Browsing by Author "Jennings, C."

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    DISTRIBUTED FORCE-BASED THINNING AND A GENERAL DISTRIBUTION METHOD
    (1993-02-01) Molaro, D.; Parker, J.R.; Jennings, C.
    A recently developed thinning algorithm, based on computing repulsive `forces' acting on each object pixel, produces nice skeletons, but involves some very intensive computations. As a result, the method takes a long time to thin any real image, when compared against other existing methods. It can be made practical by distributing the computation across a network of workstations. This has applications to other computationally difficult image processing and vision algorithms, and has been generalized and made relatively simple to do.
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    FORCE-BASED THINNING STRATEGY WITH SUB-PIXEL PRECISION
    (1993-02-01) Molaro, D.; Parker, J.R.; Jennings, C.
    Most vision researchers would agree that the medial axis transform does not yield an ideal, or in some cases even acceptable, skeleton. For example, single pixel irregularities can produce gross changes in an otherwise simple skeleton. The problem of defining what is meant by skeleton and skeletal pixel is one that has been rarely addressed, but seems important. Here, a thinning strategy is proposed that is based on a definition of a 'skeletal pixel'. The basic idea is that a skeleton is a global property of a binary object, and that the boundary should be used to locate the skeletal pixels.
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    THRESHOLDING USING AN ILLUMINATION MODEL
    (1993-02-01) Parker, J.R.; Jennings, C.; Salkauskas, A.G.
    Most grey level thresholding methods produce good results in situations where the illumination gradient in the original raster image is regular and not too large. In other cases, such as a large linear change in illumination, a satisfactory bi-level image cannot be produced. If the object pixels can be identified in a variety of positions throughout the image, these can be used to construct a surface whose height is related to illumination at each pixel. This estimate can be used to produce a threshold for each pixel. The method described here uses the Shen-Castan edge detector to identify object pixels, and creates a surface using a moving least squares method that can be used to threshold the image.