the Gaussian-Markov random field (MRF) model is a very useful technique for imageprocessing, such as feature extraction and data compression. However its strict stability condition makes the model identification comp...
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the Gaussian-Markov random field (MRF) model is a very useful technique for imageprocessing, such as feature extraction and data compression. However its strict stability condition makes the model identification complex. the major problem is the choice of a proper support region for the model. In this paper a new model is proposed which is based on the MRF model and called the modified Gaussian-Markov random field model. It is not an optimal MRF model but has a very useful property, namely decorrelation. A stable modified MRF model always exists even if a stable MRF model does not exist on the given support region. Applications to texture segmentation are also presented.< >
the depth-first picture expression (DF-Expression) is an effective data compression technique for binary images. When one applies it to real equipment, one needs a practical encoding/decoding scheme which should be fa...
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the depth-first picture expression (DF-Expression) is an effective data compression technique for binary images. When one applies it to real equipment, one needs a practical encoding/decoding scheme which should be fast and simple in the operation. the authors study two types of coding algorithm, one is the top-down and the other is the bottom-up methods. these algorithms are put into two C programs to compare the speed. the authors have designed an encoding/decoding hardware device, and built it on a breadboard which is linked to a workstaton. the hardware encoder/decoder can achieve a 50-100 times faster operation than the C programs run on a 4 MIPS workstation.< >
A system for dynamic intelligent scheduling and control (DISC) of reconfigurable parallel processors is presented. the purpose of the system is to provide a rapid prototyping capability for computervision/image proce...
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A system for dynamic intelligent scheduling and control (DISC) of reconfigurable parallel processors is presented. the purpose of the system is to provide a rapid prototyping capability for computervision/imageprocessing tasks. the scheduler particularly addresses the problems of algorithms with execution times that depend on the image data and processing scenarios that vary dynamically based on the input image. Since conventional scheduling methods cannot propose schedules for most masks of this type, a dynamic controller is used to schedule the task and reconfigure the machine on the fly. this dynamic scheduling system attempts to balance the overall processing scenario withthe needs of the individual routines that make up the task. the implementation of this system is discussed, with emphasis on the scheduling heuristics and the use of the system for prototyping computervision/imageprocessing tasks. Testing was done on a number of tasks that exercised different aspects of the scheduling strategy. the schedules determined by DISC have an average tiling percentage of 77% and an average scheduling overhead of only 0.1% of the total task execution time.
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 heterogenous 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.
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 dataflow 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.
An expert system for algorithm design for industrial machine vision is presented. Its knowledge base includes human experts' knowledge about imageprocessing techniques, and it is capable of solving given vision p...
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ISBN:
(纸本)0818620625
An expert system for algorithm design for industrial machine vision is presented. Its knowledge base includes human experts' knowledge about imageprocessing techniques, and it is capable of solving given vision problems. As a problem domain, the authors have chosen vision algorithms for a parts feeder, which determines the attitude of mechanical parts on a conveyor belt and rejects parts with inappropriate attitudes. the expert system for a parts feeder is designed to consists of three components: a feature selection expert, an imageprocessing expert, and a decision tree generator. the knowledge necessary for the design of a vision algorithm for determining the attitudes of parts is discussed. A framework for representing the knowledge needed in order to find solutions to pattern classification problems is established.
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.
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 interruptable 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.
the object-oriented language for image and vision environments (OLIVE), which is intended to make it easier to develop efficient, portable applications is presented. OLIVEs principal object types, called images and lo...
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the object-oriented language for image and vision environments (OLIVE), which is intended to make it easier to develop efficient, portable applications is presented. OLIVEs principal object types, called images and locuses (abstractions of point sets and geometric entities), and their corresponding operations, including the use of locuses as generalized indexes for images, are defined. Several examples of OLIVE for typical imageprocessing and machine vision tasks are presented. Issues concerning the implementation of OLIVE, including a hardware architecture that simplifies the implementation while enhancing its performance, are discussed.
the authors show how the data parallel programming model can be applied to high level vision tasks needed for object recognition. the architecture and programming model of the Connection Machine System are reviewed. U...
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the authors show how the data parallel programming model can be applied to high level vision tasks needed for object recognition. the architecture and programming model of the Connection Machine System are reviewed. Utilities for representing and manipulating sets of data, the primary representation outside of the image plane, are described using communications primitives, especially segmented scans. Several algorithms for matching and evidence accumulation, which are constructed from the utilities are compared. the techniques emphasize the use of sorting and sparse representations of space in order to limit the combinatorial processing requirements of high-level vision.
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