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M.M. Fleck, "Multiple Widths Yield Reliable Finite Differences (Computer Vision)," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 4, pp. 412429, April, 1992.  
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@article{ 10.1109/34.126804, author = {M.M. Fleck}, title = {Multiple Widths Yield Reliable Finite Differences (Computer Vision)}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {14}, number = {4}, issn = {01628828}, year = {1992}, pages = {412429}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.126804}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  Multiple Widths Yield Reliable Finite Differences (Computer Vision) IS  4 SN  01628828 SP412 EP429 EPD  412429 A1  M.M. Fleck, PY  1992 KW  computer vision; integral equations; faint images; finite difference edge finder; lowamplitude responses; spurious responses; ideal straight step edge; Gaussian smoothing; spatial structure; noise; blurred features; computer vision; integral equations; noise VL  14 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
A finite difference edge finder in which the finite difference is computed at a range of widths, i.e. a range of distances between data points, is introduced. Wide operators report lowamplitude responses more reliably than narrow operators, so if wide operators are used to fill gaps in narrow operator responses, each operator can be restricted to report only statistically reliable responses without losing many real features. This sharply reduces the noise in the final output. Theoretical bounds on spurious responses in the finite difference outputs, given only weak assumptions about the signal and noise, are presented. The expected response of the edge finder to an ideal straight step edge is also analyzed. These performance measures are compared with those of a standard algorithm based on Gaussian smoothing and those of a second algorithm that also considers the spatial structure of noise. The algorithms prove equally good at suppressing noise, but are better able to detect faint or blurred features. These predictions are confirmed by empirical tests on real images.
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