The objective of computer vision system is to outline the objects in a picture and label them with an appropriate interpretation. This study proposes a new paradigm for a modular computer vision system which is both d...
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The objective of computer vision system is to outline the objects in a picture and label them with an appropriate interpretation. This study proposes a new paradigm for a modular computer vision system which is both data directed and knowledge based. The system consists of three different types of units, two of which are associative data memories implemented as relational databases. The short term memory (STM) contains the raw color picture data and the most current interpretations and deductions about the original scene. The long term memory (LTM) contains a detailed model of the scene under consideration. A collection of analysis processors, each of which is specialized for a particular task can communicate with both of these memories.
An analysis is given of the Moire pattern formation in scanning and reproducing halftone pictures. The analysis includes the effects of the aperture size and shape of the reproduction printing process. Also the effect...
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An analysis is given of the Moire pattern formation in scanning and reproducing halftone pictures. The analysis includes the effects of the aperture size and shape of the reproduction printing process. Also the effects of parallelogram grid sampling as well as square grid sampling have been taken into consideration. A simulation program has been written to synthesize the Moire patterns based upon the Fourier transform of the output picture. Several observations will be discussed and among two of the more important ones are: (a) Moire fringes can result from beating together of clusters of Fourier spatial frequencies and (b) fixed value thresholding intensifies Moire patterns.
Segmentation of monochrome images to obtain boundaries of the object is an important problem in scene analysis. An algorithm is described for locating object boundaries from an image of objects. The algorithm, called ...
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Segmentation of monochrome images to obtain boundaries of the object is an important problem in scene analysis. An algorithm is described for locating object boundaries from an image of objects. The algorithm, called Global-Local-Edge-Coincidence (GLEC) uses both local and global edge information to select a stable set of object boundaries. Significantly improved results are shown in several examples including blocks, building and aerial photograph. The significance of this algorithm is that the boundaries of objects can often be located from a single image.
A description is given of a versatile imageprocessing.system developed at Pratt & Whitney Aircraft (P&WA) and the highly efficient spatial domain filtering techniques which are its computational heart. By exp...
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A description is given of a versatile imageprocessing.system developed at Pratt & Whitney Aircraft (P&WA) and the highly efficient spatial domain filtering techniques which are its computational heart. By exploiting block average filtering techniques requiring only local storage, each block filtering operation uses only four additions and one multiplication per pixel, regardless of image or block size. As a consequence, extremely rapid imageprocessing.is possible;for example, deconvolution of a 512 multiplied by 512 pixel image can be performed in just five seconds on an IBM 3033 with Fortran code.
image data compression can be achieved by a number of techniques such as DPCM and Transform Coding. Adaptive image compression can be done with any of these techniques. Adaptive image data compression is a procedure i...
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image data compression can be achieved by a number of techniques such as DPCM and Transform Coding. Adaptive image compression can be done with any of these techniques. Adaptive image data compression is a procedure in which the number of bits allocated to each image block changes from block to block depending on the block complexity. This variable input bit rate must be converted to a constant output bit rate by a rate buffer. A study is made of some causal and noncausal approaches to adaptively allocating bits under the constraint of a fixed size buffer. A discussion is also presented of the optimal noncausal approach whose performance is a least upper bound on any causal approach.
The problem of estimating the error probability of a given classification system is considered. Statistical properties of the empirical error count (C) and the average conditional error (R) estimators are studied. It ...
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The problem of estimating the error probability of a given classification system is considered. Statistical properties of the empirical error count (C) and the average conditional error (R) estimators are studied. It is shown that in the large sample case the R estimator is unbiased and its variance is less than that of the C estimator. In contrast to conventional methods of Bayes error estimation the unbiasedness of the R estimator for a given classifier can be obtained only at the price of an additional set of classified samples. On small test sets the R estimator may be subject to a pessimistic bias caused by the averaging phenomenon characterising the functioning of conditional error estimators.
The three-dimensional shape analysis problem is a very demanding test of shape analysis algorithms. Previous approaches to the problem have employed global features such as moments and Fourier descriptors. Global feat...
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The three-dimensional shape analysis problem is a very demanding test of shape analysis algorithms. Previous approaches to the problem have employed global features such as moments and Fourier descriptors. Global features lack the capacity for solving the partial shape recognition problem, in which only part of the unknown shape is available. Previous approaches to local shape analysis have employed structural (syntactic) methods, but these methods have so far failed to solve the three-dimensional problem. This study describes a hybrid structural/statistical local shape analysis algorithm which is applied to the three-dimensional problem.
The k-syntactic similarity approach is couched in graphical representation terms and its ability to provide global recognition capability while retaining a low time complexity is explored. One potential application do...
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The k-syntactic similarity approach is couched in graphical representation terms and its ability to provide global recognition capability while retaining a low time complexity is explored. One potential application domain, that of composite shape decomposition into approximately convex subshapes, is described. This is shown to be equivalent to finding cycles within a particular graph. The approach yields valid decompositions in many cases where additional semantic considerations are necessary for proper analysis. The permissible graph structures representing composite shapes given a reasonable set of relations are determined. Experimental results on non-ideal data are given.
In dynamic radiological studies time-dependent processes, ranging from motion of the patient's own anatomical structures to the kinetics of a contrast agent or radiotracer, are recorded in a sequence of image fram...
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In dynamic radiological studies time-dependent processes, ranging from motion of the patient's own anatomical structures to the kinetics of a contrast agent or radiotracer, are recorded in a sequence of image frames. If the data are available in digital format, appreciation of regional patterns of behavior can be enhanced by digital operations on the image sequence. These operations can be considered as projecting out aspects of temporal behavior that are not readily or unambiguously perceptible to the unassisted observer. A report is presented on experiences with such functional images in three radiological modalities at the University of Wisconsin. Examples include nuclear medicine studies of the heart and liver, transmission computed tomography studies of the brain, and digital fluoroscopic studies of the heart and kidneys.
The problem of automatic registration of deformed images is addressed. It describes an iterative technique for gradually updating the local registration of two images based on a dynamic cooperative model. the method i...
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The problem of automatic registration of deformed images is addressed. It describes an iterative technique for gradually updating the local registration of two images based on a dynamic cooperative model. the method is cooperative in the sense that a feature at one location in an image influences decisions made at other locations. Initially, when registration is expected to be poor and feature measures unreliable, cooperative interaction is strong. It is progressively weakened with each iteration to permit matching of fine details. For a physical analogy, consider an elastic picture whose stiffness decreases with each iteration, and which is deformed by forces arising from similar features in another picture. Examples are shown for both dot patterns and gray-scale pictures.
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