The Euclidean distance transform (EDT) is an operation to convert a binary image consisting of black and white pixels to a representation where each pixel has the Euclidean distance of the nearest black pixel. The EDT...
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The Euclidean distance transform (EDT) is an operation to convert a binary image consisting of black and white pixels to a representation where each pixel has the Euclidean distance of the nearest black pixel. The EDT has many applications in computer vision and imageprocessing. In this paper, we present a constant-time algorithm for computing the EDT of an N x N image on a reconfigurable mesh. Our algorithm. has two variants. (i) If the image is initially given in an Xx N mesh, one pixel per processor, our algorithm requires an N x N x N mesh for computing the EDT. (ii) If the image is given in an N x N-2 mesh, each row of the image in the First row of a separate N x X mesh, we can compute the EDT in the same N x N-2 mesh. The AT(2) bounds for these two variants are O(N-4) and O(N-3) respectively. The best previously known algorithm (Y. Pan and K. Li, Inform. Sci. 120 (1999), 209-221) for this problem assumes input similar to the second variant of our algorithm and runs in constant-time on an N-2 x N-2 reconfigurable mesh with an AT(2) bound of O(N-4). Hence both variants of our algorithm improve upon the processor complexity of the algorithm in Pan and Li (1999) by a factor of N and the second variant improves upon the AT(2) complexity by a factor of N. (C) 2001 Academic Press.
In this paper we propose a solution to handle two problems inducted by the growth of the complexity of machine vision systems: (i) The need of a robust, open and flexible framework to control various descriptive and o...
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In this paper we propose a solution to handle two problems inducted by the growth of the complexity of machine vision systems: (i) The need of a robust, open and flexible framework to control various descriptive and operational knowledge;(ii) the necessity to have a architecture which offer parallelprocessing that can be easily scaled to an evolving underlying hardware. We propose an agent society, implemented in the Java language, that is organized as an irregular pyramid for many reasons: (i) agent provides an abstraction to encapsulate reactive or cognitive processing;(ii) the pyramid proposes a formal graph-based approach to ensure global and distributed goal satisfaction. The evaluation of the architecture, performed on a X scanner breast image, shows up good quality results and parallelprocessing abilities.
In this paper, we propose the distributed fractal image compression and decompression on the PVM system. We apply the regional search for the fractal image compression to reduce the communication cost on the distribut...
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ISBN:
(纸本)0769509525
In this paper, we propose the distributed fractal image compression and decompression on the PVM system. We apply the regional search for the fractal image compression to reduce the communication cost on the distributed system PVM. The regional sear-ch is to search the partitioned iterated function system from a region of the image instead of over the whole image. Because the area surrounding of a partitioned block is similar to this block possibly, finding the fractal codes by regional search has a higher compression ratio and less compression time. When implemented on the PVM, the fractal image compression using regional search reduces the compression time with less compression loss. When we compress the image Lena with image size 1024 x 1024 using the region size 512 x 512 on the PVM with 4 Pentium ii-300 PCs, the compression time is 13.6 seconds, the compression ratio is 6.34 and the PSNR is 38.59. But it needs to take 176 seconds, have the compression ratio 6.30 and have PSNR 39.68 by the conventional fractal image compression. In addition, when the region size is 128 x 128, the compression rime is 7.8 seconds, the compression ratio is 7.53 and the PSNR is 36.67. In the future, we can apply this method to the fractal image compression using neural networks.
A novel adaptive nonlinear filter is proposed aimed at smoothing homogenous regions while maintaining image structures. The filter can be utilized as a pre-processing tool in image segmentation and edge estimation for...
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ISBN:
(纸本)0780370414
A novel adaptive nonlinear filter is proposed aimed at smoothing homogenous regions while maintaining image structures. The filter can be utilized as a pre-processing tool in image segmentation and edge estimation for improving the results. Several special features are introduced to the filter, including using local adaptive radial clustering and pixel filtering to exclude the influence of outliers and to maintain image structures;using steepest-ascent method to iteratively update pixels to the nearest clusters obtained by mean-shift;and introducing highly parallelprocessing by using random seed samples and their associated data blocks which enables fast processing and the global optimum solution of the nonlinear filter. Experiments were done on images of various complexities, and good results were obtained. Evaluations of the filter were also done in terms of edge preserving and image segmentation.
