The diagnostic evaluation of biomedical imagery by computer presents a massive data processing problem that may be effectively handled by multiprocessor computer systems such as the Heidelberg Polyp. A hardware and so...
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We present an algorithm for texture characterization based upon curvilinear integration of grey tone signal along some predefined *** the context of image segmentation, we compare the performances of this very simple ...
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The parallel processing, high-speed, compact system fabrication possibility, low power dissipation and size, plus weight advantages of optical processors have achieved great strides in recent years. The architectures,...
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Various levels of parallelism have recently been introduced in advanced microprocessors to meet the demanding computing need in digital video processing and other multimedia applications. Because many imaging algorith...
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Various levels of parallelism have recently been introduced in advanced microprocessors to meet the demanding computing need in digital video processing and other multimedia applications. Because many imaging algorithms are easily parallelizable, these architectural features and their wide availability at low cost have become a powerful tool in tackling both existing and new imaging applications. At the lowest level, the subword parallelism is used in the new instructions aimed at processing multiple multimedia data simultaneously. Instruction-level parallelism including subword parallelism is realized in either very long instruction word or superscalar architectures, while on-chip and/or off-chip multiprocessing capability is available for easier multiprocessor system designs. One of the difficulties in maximizing the computing throughput via parallelism has been the level of programming in that to obtain the optimal performance, assembly-level programming has typically been required. We review the architectural features in several modern microprocessors such as TMS320C60, TM-1000, PowerPC 604, Pentium Il, R10000, Alpha 21264, PA-RISC 8200, UltraSPARC-ii, and TMS320C80. Various obstacles to obtaining the best performance from these microprocessors with high-level and assembly languages are discussed, and several approaches to overcome these difficulties in diverse imaging applications are presented. (C) 1998 John Wiley & Sons, Inc.
An architecture and algorithms for a VLSI computer for back-projection image reconstruction are described. The computer consists of multiple identical back-projection processors connected in a linear array. image pixe...
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A generalized syntactic pattern recognition method is presented and applied to the interpretation of binary images containing arbitrary line structures, e.g. documents with graphics like technical drawings or road map...
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The discrete cosine transform (DCT) is a central mathematical operation in several digital signal processing methods and image/video standards. In this paper, we propose a collection of twelve approximations for the 8...
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The discrete cosine transform (DCT) is a central mathematical operation in several digital signal processing methods and image/video standards. In this paper, we propose a collection of twelve approximations for the 8-point DCT based on integer functions. Considered functions include: the floor, ceiling, truncation, and rounding-off functions. Sought approximations are required to meet the following specific criteria: (i) very low arithmetic complexity, (ii) orthogonality or quasi-orthogonality, and (iii) low-complexity inversion. By varying a scaling parameter, approximations could be systematically obtained and several existing approximations were identified as particular cases of the proposed methodology. Particular cases include the signed DCT and the rounded DCT. Four new quasi-orthogonal approximations were introduced and their practical relevance was demonstrated. All approximations were given fast algorithms based on matrix factorization methods. Proposed approximations are multiplierless;their computation requires only additions and bit-shifting operations. Additive complexity ranged from 18 to 24 additions. Obtained approximations were compared with the exact DCT and assessed in the context of JPEG-like image compression. As quality assessment measures, we considered the peak signal-to-noise ratio and the structural similarity index. Because its low-complexity and good performance properties, the proposed approximations are suitable for hardware implementation in dedicated architectures. (C) 2013 Elsevier B.V. All rights reserved.
Textural analysis is now a commonly used technique in digitalimageprocessing. In this paper, we present an application of textural analysis to high resolution SPOT satellite images. The purpose of the methodology is...
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Matrix inversion is a computationally intensive basic block of many digital signal processingalgorithms. To decrease the cost of their implementations, programmers often prefer the fixed-point arithmetic. This arithm...
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ISBN:
(纸本)9791092279061
Matrix inversion is a computationally intensive basic block of many digital signal processingalgorithms. To decrease the cost of their implementations, programmers often prefer the fixed-point arithmetic. This arithmetic requires less resources and runs faster than the floating-point arithmetic, but all the arithmetical details must be handled by the programmer. In this article, we overcome this drawback by presenting an automated approach to synthesize fixed-point code for matrix inversion based on Cholesky decomposition. First we rigorously define the square root and division operators especially in terms of rounding error, and we implement them in the CGPE library. This allows us to provide accuracy certificates for the generated code. Second we propose a workflow based on Cholesky decomposition that carefully uses these operators to produce accurate code for the basic blocks of matrix inversion. Finally we illustrate the efficiency of our approach on some benchmarks, and show how it allows us to synthesize accurate code in a few seconds and thus to reduce the development time of fixed-point matrix inversion.
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