelos provienen de una diversidad de campos tales como la física, matemática, ingeniería, arquitectura, fenómenos naturales, etc. En este contexto, las imágenes son potenciales herramientas para la toma de decisiones dado que permiten aumentar la información transmitida. Sin embargo, el crear y reproducir imágenes presenta problemas específicos a la manera en que estas pretenden utilizarse. El área de los gráficos por computadora (computer imagery) puede dividirse en tres grandes campos que interactúan entre sí: la computación gráfica, el procesamiento de imágenes y la visión por computadora. La computación gráfica se ocupa de la síntesis gráfica de objetos reales e imaginarios obtenidos a partir de modelos generados computacionalmente. El procesamiento de imágenes trata el análisis y manipulación de imágenes ya existentes; donde la nueva imagen generada es de alguna manera diferente a la imagen original. En particular, el análisis de imágenes es importante para áreas tales como la biomedicina, imágenes de reconocimiento aéreo, scan de cromosomas, etc. Esta rama posee sub-areas tales como: realce (enhancement) de imágenes, detección y reconocimiento de patrones, análisis de escenas, etc. Por último, el campo de visión por computadora se relaciona con la extracción de información a partir de una imagen (imágenes capturadas desde el 'ojo' de robots) para la reconstrucción de escenas en 3D a partir de modelos de 2D, intentando emular el sistema visual humano.Eje: Visualización - Computación GráficaRed de Universidades con Carreras en Informática (RedUNCI

Líneas de investigación en computer imagery

En la actualidad los gráficos se han convertido en uno de los medios de comunicación más naturales que existen. Esto se debe a la habilidad inherente de las personas de reconocer patrones en 2D y 3D que les permite percibir y procesar información de datos gráficos en forma rápida y eficiente. Paralelamente el uso de las computadoras ha crecido de manera que permite la creación, almacenamiento y manipulación de modelos e imágenes de objetos. Estos modelos provienen de una diversidad de campos tales como la física, matemática, ingeniería, arquitectura, fenómenos naturales, etc. En este contexto, las imágenes son potenciales herramientas para la toma de decisiones dado que permiten aumentar la información transmitida. Sin embargo, el crear y reproducir imágenes presenta problemas específicos a la manera en que estas pretenden utilizarse. El área de los gráficos por computadora (computer imagery) puede dividirse en tres grandes campos que interactúan entre sí: la computación gráfica, el procesamiento de imágenes y la visión por computadora. La computación gráfica se ocupa de la síntesis gráfica de objetos reales e imaginarios obtenidos a partir de modelos generados computacionalmente. El procesamiento de imágenes trata el análisis y manipulación de imágenes ya existentes; donde la nueva imagen generada es de alguna manera diferente a la imagen original. En particular, el análisis de imágenes es importante para áreas tales como la biomedicina, imágenes de reconocimiento aéreo, scan de cromosomas, etc. Esta rama posee sub-areas tales como: realce (enhancement) de imágenes, detección y reconocimiento de patrones, análisis de escenas, etc. Por último, el campo de visión por computadora se relaciona con la extracción de información a partir de una imagen (imágenes capturadas desde el 'ojo' de robots) para la reconstrucción de escenas en 3D a partir de modelos de 2D, intentando emular el sistema visual humano.Eje: Visualización - Computación GráficaRed de Universidades con Carreras en Informática (RedUNCI

Improvement of a Parallel System for Image Processing

Digital images are digital signals captured through different means. Sometimes these captured images contain variations combined with the original signal, this variations are called noise. Echo is a particular kind of noise with characteristics that turns it into a very interesting problem to solve. The echo detection process and its subsequent elimination from a digital Image involves extensive mathematical calculations. Differents Parallel approaches taking advantage of new architectures can be implemented to solve this problem. Nevertheless these approaches have time depending characteristics, so the Processing time is still the critical point. One improved version of a Parallel one may be implemented by using a different algorithm and some other techniques that much more reduce the Processing time. In this work, the authors discuss their earlier work, the present approach and the future directions of this experimental application. Finally the resulting values are sketched.Sistemas Distribuidos - Redes ConcurrenciaRed de Universidades con Carreras en Informática (RedUNCI