A new signal processing method is developed for solving the multi-line fitting problem in a two dimensional image. We first reformulate the former problem in a special parameter estimation framework such that a first ...
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ISBN:
(纸本)0780370414
A new signal processing method is developed for solving the multi-line fitting problem in a two dimensional image. We first reformulate the former problem in a special parameter estimation framework such that a first order or a second order polynomial phase signal structure is obtained. Then, the recently developed algorithms in that formalism (and particularly the downsampling technique for high resolution frequency estimation) can be exploited to produce accurate estimates fox line parameters. This method is able to estimate the parameters of parallel lines with different offsets and handles the quantization noise effect which can not be done by the sensor array processing technique introduced by Aghajan et al. Simulation results are presented to demonstrate the usefulness of the proposed method.
A new technique for transmitting information through multimode fiber-optic cables is presented. This technique sends parallel channels through the fiber-optic cable, thereby greatly improving the data transmission rat...
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A new technique for transmitting information through multimode fiber-optic cables is presented. This technique sends parallel channels through the fiber-optic cable, thereby greatly improving the data transmission rate compared with that of the current technology, which uses serial data transmission through single-mode fiber. An artificial neural network is employed to decipher the transmitted information from the received speckle pattern. Several different preprocessing algorithms are developed, tested, and evaluated. These algorithms employ average region intensity,distributed individual pixel intensity, and maximum mean-square-difference optimal group selection methods. The effect of modal dispersion on the data rate is analyzed. An increased data transmission rate by a factor of 37 over that of single-mode fibers is realized. When implementing our technique, we can increase the channel capacity of a typical multimode fiber by a factor of 6. (C) 2001 Optical Society of America OC;CS codes: 060.0060, 060.2330, 060.2350, 060.4230, 200.4260.
This paper describes a parallel implementation developed to improve the time performance of the Iterative Closest Point Algorithm. Within each iteration, the correspondence calculations are distributed among the proce...
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ISBN:
(纸本)0769509851
This paper describes a parallel implementation developed to improve the time performance of the Iterative Closest Point Algorithm. Within each iteration, the correspondence calculations are distributed among the processor resources. Ar the end of each iteration, the results of the correspondence determination are communicated back to a central processor and the current transformation is calculated A number of additional techniques were developed that sen,ed to improve upon this basic scheme. Calculating the partial sums within each distributed resource made it unnecessary to transmit the correspondence values back to the central processor, which reduced the communication overhead, and improved time performance. Randomly distributing the points among the processor resources resulted in a better load balancing, which further improved time performance. We also found that thinning the image by randomly removing a certain percentage of the points did not improve the performance, when viewed as the progression of mse with time. The method was implemented and tested on a 22 node Beowulf class cluster. For a large image, linear performance improvements were obtained for up to 16 processors, while they held for rtp to 8 processors with a smaller image.
In this paper, we present RPV-ii, a stream-based real-time parallelimageprocessing environment on distributedparallel computers, or PC-cluster, and its performance evaluation using a realistic application. The syst...
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Deblocking and deringing are two video post-processing techniques largely used to remove coding artifacts and improve the visual quality when rendering low bit rate coded video. The algorithms used to achieve these ta...
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ISBN:
(纸本)0780370414
Deblocking and deringing are two video post-processing techniques largely used to remove coding artifacts and improve the visual quality when rendering low bit rate coded video. The algorithms used to achieve these tasks are computationally intensive and usually require high speed processors to be able to run in real time. Efficient implementations of signal adaptive filters for video post-processing can be obtained using the specialized features of the parallel BOPS(R) DSP cores. The performance achieved by deblocking and deringing CIF and SDTV size video sequences on the MANTA(TM) prototype chip are illustrated. It is shown that such complex tasks may be executed at low clock rates using the BOPS ManArray(TM) technology.
We develop algorithms for sequential signal encoding from sensor measurements, and for signal estimation via fusion of channel-corrupted versions of these encodings. For signals described by state space models, we pre...
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ISBN:
(纸本)0780370414
We develop algorithms for sequential signal encoding from sensor measurements, and for signal estimation via fusion of channel-corrupted versions of these encodings. For signals described by state space models, we present optimized sequential binary-valued encodings constructed via threshold-controlled scalar quantization of a running Kalman filter signal estimate from the sensor measurements. We also develop methods for robust fusion from observations of these encodings corrupted by binary symmetric channels.
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