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C. Peng

dblp:48/6321 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1995
—ORCID · none

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

Artificial intelligence and machine learning · 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
1 paper
Image and video processing · 67% Multimedia analysis and retrieval · 33%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

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

TopicWeightPapersLastEvidence papers
Image and video processing › image segmentation
contour refinement
0.011995
Tracking of Tubular Molecules for Scientific Applications · IEEE Trans. Pattern Anal. Mach. Intell. 1995
Image and video processing
image segmentation
0.011995
Tracking of Tubular Molecules for Scientific Applications · IEEE Trans. Pattern Anal. Mach. Intell. 1995
Multimedia analysis and retrieval › video analysis
object detection and tracking
0.011995
Tracking of Tubular Molecules for Scientific Applications · IEEE Trans. Pattern Anal. Mach. Intell. 1995
Bioinformatics and computational biology › bioimage informatics › bioimage analysis
molecular image analysis
0.011995
Tracking of Tubular Molecules for Scientific Applications · IEEE Trans. Pattern Anal. Mach. Intell. 1995

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

symmetry axis detection · 0.0dynamic programming · 0.0bounding polygon · 0.0
YearPublicationVenuePosition
1995 Tracking of Tubular Molecules for Scientific Applications
abstract
In this paper, we present a system for detection and tracking of tubular molecules in images. The automatic detection and characterization of the shape, location, and motion of these molecules can enable new laboratory protocols in several scientific disciplines. The uniqueness of the proposed system is twofold: At the macro level, the novelty of the system lies in the integration of object localization and tracking using geometric properties; at the micro level, in the use of high and low level constraints to model the detection and tracking subsystem. The underlying philosophy for object detection is to extract perceptually significant features from the pixel level image, and then use these high level cues to refine the precise boundaries. In the case of tubular molecules, the perceptually significant features are antiparallel line segments or, equivalently, their axis of symmetries. The axis of symmetry infers a coarse description of the object in terms of a bounding polygon. The polygon then provides the necessary boundary condition for the refinement process, which is based on dynamic programming. For tracking the object in a time sequence of images, the refined contour is then projected onto each consecutive frame.>
Bahram Parvin, C. Peng, William E. Johnston, Marcos Maestre
IEEE Trans. Pattern Anal. Mach. Intell.2