This paper deals with the problem of computing, projections of digitalimages. The novelty of our contribution is that we present algorithms which are suitable for implementation in general purpose imageprocessing an...
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This paper deals with the problem of computing, projections of digitalimages. The novelty of our contribution is that we present algorithms which are suitable for implementation in general purpose imageprocessing and image analysis pipeline architectures. Also, we propose some new pipeline configurations which achieve a remarkable degree of parallelism in the computation of projection data and in fact, of many other geometrical descriptors of digitalimages. In particular, random access memories and other dedicated hardware devices are not needed in our algorithms. The effectiveness of our approach and feasibility of the proposed architectures are demonstrated by running our algorithms in commercially available short-pipelines for imageprocessing and analysis. Examples are shown of the use of projection data for machine vision applications.
The use of digital computers to process various types of sensor data is becoming increasingly common, in both civilian and military applications. One example of this use is the enhancement of photographs to increase t...
The use of digital computers to process various types of sensor data is becoming increasingly common, in both civilian and military applications. One example of this use is the enhancement of photographs to increase their clarity, or emphasize a particular detail. Previously, the computers used to perform this processing was done in specialized circuits, mainframe or minicomputers. More recently, extremely powerful microprocessors have become available that show potential to be applied in this area. This thesis explores a particular class of imageprocessing, known as image Segmentation, implemented on a particular microprocessor. The microprocessor is the Fairchild F9450, the first civilian version of the 1750A military specification microprocessor. This microprocessor, along with its associated chip set, appears well suited to imageprocessing, having high speed capability, direct floating point arithmetic instructions, multiprocessing capacity, and the ability to address up to sixteen megabytes of memory. Additionally, a sophisticated software development tool set, known as Microprocessor Pascal, is available to develop and test software for the 1750A/F9450 microprocessor. This tool set allows software to be developed on the VAX-11/780 minicomputer, targeted for final use on the 1750A/F9450. This work utilized the Microprocessor Pascal tool set to test and compare representative image Segmentation algorithms. The speeds of execution and code sizes of the programs were determined for the F9450/1750A microprocessor and the VAX-11/780 minicomputer, and were compared to determine the feasibility of using the F9450/1750A microprocessor for image segmentation work. Several images resulting from the image segmentation processing are included, as well as the Pascal programs used to perform the processing.
digital Subtraction Angiography (DSA) is commonly used in conjunction with intravenous contrast injection for detection of atherosclerotic disease in arteries outside of the heart. images of coronary arteries obtained...
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ISBN:
(纸本)0892525703
digital Subtraction Angiography (DSA) is commonly used in conjunction with intravenous contrast injection for detection of atherosclerotic disease in arteries outside of the heart. images of coronary arteries obtained with intravenous DSA have been limited in quality by several important factors. Among these is the confusing background provided by superposed pulmonary veins. Because these opacify just before the coronary arteries, conventional remasking results in substantial loss in coronary artery contrast. This paper presents preliminary work on a processing scheme in which the degree of correlation between the contrast pass curves in individual pixels and a reference region can be used to adaptively suppress pulmonary structures.
In this paper, we deal with the problem of detecting and segmenting objects in textured darkfield digitalimagery for automated visual inspection applications. The technique we will follow is based on a sequential app...
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In this paper, we deal with the problem of detecting and segmenting objects in textured darkfield digitalimagery for automated visual inspection applications. The technique we will follow is based on a sequential application of local operators which serves the purpose of clustering the object and the background gray levels. This procedure can be considered as an extension of average-thresholding type techniques. This algorithm has fast implementations in general purpose imageprocessing pipeline architectures and therefore, it is appealing to real-time computer vision applications. Computational examples showing the effectiveness of the segmentation technique will be discussed.
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