Haralick, R.M.[Robert M.],
Watson, L.T.[Layne T.],
A Facet Model for Image Data,
CGIP(15), No. 2, February 1981, pp. 113-129.
Elsevier DOI Compared in:
See also Edge Detection and Linear Feature Extraction Using a 2-D Random Field Model.
See also Edge and Region Analysis for Digital Image Data.
BibRef
8102
Haralick, R.M.[Robert M.],
Digital Step Edges from Zero-Crossings of Second
Directional Derivatives,
PAMI(6), No. 1, January 1984, pp. 58-68.
BibRef
8401
And:
RCV87(216-226).
BibRef
And:
Reply to Comments:
PAMI(7), No. 1, January 1985, pp. 127-129.
BibRef
Earlier:
Second Directional Derivative Zero Crossing
Detector Using the Cubic Facet Model,
CVPR85(672-677).
BibRef
Earlier:
Zero-Crossing of Second Directional Derivative Edge Operator,
SPIE(xx), Robot Vision, 1982.
Facet Model. The comparison is based on an inaccurate implementation of the
See also Theory of Edge Detection. algorithm based on incomplete details in their paper.
This paper caused some controversy. A facet model application to
edges paper.
See also The Facet Model for Descriptions.
See also Comments On Digital Step Edges from Zero Crossings of Second Directional Derivatives.
BibRef
Haralick, R.M.[Robert M.],
The Digital Edge,
PRIP81(285-291).
BibRef
8100
Haralick, R.M.[Robert M.],
Lee, J.S.J.[James S.J.],
Context Dependent Edge Detection and Evaluation,
PR(23), No. 1/2, 1990, pp. 1-19.
BibRef
9000
Earlier:
Elsevier DOI
Context Dependent Edge Detection,
CVPR88(223-228).
IEEE DOI
BibRef
And:
ICPR88(I: 203-207).
IEEE DOI
Edges, Evaluation. Use the best interpretation based on all edge directions
through a pixel (or something like that).
BibRef
Lee, J.S.,
Haralick, R.M.[Robert M.],
Shapiro, L.G.,
Morphologic Edge Detection,
RA(3), 1987, pp. 142-156.
BibRef
8700
Matalas, I.[Ioannis],
Benjamin, R.[Ralph],
Kitney, R.[Richard],
An Edge-Detection Technique Using the Facet Model and
Parameterized Relaxation Labeling,
PAMI(19), No. 4, April 1997, pp. 328-341.
IEEE DOI
9705
BibRef
Earlier:
Edge Detection and Curve Enhancement Using the Facet Model and
Parameterized Relaxation Labeling,
ICPR94(A:1-5).
IEEE DOI First a variant of the cubic facet model detects the location, orientation
and curvature of the edge. Then relaxation cleans it up, and maximizes
connected contours.
BibRef
Zuniga, O.A.[Oscar A.],
Haralick, R.M.[Robert M.],
Gradient Threshold Selection Using the Facet Model,
PR(21), No. 5, 1988, pp. 493-503.
Elsevier DOI
BibRef
8800
Earlier:
Corner Detection Using the Facet Model,
CVPR83(30-37).
Corner Detector, Evaluation. Evaluation of corner detectors. For the facet model,
gray values are from a 3D surface.
BibRef
Li, C.H.,
Tam, P.K.S.,
A global energy approach to facet model and its minimization using
weighted least-squares algorithm,
PR(33), No. 2, February 2000, pp. 281-293.
Elsevier DOI
0001
BibRef
Ji, Q.A.[Qi-Ang],
Haralick, R.M.[Robert M.],
Efficient facet edge detection and quantitative performance evaluation,
PR(35), No. 3, March 2002, pp. 689-700.
Elsevier DOI
0201
BibRef
Earlier:
Quantitative Evaluation of Edge Detectors Using the Minimum Kernel
Variance Criterion,
ICIP99(II:705-709).
IEEE Abstract.
BibRef
Sher, D.B.[David B.],
Tunable Facet Model Likelihood Generators for Boundary Pixel Detection,
CVWS87(35-40).
BibRef
8700
And:
Generating Robust Operators from Specialized Ones,
CVWS87(301-303).
BibRef
Earlier:
Optimal Likelihood Generators for Edge Detection under
Gaussian Additive Noise,
CVPR86(94-99).
The facet model is used and can be adjusted for
various properties in the data.
BibRef
Chapter on Edge Detection and Analysis, Lines, Segments, Curves, Corners, Hough Transform continues in
Evaluation of Edge Detection Algorithms .