Automatic visual recognition using parallel machines

Invariant features and quick matching algorithms are two major concerns in the area of automatic visual recognition. The former reduces the size of an established model database, and the latter shortens the computation time. This dissertation, will discussed both line invariants under perspective projection and parallel implementation of a dynamic programming technique for shape recognition. The feasibility of using parallel machines can be demonstrated through the dramatically reduced time complexity. In this dissertation, our algorithms are implemented on the AP1000 MIMD parallel machines. For processing an object with a features, the time complexity of the proposed parallel algorithm is O(n), while that of a uniprocessor is O(n2). The two applications, one for shape matching and the other for chain-code extraction, are used in order to demonstrate the usefulness of our methods. Invariants from four general lines under perspective projection are also discussed in here. In contrast to the approach which uses the epipolar geometry, we investigate the invariants under isotropy subgroups. Theoretically speaking, two independent invariants can be found for four general lines in 3D space. In practice, we show how to obtain these two invariants from the projective images of four general lines without the need of camera calibration. A projective invariant recognition system based on a hypothesis-generation-testing scheme is run on the hypercube parallel architecture. Object recognition is achieved by matching the scene projective invariants to the model projective invariants, called transfer. Then a hypothesis-generation-testing scheme is implemented on the hypercube parallel architecture

FPGA implementations for parallel multidimensional filtering algorithms

PhD ThesisOne and multi dimensional raw data collections introduce noise and artifacts, which need to be recovered from degradations by an automated filtering system before, further machine analysis. The need for automating wide-ranged filtering applications necessitates the design of generic filtering architectures, together with the development of multidimensional and extensive convolution operators. Consequently, the aim of this thesis is to investigate the problem of automated construction of a generic parallel filtering system. Serving this goal, performance-efficient FPGA implementation architectures are developed to realize parallel one/multi-dimensional filtering algorithms. The proposed generic architectures provide a mechanism for fast FPGA prototyping of high performance computations to obtain efficiently implemented performance indices of area, speed, dynamic power, throughput and computation rates, as a complete package. These parallel filtering algorithms and their automated generic architectures tackle the major bottlenecks and limitations of existing multiprocessor systems in wordlength, input data segmentation, boundary conditions as well as inter-processor communications, in order to support high data throughput real-time applications of low-power architectures using a Xilinx Virtex-6 FPGA board. For one-dimensional raw signal filtering case, mathematical model and architectural development of the generalized parallel 1-D filtering algorithms are presented using the 1-D block filtering method. Five generic architectures are implemented on a Virtex-6 ML605 board, evaluated and compared. A complete set of results on area, speed, power, throughput and computation rates are obtained and discussed as performance indices for the 1-D convolution architectures. A successful application of parallel 1-D cross-correlation is demonstrated. For two dimensional greyscale/colour image processing cases, new parallel 2-D/3-D filtering algorithms are presented and mathematically modelled using input decimation and output image reconstruction by interpolation. Ten generic architectures are implemented on the Virtex-6 ML605 board, evaluated and compared. Key results on area, speed, power, throughput and computation rate are obtained and discussed as performance indices for the 2-D convolution architectures. 2-D image reconfigurable processors are developed and implemented using single, dual and quad MAC FIR units. 3-D Colour image processors are devised to act as 3-D colour filtering engines. A 2-D cross-correlator parallel engine is successfully developed as a parallel 2-D matched filtering algorithm for locating any MRI slice within a MRI data stack library. Twelve 3-D MRI filtering operators are plugged in and adapted to be suitable for biomedical imaging, including 3-D edge operators and 3-D noise smoothing operators. Since three dimensional greyscale/colour volumetric image applications are computationally intensive, a new parallel 3-D/4-D filtering algorithm is presented and mathematically modelled using volumetric data image segmentation by decimation and output reconstruction by interpolation, after simultaneously and independently performing 3-D filtering. Eight generic architectures are developed and implemented on the Virtex-6 board, including 3-D spatial and FFT convolution architectures. Fourteen 3-D MRI filtering operators are plugged and adapted for this particular biomedical imaging application, including 3-D edge operators and 3-D noise smoothing operators. Three successful applications are presented in 4-D colour MRI (fMRI) filtering processors, k-space MRI volume data filter and 3-D cross-correlator.IRAQI Government

An optimized hardware architecture and communication protocol for scheduled communication

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (p. 173-177).by David Shoemaker.Ph.D