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Richard M. Goldberg

dblp:05/5285 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2001
—ORCID · none

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

Artificial intelligence and machine learning · 2Graphics, computer vision, multimedia, augmented reality and games · 1

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
Visual content generation and editing · 54% Image and video processing · 39% Geometric modeling and processing · 6%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visual content generation and editing
image editing
0.122001
Image Editing in the Contour Domain · IEEE Trans. Pattern Anal. Mach. Intell. 2001
Image Editing in the Contour Domain · CVPR 1998
Image and video processing
edge detection
0.022001
Image Editing in the Contour Domain · IEEE Trans. Pattern Anal. Mach. Intell. 2001
Image Editing in the Contour Domain · CVPR 1998
Geometric modeling and processing › shape representation › 2d shape representation
contour representation
0.011998
Image Editing in the Contour Domain · CVPR 1998

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

contour grouping · 0.1user interface · 0.0edge map inversion · 0.0
YearPublicationVenuePosition
2001 Image Editing in the Contour Domain
abstract
We propose a novel method for image editing in which the primitive working unit is not a pixel but an edge. The feasibility of this proposal is suggested by the recent work of Elder et al. (1998) showing that a gray-scale image can be accurately represented by its edge map if a suitable edge model and scale selection method are employed. In particular, an efficient algorithm has been reported by Elder et al. (1996) and Elder (1999) to invert such an edge representation to yield a high-fidelity reconstruction of the original image. We combined these algorithms together with an efficient method for contour grouping and an intuitive user interface to allow users to perform image editing operations directly in the contour domain. Experimental results suggest that this novel combination of vision algorithms may increase the efficiency of certain classes of image editing operations.
James H. Elder, Richard M. Goldberg
IEEE Trans. Pattern Anal. Mach. Intell.2
1998 Image Editing in the Contour Domain
abstract
Image editing systems are essentially pixel-based. In this paper we propose a novel method for image editing in which the primitive working unit is not a pixel but an edge. The feasibility of this proposal is suggested by recent work showing that a grey-scale image can be accurately represented by its edge map if a suitable edge model and scale selection method are employed. In particular, an efficient algorithm has been reported to invert such an edge representation to yield a high-fidelity reconstruction of the original image. We have combined these algorithms together with an efficient method for contour grouping and an intuitive user interface to allow users to perform image editing operations directly in the contour domain. Experimental results suggest that this novel combination of vision algorithms may lead to substantial improvements in the efficiency of certain classes of image editing operations.
James H. Elder, Richard M. Goldberg
CVPR2