Haralick, R.M., and
Watson, L.T.,
A Facet Model for Image Data,
CGIP(15), No. 2, February 1981, pp. 113-129.
WWW Version. Compared in:
See also Edge Detection and Linear Feature Extraction Using a 2-D Random Field Model.
BibRef
8102
Haralick, R.M.,
Second Directional Derivative Zero Crossing
Detector Using the Cubic Facet Model,
CVPR85(672-677).
One dimensional results are presented.
BibRef
8500
Haralick, R.M., and
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:
WWW Version.
Context Dependent Edge Detection,
CVPR88(223-228).
IEEE Abstract. IEEE Top Reference.
BibRef
And:
ICPR88(I: 203-207).
IEEE DOI Reference
IEEE Top Reference.
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.,
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 Abstract. IEEE Top Reference.
WWW Version.
9705
BibRef
Earlier:
Edge Detection and Curve Enhancement Using the Facet Model and
Parameterized Relaxation Labeling,
ICPR94(A:1-5).
IEEE DOI Reference 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
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.
WWW Version.
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.
WWW Version.
0201
BibRef
Ji, Q.A.[Qi-Ang],
Haralick, R.M.[Robert M.],
Quantitative Evaluation of Edge Detectors Using the Minimum Kernel
Variance Criterion,
ICIP99(II:705-709).
IEEE Abstract. IEEE Top Reference.
BibRef
9900
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 .