Fram, J.R.,
Deutsch, E.S.,
On the Quantitative Evaluation of Edge Detection Schemes and Their
Comparisons with Human Performance,
TC(24), No. 6, June 1975, pp. 616-627.
Ground truth, comparison using vertical step edge.
BibRef
7506
Deutsch, E.S.,
Fram, J.R.,
A Quantitative Study of the
Orientational Bias of Some Edge Detector Schemes,
TC(27), No. 3, March 1978, pp. 205-213.
And comments below.
BibRef
7803
MacLeod, I.D.G.,
Comments on 'A Quantitative Study of the Orientation Bias of
Some Edge Detector Schemes',
PAMI(1), No. 4, October 1979, 408-409.
Comment on above paper.
BibRef
7910
Abdou, I.E.,
Pratt, W.K.,
Qualitative Design and
Evaluation of Enhancement/Thresholding Edge Detector,
PIEEE(67), No. 5, May 1979, pp. 753-763.
Design of edge evaluation experiments.
Evaluation for horizontal, vertical and diagonal step edges.
BibRef
7905
Abdou, I.E.,
Quantitative Methods of Edge Detection,
Ph.D.July 1978.
BibRef
7807
USC
BibRef
Panda, D.P.[Durga P.],
Dubitzki, T.[Tsvi],
Statistical Analysis of Some Edge Operators,
CGIP(11), No. 4, December 1979, pp. 313-348.
Elsevier DOI
BibRef
7912
Peli, T.[Tamar],
Malah, D.[David],
A Study of Edge Detection Algorithms,
CGIP(20), No. 1, September 1982, pp. 1-21.
Elsevier DOI
BibRef
8209
Basseville, M.,
Benveniste, A.,
Design and Comparative Study of Some Sequential Jump Detection Algorithms
for Digital Signals,
ASSP(31), 1983, pp. 521-535.
BibRef
8300
Bernsen, J.A.C.,
An Objective and Subjective Evaluation of Edge Detection Methods
in Images,
Phillips J. Res.(46), 1991, pp. 57-94.
BibRef
9100
Fleck, M.M.[Margaret M.],
Some Defects in Finite-Difference Edge Finders,
PAMI(14), No. 3, March 1992, pp. 337-345.
IEEE DOI
Edges, Evaluation.
Analysis of
See also Computational Approach to Edge Detection, A.
See also Two Dimensional Optimal Edge Recognition Using Matched and Weiner Filters for Machine Vision. and
See also Theory of Edge Detection. with regard to
gaps, deformations, and spurious boundaries. The results are
similar, all have major problems due to design, etc. Interesting
closing question that the unpublished details are more important
the the shape of the filters.
BibRef
9203
Zhou, Y.T.,
Venkateswar, V.,
Chellappa, R.,
Edge Detection and Linear Feature Extraction Using a 2-D
Random Field Model,
PAMI(11), No. 1, January 1989, pp. 84-95.
IEEE DOI Comparison of the results with
See also Linear Feature Extraction and Description.
See also Facet Model for Image Data, A.
See also Computational Approach to Edge Detection, A. and
See also Theory of Edge Detection. Find edges and then find long straight lines.
BibRef
8901
Kanungo, T.,
Jaisimha, M.Y.,
Palmer, J.,
Haralick, R.M.[Robert M.],
A Methodology for Quantitative Performance Evaluation
of Detection Algorithms,
IP(4), No. 12, December 1995, pp. 1667-1674.
IEEE DOI
BibRef
9512
Earlier:
A Quantitative Methodology for Analyzing the
Performance of Detection Algorithms,
ICCV93(247-252).
IEEE DOI Vertical edge with added noise.
BibRef
Ramesh, V.,
Haralick, R.M.[Robert M.],
Performance Characterization of Edge Operators,
DARPA93(1071-1079).
BibRef
9300
And:
Performance Characterization of Edge Detectors,
SPIE(1708), April 1992, pp. 252-266.
A ramp edge embedded in noise.
