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Sridhar R. Kundur

dblp:82/6168 · DBLP profile ↗
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5ranked-venue papers
5as first author
0since 2021 · last 2000
—ORCID · none

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

Artificial intelligence and machine learning · 5 · 5 first-authorGraphics, computer vision, multimedia, augmented reality and games · 2 · 2 first-author

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
2 papers
Robot navigation and mapping · 77% Motion planning and robot control · 23%

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

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping
visual navigation
0.021997
An image-based visual-motion-cue for autonomous navigatio · CVPR 1997
Novel active-vision-based visual-threat-cue for autonomous navigation tasks · CVPR 1996
Robotics › Robot navigation and mapping › visual perception for robotics
time-to-contact estimation
0.011997
An image-based visual-motion-cue for autonomous navigatio · CVPR 1997
Robotics › Motion planning and robot control
collision avoidance
0.011996
Novel active-vision-based visual-threat-cue for autonomous navigation tasks · CVPR 1996
YearPublicationVenuePosition
2000 Active vision-based control schemes for autonomous navigation tasks
Sridhar R. Kundur, Daniel Raviv
Pattern Recognit.1
1999 Novel Active Vision-Based Visual Threat Cue for Autonomous Navigation Tasks
Sridhar R. Kundur, Daniel Raviv
Comput. Vis. Image Underst.1
1998 A Vision-Based Pragmatic Strategy For Autonomous Navigation
Sridhar R. Kundur, Daniel Raviv
Pattern Recognit.1
1997 An image-based visual-motion-cue for autonomous navigatio
abstract
This paper presents a novel time-based visual motion cue called the Hybrid Visual Threat Cue (HVTC) that provides some measure for a change in relative range as well as absolute clearances, between a 3D surface and a moving observer. It is shown that the HVTC is a linear combination of Time-To-Contact (TTC), visual looming and the Visual Threat Cue (VTC). The visual field associated with the HVTC can be used to demarcate the regions around a moving observer into safe and danger zones of varying degree, which may be suitable for autonomous navigation tasks. The HVTC is independent of the 3D environment and needs almost no a-priori information about it. It is rotation independent, and is measured in ~time/sup -1/\ units Several approaches to extract the HVTC, are suggested. Also a practical method to extract it from a sequence of images of a 3D textured surface obtained by a visually fixating, fixed-focus monocular camera in motion is presented. This approach of extracting the HVTC is independent of the type of 3D surface texture and needs no optical flow information, 3D reconstruction, segmentation, feature tracking.
Sridhar R. Kundur, Daniel Raviv, Ernest Kent
CVPR1
1996 Novel active-vision-based visual-threat-cue for autonomous navigation tasks
abstract
This paper presents a new visual motion cue, we call the Visual Threat Cue (VTC) that provides some measure for a relative change in range as well as clearance between a 3D surface and a fixing observer in motion. The VTC corresponds to visual fields surrounding a moving observer. The fields are time-based imaginary 3-D surfaces that move with the observer. They are analogous to equi-potential fields of an electric dipole. A practical method to extract the VTC is presented. The approach is independent of the 3D surface texture and needs no optical flow information, 3D reconstruction, segmentation, feature tracking or pre-processing. This algorithm to extract the VTC was applied to several indoor as well as outdoor real images of textures, where we observed a similar behavior for most of the textures employed.
Sridhar R. Kundur, Daniel Raviv
CVPR1