The main aim of this work is to show, how GPGPUs can facilitate certain type of imageprocessingmethods. The software used in this paper is used to detect special tissue part, the nuclei on (HE - hematoxilin eosin) s...
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Phase shift Moiré is a very popular and one of the most successful techniques for shape measurement of 3-D objects such as PCB (printed circuit board), TFT (thin film transistor), LCD (Liquid crystal display) etc...
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In this paper, we present a new method for quantifying color information so as to detect edges in color images. Our method uses the volume of a pixel in the HSI color space, allied with noise reduction, thresholding a...
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ISBN:
(纸本)9780889869073
In this paper, we present a new method for quantifying color information so as to detect edges in color images. Our method uses the volume of a pixel in the HSI color space, allied with noise reduction, thresholding and edge thinning. We implement our algorithm using NVIDIA Compute Unified Device Architecture (CUDA) for direct execution on Graphics processing Units (GPUs). Our experimental results show that: compared to traditional edge detection methods, our method can improve the accuracy of edge detection and withstand greater levels of noise in images;and our GPU implementation achieves speedups over related CUDA implementations.
Mammogram breast cancer images have the ability to assist physician in detection of disease caused by cells normal growth. Developing algorithms and software to analyse these images may also assist physicians in their...
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ISBN:
(纸本)9783642240362;9783642240379
Mammogram breast cancer images have the ability to assist physician in detection of disease caused by cells normal growth. Developing algorithms and software to analyse these images may also assist physicians in their daily work. Micro calcifications are tiny calcium deposits in breast tissues. They appear as small bright spots on mammograms. Since micro calcifications are small and subtle abnormalities, they may he overlooked by an examining radiologist. image Enhancement and Filtering is always the root process in many medical imageprocessing applications. It is aimed at reducing noise in images. In this paper we have made comparison between several novel and hybrid enhancement techniques. The comparison is based on the basis of performance evaluation parameters (statistical parameter) such as PSNR, and CNR. These can be used for identifying breast nodule malignancy to provide better chance of a proper treatment. These methods are tested on digital mammograms present in mini-MIAS database.
The main aim of this work is to show, how the GPGPUs can be used to speed up certain imageprocessingmethods. The algorithm explained in this paper is used to detect nuclei on (HE hematoxilin eosin) stained colon tis...
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The edge-directed interpolation scheme is a non-iterative, orientation-adaptive method to enhance image resolution with better visual effect than conventional interpolation methods. It interpolates the missing pixels ...
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The edge-directed interpolation scheme is a non-iterative, orientation-adaptive method to enhance image resolution with better visual effect than conventional interpolation methods. It interpolates the missing pixels based on the covariance of a high-resolution image estimated from the covariance of the low-resolution image. In spite of the impressive performance, the computational complexity of covariance-based adaptation is significantly higher than that of the conventional linear interpolation algorithms. In this paper, we propose a GPU-based massively parallel version of the edge-directed interpolation scheme. A speedup of 61.7x can be achieved with respect to its single-threaded CPU counterpart in the host computer.
Stencil calculations comprise an important class of kernels in many scientific computing applications ranging from simple PDE solvers to constituent kernels in multigrid methods as well as imageprocessing application...
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Stencil calculations comprise an important class of kernels in many scientific computing applications ranging from simple PDE solvers to constituent kernels in multigrid methods as well as imageprocessing applications. In such types of solvers, stencil kernels are often the dominant part of the computation, and an efficient parallel implementation of the kernel is therefore crucial in order to reduce the time to solution. However, in the current complex hardware micro architectures, meticulous architecture-specific tuning is required to elicit the machine's full compute power. We present a code generation and auto-tuning framework \textsc{Patus} for stencil computations targeted at multi- and many core processors, such as multicore CPUs and graphics processing units, which makes it possible to generate compute kernels from a specification of the stencil operation and a parallelization and optimization strategy, and leverages the auto tuning methodology to optimize strategy-dependent parameters for the given hardware architecture.
During the present decade, emerging architectures like multicore CPUs and graphics processing units (GPUs) have steadily gained popularity for their ability to deploy high computational power at a low cost. In this pa...
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During the present decade, emerging architectures like multicore CPUs and graphics processing units (GPUs) have steadily gained popularity for their ability to deploy high computational power at a low cost. In this paper, we combine parallelization techniques on a cooperative cluster of multicore CPUs and multisocket GPUs to apply their joint computational power to an automatic image registration algorithm intended for the analysis of high-resolution microscope images. Registration methods pose a computational challenge within the biomedical field due to the large size of microscope image data sets, which typically extend to the Terabyte scale. We analyze this application to identify those parts which are more favorable to the CPU and GPU execution models and decompose the process accordingly. Performance results are presented for two sets of images: mouse placenta (16K × 16K pixels) and mouse mammary tumor (23K × 62K pixels). Execution times are shown on different multi-node, multi-socket and multi-core configurations to provide performance insights about the most effective approach.
In the present work, automatic corner detection in soccer games based on image features (e.g., object-based features) has been studied. For this purpose, a framework has been proposed that consists of five steps. This...
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In the present work, automatic corner detection in soccer games based on image features (e.g., object-based features) has been studied. For this purpose, a framework has been proposed that consists of five steps. This paper mainly focuses on the first three steps and specially ball detection step. Ball position on the field plays an important role in determining which event has occurred in the game. Therefore, it is necessary to detect exact position of the ball in the playfield and then track it. Ball trajectory that can be obtained via tracking is useful for identifying and detecting main events of soccer games. In these three steps, the most important processing that has been applied to the images is based on image segmentation to detect playfield, field lines, and ball. Cleaning morphological method is applied to detect the ball. This method is real-time and automatic, it yields superior results in comparison with other common methods such as Template Matching and Circular Hough Transform (CHT). By applying the proposed method, only one candidate for the ball position is obtained and non-ball candidates are removed, hence, it is more reliable than other methods. The results of the proposed method are compared with those of CHT. They illustrate that the proposed method is fast, effective, and reliable.
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