the problem of efficiently processing small data structures on massively parallel single-instruction multiple-data machines using replication methods is discussed. the problem stems from considerations of both multire...
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the problem of efficiently processing small data structures on massively parallel single-instruction multiple-data machines using replication methods is discussed. the problem stems from considerations of both multiresolution vision systems and focus of attention vision systems. A general framework for developing replicated algorithms, based on the four steps of embedding, distribution, decomposition, and collection, is described. A simple example is provided based on computing the histogram of a gray-level image. Replicated chain processing is discussed, and an efficient algorithm for ranking the elements in a chain in log (n) time on a CRCW PRAM (concurrent read, concurrent write parallel random access machine) is presented.
A research project initiated to study the modeling, analysis, and visualization of nonrigid motion in terms of three important types of objects--heart, human face/head, and fluid--is discussed. Attention is given to m...
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ISBN:
(纸本)0818620625
A research project initiated to study the modeling, analysis, and visualization of nonrigid motion in terms of three important types of objects--heart, human face/head, and fluid--is discussed. Attention is given to modeling and analysis of heart wall motion, human face/head motion analysis and synthesis in model-based image compression, and the evolution of coherent structures in fluid motion. It is believed that techniques from computervision can be useful for solving these problems. In particular, the following computervision techniques appear relevant: 3-D solid modeling, motion estimation, curvature analysis, shape from shading, relational structures, model-based systems, aspect graphs, and silhouette-slide theorems. the goal of the research is to develop concepts, methodologies, and techniques that will be widely applicable to many types of nonrigid motion.
the 4-D approach to dynamic real-time machine vision is reviewed. the influence of control integration on the development of behavioral skills and intelligence are emphasized. this approach leads to a processing struc...
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the 4-D approach to dynamic real-time machine vision is reviewed. the influence of control integration on the development of behavioral skills and intelligence are emphasized. this approach leads to a processing structure oriented towards the internal representation of physical objects in 3-D space and time. Results achieved in road vehicle guidance are discussed;this includes convoying with no direct range measurement.
A model for programming parallel and distributed computers in which the user is presented with a single linear address space, unlimited computational parallelism, and a global clock for synchronization is presented. T...
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A model for programming parallel and distributed computers in which the user is presented with a single linear address space, unlimited computational parallelism, and a global clock for synchronization is presented. this allows the user to express the abstract parallelism without being tied to the target architecture and makes the resulting software transportable. An implementation of this model using C as the source language supplemented with a distributed data storage class and concurrent assignments is presented. An example of generating code for low-level imageprocessing algorithms, including histogram equalization, is given. this example shows that parallel algorithms can be developed without architectural knowledge and the resulting programs can be compiled for a wide range of target processors.
An implementation of a texture segmentation algorithm on two high-performance computers, the Connection Machine CM-2 and the Convex mini-supercomputer, is presented. Texture segmentation is the process of identifying ...
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An implementation of a texture segmentation algorithm on two high-performance computers, the Connection Machine CM-2 and the Convex mini-supercomputer, is presented. Texture segmentation is the process of identifying regions with similar texture and separating regions with different textures and is one of the early steps towards identifying surfaces and objects in an image. A segmentation algorithm is described which first extracts texture tokens from the input image, then computes the Voronoi tessellation of the extracted tokens and measures shape features (moments of area) of the resulting Voronoi polygons. Feature similarity is used to obtain an initial labeling of texture tokens as interior or border with four quantized directions. this labeling is then refined using probabilistic relaxation labeling. the computation of the Voronoi tessellation and the probabilistic relaxation labeling process, which are highly data-parallel procedures, are discussed. Substantial speedups were obtained over a sequential (Sun-4/280) implementation.
A parallel computer architecture for real-time imageprocessing is described. the architecture centers on the direct mapping of a static dataflow graph into hardware: each node (or group of nodes) is replaced by a pro...
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A parallel computer architecture for real-time imageprocessing is described. the architecture centers on the direct mapping of a static dataflow graph into hardware: each node (or group of nodes) is replaced by a processing element. the processing elements are connected by a flexible and fast communication network based on multiple interruptible buses. A prototype of the system has been built. Many low-level imageprocessing algorithms run in real time at video rate. Applications of the system include real-time computervision, real-time image enhancement, robotics, and autonomous systems. A color classification algorithm for autonomous vehicle guidance runs at video rate (50 frames/s). For this application, more than 200 million instructions (VAX equivalent) are executed.< >
Research work dealing with some of the problems in computervision (CV) and image understanding (IU) is described. Attention is given to progress in CV and IU involving active range finders, passive stereo sensing, 3D...
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Research work dealing with some of the problems in computervision (CV) and image understanding (IU) is described. Attention is given to progress in CV and IU involving active range finders, passive stereo sensing, 3D reconstruction, 3D scene analysis, dynamic scene analysis, automatic knowledge acquisition for model- and strategy-based systems, and autonomous vision systems.< >
An overview of the requirements for industrial applications of computervision is given. A method called multilevel input binary template matching, which applies binary template matching to gray-level images, is prese...
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An overview of the requirements for industrial applications of computervision is given. A method called multilevel input binary template matching, which applies binary template matching to gray-level images, is presented. Withthis method, a vision system can be tailored to the difficulty of the individual computer-vision application while maintaining high speed and high reliability in all cases. A full custom VLSI specially designed for binary template matching is discussed. this dedicated VLSI chip, called IRIS, has a configurable kernel up to a size of 1024 elements for 1-D or up to 32*32 elements for 2-D, and has integrated line buffers. Some application examples from the field of industrial automation are presented.< >
An autonomous multiprocessor (AMP) for imageprocessing and computervision is presented. It provides high-performance imageprocessing. It has many processor elements (PEs) connected to each other through a communica...
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An autonomous multiprocessor (AMP) for imageprocessing and computervision is presented. It provides high-performance imageprocessing. It has many processor elements (PEs) connected to each other through a communication network. Each PE is asynchronously executed by a data flow control mechanism. It is designed on the basis of a circular pipeline architecture and has a mechanism to work like multiple logical PEs. Each pixel of an image is mapped onto one logical PE, and its data is stored in an operand memory register of a logical PE. image data is stored distributively in the PEs of the system, and required pixel values are transferred to other PEs by inter-PE communication.< >
First- and second-generation implementations of an image understanding architecture, IUA, are described. the IUA system is designed specifically for computervisionprocessingthat relies heavily on artificial intelli...
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First- and second-generation implementations of an image understanding architecture, IUA, are described. the IUA system is designed specifically for computervisionprocessingthat relies heavily on artificial intelligence techniques to classify objects. To provide for low-, intermediate-, and high-level computervisionprocessing-required for model-/knowledge-based interpretation of sensor data-the IUA system combines three heterogeneous levels of parallelism with associative processing mechanisms. the tightly coupled symbolic and numeric processing capabilities constitute a unique computing paradigm ideally suited to computervision applications, which require both control- and data-parallel processing. the IUA architecture, hardware, and software are described.< >
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