BibRef
Haralick, R.M.[Robert M.],
Ramesh, V.[Visvanathan],
An Integrated Gradient Edge Detector --
Theory and Performance Evaluation,
ARPA94(I:689-702).
BibRef
9400
Ramesh, V.,
Haralick, R.M.[Robert M.],
Zhang, X.,
Nadadur, D.C.,
Thornton, K.B.,
Automatic Selection of Tuning Parameters for
Feature Extraction Sequences,
CVPR94(672-677).
IEEE DOI
BibRef
9400
Cho, K.,
Meer, P.,
Cabrera, J.,
Performance Assessment Through Bootstrap,
PAMI(19), No. 11, November 1997, pp. 1185-1198.
IEEE DOI
9712
BibRef
And:
Correction:
PAMI(20), No. 1, January 1998, pp. 94.
IEEE DOI
BibRef
Earlier:
Quantitative Evaluation of Performance Through Bootstrapping:
Edge Detection,
SCV95(491-496).
IEEE DOI Rutgers University.
Evaluation of detectors using the same edge model.
Bootstrap is a resampling technique.
BibRef
Heath, M.D.[Mike D.],
Sarkar, S.[Sudeep],
Sanocki, T.A.[Thomas A.], and
Bowyer, K.W.[Kevin W.],
A Robust Visual Method for Assessing the Relative Performance of
Edge Detection Algorithms,
PAMI(19), No. 12, December 1997, pp. 1338-1359.
IEEE DOI
9712
Table of 21 recent algorithms with what they compared to and how
many examples. They did not show many results or make many
comparisons. Also lists 12 comparison papers.
Compares:
Canny (
See also Computational Approach to Edge Detection, A. ),
Nalwa-Binford (
See also On Detecting Edges. ),
Iverson-Zucker (
See also Logical/Linear Operators for Image Curves. ),
Bergholm (
See also Edge Focusing. ),
and
Rothwell. (
See also Driving Vision by Topology. ).
Visual (human) comparison of results on real images with a variety
of input parameters for each.
BibRef
Sanocki, T.A.[Thomas A.],
Bowyer, K.W.[Kevin W.],
Heath, M.D.[Mike D.],
Sarkar, S.[Sudeep],
Are Edges Sufficient for Object Recognition,
JEP:HPP(24), No. 1, 1998, pp. 340-349.
Edges as line drawings are not reality, edges as outputs of
local edge extractors are. These are not good enough (50%
performance in a recognition task).
BibRef
9800
Heath, M.D.[Mike D.],
Sarkar, S.[Sudeep],
Sanocki, T.A.[Thomas A.],
Bowyer, K.W.[Kevin W.],
Comparison of Edge Detectors,
CVIU(69), No. 1, January 1998, pp. 38-54.
DOI Link Earlier version of
See also Robust Visual Method for Assessing the Relative Performance of Edge Detection Algorithms, A. with fewer images and edge detectors and less rigorous
sampling of parameter space.
BibRef
9801
Heath, M.D.[Mike D.],
Sarkar, S.[Sudeep],
Sanocki, T.A.[Thomas A.], and
Bowyer, K.W.[Kevin W.],
Comparison of Edge Detectors: A Methodology and Initial Study,
CVPR96(143-148).
IEEE DOI Use humans to rate the image algorithms. Pairwise comparisons.
Canny (
See also Computational Approach to Edge Detection, A. ),
Nalwa-Binford (
See also On Detecting Edges. ),
Sarkar-Boyer (
See also On Optimal Infinite Impulse Response Edge Detection Filters. ),
Sobel (
See also Visual Perception by a Computer. ).
The article above extends these results with a better set
of edge detectors.
BibRef
9600
Dougherty, S.[Sean],
Bowyer, K.W.[Kevin W.],
Objective Evaluation of Edge Detectors Using
a Formally Defined Framework,
EEMTV98(xx).
BibRef
9800
And:
EEMCV98(xx).
