Mark Stuff

dblp:96/7583 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2009
—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.

Artificial intelligence
1 paper
3D vision · 100%
Computer graphics and multimedia
1 paper
Image and video processing · 100%

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

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision
3d reconstruction
0.112009
Shape and Motion Reconstruction from 3D-to-1D Orthographically Projected Data via Object-Image Relations · IEEE Trans. Pattern Anal. Mach. Intell. 2009
Computer vision › 3D vision
shape and motion recovery
0.112009
Shape and Motion Reconstruction from 3D-to-1D Orthographically Projected Data via Object-Image Relations · IEEE Trans. Pattern Anal. Mach. Intell. 2009
Computer vision › 3D vision
structure from motion
0.112009
Shape and Motion Reconstruction from 3D-to-1D Orthographically Projected Data via Object-Image Relations · IEEE Trans. Pattern Anal. Mach. Intell. 2009
Image and video processing › radar imaging
inverse synthetic aperture radar imaging
0.012009
Shape and Motion Reconstruction from 3D-to-1D Orthographically Projected Data via Object-Image Relations · IEEE Trans. Pattern Anal. Mach. Intell. 2009

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

object-image relations · 0.2invariant-based reconstruction · 0.2
YearPublicationVenuePosition
2009 Shape and Motion Reconstruction from 3D-to-1D Orthographically Projected Data via Object-Image Relations
abstract
This paper describes an invariant-based shape- and motion reconstruction algorithm for 3D-to-1D orthographically projected range data taken from unknown viewpoints. The algorithm exploits the object-image relation that arises in echo-based range data and represents a simplification and unification of previous work in the literature. Unlike one proposed approach, this method does not require uniqueness constraints, which makes its algorithmic form independent of the translation removal process (centroid removal, range alignment, etc.). The new algorithm, which simultaneously incorporates every projection and does not use an initialization in the optimization process, requires fewer calculations and is more straightforward than the previous approach. Additionally, the new algorithm is shown to be the natural extension of the approach developed by Tomasi and Kanade for 3D-to-2D orthographically projected data and is applied to a realistic inverse synthetic aperture radar imaging scenario, as well as experiments with varying amounts of aperture diversity and noise.
Matthew Ferrara, Gregory Arnold, Mark Stuff
IEEE Trans. Pattern Anal. Mach. Intell.3