6.1.9 Facet Model for Edge Detection

Chapter Contents (Back)
Facet Model. ; Edges, Facet Model. See also The Facet Model for Descriptions.

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.
WWW Link. 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
Second Directional Derivative Zero Crossing Detector Using the Cubic Facet Model,
CVPR85(672-677). BibRef
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
WWW Link.
Context Dependent Edge Detection,
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.
Edge Detection and Curve Enhancement Using the Facet Model and Parameterized Relaxation Labeling,
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.
WWW Link. BibRef 8800
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.
WWW Link. 0001

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 Link. 0201
Quantitative Evaluation of Edge Detectors Using the Minimum Kernel Variance Criterion,
IEEE Abstract. BibRef

Worthington, P.L.,
Enhanced Canny edge detection using curvature consistency,
ICPR02(I: 596-599).

Sher, D.B.[David B.],
Tunable Facet Model Likelihood Generators for Boundary Pixel Detection,
CVWS87(35-40). BibRef 8700
Generating Robust Operators from Specialized Ones,
CVWS87(301-303). BibRef
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 .

Last update:Dec 28, 2017 at 17:11:31