Uses Real Images, hand-specified ground truth, ROC analysis of true
positive and false positives. 6 edge detectors. General conclusion
is that the reputation of Canny (
See also Computational Approach to Edge Detection, A. ) is deserved.
Heitger (
See also Feature Detection using Suppression and Enhancement. ) gives good results.
Also included
Bergholm (
See also Edge Focusing. ),
Rothwell (
See also Driving Vision by Topology. ).
Sobel (
See also Visual Perception by a Computer. ),
Sarkar-Boyer (
See also On Optimal Infinite Impulse Response Edge Detection Filters. ).
BibRef
Bowyer, K.W.[Kevin W.],
Kranenburg, C.[Christine],
Dougherty, S.[Sean],
Edge Detector Evaluation Using Empirical ROC Curves,
CVIU(84), No. 1, October 2001, pp. 77-103.
DOI Link
0203
BibRef
Earlier:
CVPR99(I: 354-359).
IEEE DOI Explains the method of evaluation, used in the next one.
Canny (
See also Computational Approach to Edge Detection, A. ),
Heitger (
See also Feature Detection using Suppression and Enhancement. ) give the best results.
Also included
Bergholm (
See also Edge Focusing. ),
Rothwell (
See also Driving Vision by Topology. ).
Black (
See also Robust Anisotropic Diffusion. ),
Sobel (
See also Visual Perception by a Computer. ),
Susan (
See also Susan: A New Approach to Low-Level Image-Processing. ).
BibRef
Dougherty, S.,
Bowyer, K.W.,
Kranenburg, C.,
ROC curve evaluation of edge detector performance,
ICIP98(II: 525-529).
IEEE DOI
9810
BibRef
Shin, M.C.[Min C.],
Goldgof, D.B.[Dmitry B.],
Bowyer, K.W.[Kevin W.],
Comparison of Edge Detector Performance through Use in an Object
Recognition Task,
CVIU(84), No. 1, October 2001, pp. 160-178.
DOI Link
0203
BibRef
Earlier:
Comparison of Edge Detectors Using an Object Recognition Task,
CVPR99(I: 360-365).
IEEE DOI Cross refs for CVPR version, I assume they are the same papers.
Use the recognition method of Huttenlocher, et al.
(
See also Comparing Images Using the Hausdorff Distance. ).
Using edge detectors from:
Bergholm (
See also Edge Focusing. ),
Canny (
See also Computational Approach to Edge Detection, A. ),
Heitger (
See also Feature Detection using Suppression and Enhancement. ),
Sobel (
See also Visual Perception by a Computer. ),
Susan (
See also Susan: A New Approach to Low-Level Image-Processing. ).
Training is essential. Conclusions are
different from the other studies.
BibRef
Shin, M.C.[Min C.],
Goldgof, D.B.[Dmitry B.],
Bowyer, K.W.[Kevin W.],
Nikiforou, S.,
Comparison of Edge Detection Algorithms Using a
Structure from Motion Task,
SMC-B(31), No. 4, August 2001, pp. 589-601.
IEEE Top Reference.
0109
Use the structure from motion accuracy to test edge detectors.
Results are that these results are well correlated wht the
results on pixel-level metrics. Canny (
See also Computational Approach to Edge Detection, A. )and
Heitger (
See also Feature Detection using Suppression and Enhancement. )detector offer
the best performance.
BibRef
Shin, M.C.[Min Chul],
Goldgof, D.B.[Dmitry B.],
Bowyer, K.W.[Kevin W.],
An Objective Comparison Methodology of Edge Detection Algorithms
Using a Structure from Motion Task,
EEMCV98(xx).
BibRef
9800
Earlier:
CVPR98(190-195).
IEEE DOI Bergholm (
See also Edge Focusing. ),
Canny (
See also Computational Approach to Edge Detection, A. ),
Rothwell. (
See also Driving Vision by Topology. ) and
Sarkar (
See also On Optimal Infinite Impulse Response Edge Detection Filters. ).
