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Keith Phillips

dblp:24/737 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 1989
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
2 papers
Image and video processing · 77% Geometric modeling and processing · 23%
Theoretical computer science
2 papers
Computational geometry · 100%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Image and video processing
edge detection
0.021989
The Geometry of Differential Operators with Application to Image Processing · IEEE Trans. Pattern Anal. Mach. Intell. 1989
Applications of Vector Fields to Image Processing · IEEE Trans. Pattern Anal. Mach. Intell. 1983
Geometric modeling and processing › shape analysis
curvature analysis
0.011983
Applications of Vector Fields to Image Processing · IEEE Trans. Pattern Anal. Mach. Intell. 1983
Image and video processing › feature extraction
image feature extraction
0.011983
Applications of Vector Fields to Image Processing · IEEE Trans. Pattern Anal. Mach. Intell. 1983
Geometric modeling and processing
vector field analysis
0.011983
Applications of Vector Fields to Image Processing · IEEE Trans. Pattern Anal. Mach. Intell. 1983
Computational geometry
differential geometry
0.021989
The Geometry of Differential Operators with Application to Image Processing · IEEE Trans. Pattern Anal. Mach. Intell. 1989
Applications of Vector Fields to Image Processing · IEEE Trans. Pattern Anal. Mach. Intell. 1983

Methods — techniques the papers use, named apart from their topics

vector calculus · 0.0differential geometry · 0.0b-spline interpolation · 0.0vector analysis · 0.0topological degree · 0.0rotation number · 0.0
YearPublicationVenuePosition
1989 The Geometry of Differential Operators with Application to Image Processing
abstract
A family of second-order nonlinear differential operators that are useful in building simple edge detectors is treated. The authors provide a uniform definition for these operators and describe their internal geometry. Based on their geometric analysis, they associate symbolic descriptors with the abstract edges defined by the operators. One of them, Q, is essentially the same operator that J.F. Canny (ibid., vol.PAMI-8, p.552-7, 1986) used to define his two-dimensional simple edge detector. The authors give a parse tree for Q, which provides a complete analysis of its local geometry. The analysis shows that Q has a fondness for edges that lift to certain types of asymptotic curves. These include select straight line segments lying on the surface, inflections of mean curvature, and paths with constant rate of ascent. The methods are drawn largely from vector calculus and differential geometry. The authors illustrate their theoretical work with results obtained when Q was applied to image data that were interpolated with a B-spline.>
Warren M. Kruegger, Keith Phillips
IEEE Trans. Pattern Anal. Mach. Intell.2
1983 Applications of Vector Fields to Image Processing
abstract
We use rotational and curvature properties of vector fields to identify critical features of an image. Using vector analysis and dif-ferential geometry, we establish the properties needed, and then use these properties in three ways. First, our results make it theoretically possible to identify extremal edges of an intensity function f(x, y) of two variables by considering the gradient vector field V = ¿f. There is also enough information in ¿f to find regions of high curvature (i.e., high curvature of the level paths of f). For color images, we use the vector field V = (I, Q). In application, the image is partitioned into a grid of squares. On the boundary of each square, V/|V| is sampled, and these unit vectors are used as the tangents of a curve ¿. The rotation number (or topological degree) ¿(¿) and the average curvature f|¿¿| are computed for each square. Analysis of these numbers yields infor-mation on edges and curvature. Experimental results from both simu-lated and real data are described.
Raul Machuca, Keith Phillips
IEEE Trans. Pattern Anal. Mach. Intell.2