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Detection, localization, and estimation of edges
February 1989 (vol. 11 no. 2)
pp. 191,192,193,194,195,196,197,198
A method to detect, locate, and estimate edges in a one-dimensional signal is presented. It is inherently more accurate than all previous schemes as it explicitly models and corrects interaction between nearby edges. The method is iterative with initial estimation of edges provided by the zero crossings of the signal convolved with Laplacian of Gaussian (LoG) filter. The necessary computations necessitate knowledge of this convolved output only in a neighborhood around each zero crossing and in most cases, could be performed locally by independent parallel processors. Results on one-dimensional slices extracted from real images, and on images which have been proposed independently in the row and column directions are shown. An analysis of the method is provided including issues of complexity and convergence, and directions of future research are outlined.<>

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Index Terms:
picture processing,filtering and prediction theory,iterative methods,pattern recognition,zero crossings,edge estimation,edge detection,edge localisation,Laplacian of Gaussian filter,pattern recognition,picture processing,Image edge detection,Image reconstruction,Convergence,Iterative methods,Filters,Concurrent computing,Convolution,Biology computing,Physics computing,Layout
Citation:
"Detection, localization, and estimation of edges," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 2, pp. 191,192,193,194,195,196,197,198, Feb. 1989, doi:10.1109/34.16714
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