BibRef
Shin, M.C.[Min Chul],
Goldgof, D.B.[Dmitry B.],
Bowyer, K.W.[Kevin W.],
Evaluation of Edge Detection Algorithms Using
a Structure from Motion Task,
EEMCV98(xx).
BibRef
9800
And:
An Objective Comparison Methodology of Edge Detection Algorithms
Using a Structure from Motion Task,
EEMTV98(xx)
BibRef
Ziou, D.[Djemel],
Koukam, A.,
Knowledge Based Assistant for the Selection of Edge Detectors,
PR(31), No. 5, May 1998, pp. 587-596.
Elsevier DOI
9805
See also influence of edge direction on the estimation of edge contrast and orientation, The.
BibRef
Nguyen, T.B.,
Ziou, D.,
Contextual and non-contextual performance evaluation of edge detectors,
PRL(21), No. 9, August 2000, pp. 805-816.
0008
BibRef
Medina Carnicer, R.,
Madrid Cuevas, F.J.,
Fernández García, N.L.,
Carmona Poyato, A.,
Evaluation of global thresholding techniques in non-contextual edge
detection,
PRL(26), No. 10, 15 July 2005, pp. 1423-1434.
Elsevier DOI
0506
BibRef
Medina-Carnicer, R.,
Madrid-Cuevas, F.J.,
Munoz-Salinas, R.,
Carmona-Poyato, A.,
Solving the process of hysteresis without determining the optimal
thresholds,
PR(43), No. 4, April 2010, pp. 1224-1232.
Elsevier DOI
1002
Hysteresis; Thresholding; Edge detection
BibRef
Chabrier, S.[Sébastien],
Laurent, H.[Hélène],
Rosenberger, C.[Christophe],
Emile, B.[Bruno],
Comparative Study of Contour Detection Evaluation Criteria Based on
Dissimilarity Measures,
JIVP(2008), No. 2008, pp. xx-yy.
DOI Link
0804
BibRef
Benezeth, Y.[Yannick],
Hemery, B.[Baptiste],
Laurent, H.[Hélène],
Emile, B.[Bruno],
Rosenberger, C.[Christophe],
Evaluation of Human Detection Algorithms in Image Sequences,
ACIVS10(II: 121-130).
Springer DOI
1012
BibRef
Benezeth, Y.,
Jodoin, P.M.,
Emile, B.,
Laurent, H.,
Rosenberger, C.,
Human Detection with a Multi-sensors Stereovision System,
ICISP10(228-235).
Springer DOI
1006
BibRef
Benezeth, Y.[Yannick],
Emile, B.[Bruno],
Laurent, H.[Hélène],
Rosenberger, C.[Christophe],
A Real Time Human Detection System Based on Far Infrared Vision,
ICISP08(76-84).
Springer DOI
0807
BibRef
Hemery, B.[Baptiste],
Laurent, H.[Hélène],
Rosenberger, C.[Christophe],
Emile, B.[Bruno],
Evaluation Protocol for Localization Metrics:
Application to a Comparative Study,
ICISP08(273-280).
Springer DOI
0807
BibRef
Javhar, A.[Aminov],
Chen, X.[Xi],
Bao, A.[Anming],
Jamshed, A.[Aminov],
Yunus, M.[Mamadjanov],
Jovid, A.[Aminov],
Latipa, T.[Tuerhanjiang],
Comparison of Multi-Resolution Optical Landsat-8, Sentinel-2 and
Radar Sentinel-1 Data for Automatic Lineament Extraction: A Case
Study of Alichur Area, SE Pamir,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link
1904
BibRef
Mazumdar, M.,
Sinha, B.K.,
Li, C.C.,
A Comparison of Several Estimators of Edge Point in Noisy
Digital Data Across a Step Edge,
CVPR85(27-33). (Univ. of Pittsburgh)
Compare:
moments, maximum likelihood, and Bayes for edge detection in 1-D data.
BibRef
8500
Chapter on Edge Detection and Analysis, Lines, Segments, Curves, Corners, Hough Transform continues in
Boundaries - More Than Simple Edge Points, Linking .