image registration is a key pre-procedure for high level imageprocessing. However, taking into consideration the complexity and accuracy of the algorithm, the image registration algorithm always has high time complex...
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image registration is a key pre-procedure for high level imageprocessing. However, taking into consideration the complexity and accuracy of the algorithm, the image registration algorithm always has high time complexity. To speed up the registration algorithm, parallel computation is a relevant strategy. parallelizing the algorithm by implementing Lattice Boltzmann method (LBM) seems a good candidate. In consequence, this paper proposes a novel parallel LBM based model (LB model) for image registration. The main idea of our method consists in simulating the convection diffusion equation through a LB model with an ad hoc collision term. By applying our method on computed tomography angiography images (CTA images), Magnet Resonance images (MR images), natural scene image and artificial images, our model proves to be faster than classical methods and achieves accurate registration. In the continuity of 2D image registration model, the LB model is extended to 3D volume registration providing excellent results in domain such as medical imaging. Our method can run on massively parallel architectures, ranging from embedded field programmable gate arrays (FPGAs) and digital signal processors (DSPs) up to graphics processing units (GPUs).
A compact parallel image processing system concept has been developed. The main features of this system is the use of off-axis paraboloidal mirror segments as collimating, Fourier transforming and image reconstructing...
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A compact parallel image processing system concept has been developed. The main features of this system is the use of off-axis paraboloidal mirror segments as collimating, Fourier transforming and image reconstructing elements, and the use of a GaAs laser diode as the coherent radiation source. Preliminary experiments to demonstrate the usefulness of this system have been performed.
作者：
Vorontsov, MAUSA
Res Lab Informat Sci & Technol Directorate Adelphi MD 20783 USA
Here we discuss new potential applications in parallel image processing and adaptive optics for nonlinear spatio-temporal processes occurring in optical (opto-electronic) nonlinear two-dimensional feedback systems.
Here we discuss new potential applications in parallel image processing and adaptive optics for nonlinear spatio-temporal processes occurring in optical (opto-electronic) nonlinear two-dimensional feedback systems.
Run length encoding can be found in numerous applications such as data transfer or image storing (Sayood, 2002). It is a well known, easy and efficient compression method based on the assumption of long data sequences...
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ISBN:
(纸本)9780863419317
Run length encoding can be found in numerous applications such as data transfer or image storing (Sayood, 2002). It is a well known, easy and efficient compression method based on the assumption of long data sequences without the change of content. These sequences can be described by their position and length of appearance. Implementations using dedicated logic are optimised for parallel data processing. Here, images are transferred in blocks of multiple pixels in parallel. A compression of these streams into a run length code requires an encoder with a parallel input. This run length encoder has to compress the sequence at a minimum of clock cycles to avoid long inhibit intervals at the input. This paper describes a hardware algorithm performing a high performance run length encoding for binary images using a parallel input.
The processing of large hyperspectral images presents challenges from both memory usage and computation points of view. Large images require proper partitioning in order to be stored in memory and to exploit the benef...
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ISBN:
(纸本)9781467322423
The processing of large hyperspectral images presents challenges from both memory usage and computation points of view. Large images require proper partitioning in order to be stored in memory and to exploit the benefits of parallel implementation on high performance computing architectures. This paper analyzes several variants of reading and distributing large images and presents critical issues and some corresponding solutions in designing efficient parallel implementations of two clustering algorithms which use both spectral and spatial information. All experiments were conducted on a BlueGene/P supercomputer using up to 1024 processors.
In the present era, huge amount of data is being produced every single day. A significant portion of this massive data or big data is contributed by images. Besides the amount of data, the size and resolution of indiv...
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ISBN:
(纸本)9781509020300
In the present era, huge amount of data is being produced every single day. A significant portion of this massive data or big data is contributed by images. Besides the amount of data, the size and resolution of individual images is also increasing at a very fast pace, leading to more and more complex imageprocessing algorithms which in turn pose great demand to computation power. This paper provides a solution to one such imageprocessing application which analyzes the image-processing kernels from an industrial application: Organic-Light-Emitting-Diode (OLED) Printing for OLED center detection. The application uses Hadoop and Hadoop imageprocessing Interface (HIPI) for parallelizing the processing. Hadoop provides the parallelprocessing paradigm, which when used along with HIPI can provide significant performance improvements for processingimages.
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