Finding Edges and Lines in Images

The problem of detecting intensity changes in images is canonical in vision. Edge detection operators are typically designed to optimally estimate first or second derivative over some (usually small) support. Other criteria such as output signal to noise ratio or bandwidth have also been argue...

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Main Author: Canny, John Francis
Language:en_US
Published: 2004
Online Access:http://hdl.handle.net/1721.1/6939
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author Canny, John Francis
author_facet Canny, John Francis
author_sort Canny, John Francis
collection MIT
description The problem of detecting intensity changes in images is canonical in vision. Edge detection operators are typically designed to optimally estimate first or second derivative over some (usually small) support. Other criteria such as output signal to noise ratio or bandwidth have also been argued for. This thesis is an attempt to formulate a set of edge detection criteria that capture as directly as possible the desirable properties of an edge operator. Variational techniques are used to find a solution over the space of all linear shift invariant operators. The first criterion is that the detector have low probability of error i.e. failing to mark edges or falsely marking non-edges. The second is that the marked points should be as close as possible to the centre of the true edge. The third criterion is that there should be low probability of more than one response to a single edge. The technique is used to find optimal operators for step edges and for extended impulse profiles (ridges or valleys in two dimensions). The extension of the one dimensional operators to two dimentions is then discussed. The result is a set of operators of varying width, length and orientation. The problem of combining these outputs into a single description is discussed, and a set of heuristics for the integration are given.
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spelling mit-1721.1/69392019-04-10T14:25:54Z Finding Edges and Lines in Images Canny, John Francis The problem of detecting intensity changes in images is canonical in vision. Edge detection operators are typically designed to optimally estimate first or second derivative over some (usually small) support. Other criteria such as output signal to noise ratio or bandwidth have also been argued for. This thesis is an attempt to formulate a set of edge detection criteria that capture as directly as possible the desirable properties of an edge operator. Variational techniques are used to find a solution over the space of all linear shift invariant operators. The first criterion is that the detector have low probability of error i.e. failing to mark edges or falsely marking non-edges. The second is that the marked points should be as close as possible to the centre of the true edge. The third criterion is that there should be low probability of more than one response to a single edge. The technique is used to find optimal operators for step edges and for extended impulse profiles (ridges or valleys in two dimensions). The extension of the one dimensional operators to two dimentions is then discussed. The result is a set of operators of varying width, length and orientation. The problem of combining these outputs into a single description is discussed, and a set of heuristics for the integration are given. 2004-10-20T20:08:44Z 2004-10-20T20:08:44Z 1983-06-01 AITR-720 http://hdl.handle.net/1721.1/6939 en_US AITR-720 13632244 bytes 9728427 bytes application/postscript application/pdf application/postscript application/pdf
spellingShingle Canny, John Francis
Finding Edges and Lines in Images
title Finding Edges and Lines in Images
title_full Finding Edges and Lines in Images
title_fullStr Finding Edges and Lines in Images
title_full_unstemmed Finding Edges and Lines in Images
title_short Finding Edges and Lines in Images
title_sort finding edges and lines in images
url http://hdl.handle.net/1721.1/6939
work_keys_str_mv AT cannyjohnfrancis findingedgesandlinesinimages