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High-probability grants
According to our matching algorithm, Manish Singh is the likely recipient of the following grants.
Years |
Recipients |
Code |
Title / Keywords |
Matching score |
2002 — 2005 |
Singh, Manish |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Visual Computation of Surfaces Under Partial Occlusion and Transparency @ Rutgers University New Brunswick
With National Science Foundation support, Dr. Manish Singh will conduct two years of research on visual perception-how we see a complex, changing world of overlapping objects. The perceived world includes three dimensional objects and surfaces. The inputs to our eyes, however, are not "objects" or "surfaces," but fluctuations in the two-dimensional arrays of light projected onto each retina. These retinal arrays change a great deal as we move in the world-as a couch gets partly hidden behind a coffee table when walking across a room, or a dog is partly seen behind thick foliage. A fundamental and unsolved problem of vision is to understand how our brain can sort out such complex changing light arrays into perceived surfaces and objects.
The funded research uses computer-generated stereoscopic displays in which some objects are partly occluded by other objects, and thus present only disjoined portions of their boundaries to the eyes. Despite this, observers perceive complete, unitary, objects-not disparate fragments. One aspect of the research program examines constraints involving both surface shape and contour geometry that may determine the continuity and shape of partly occluded objects. A second aspect of this research concerns when an occluding object is partially transparent. In this case, light reflected from two different surfaces (the transparent, and the underlying opaque surface) is merged at the retinas, but we see two separate surfaces placed at different depths along the same line of sight. This component of the research investigates the geometric (figural) and photometric (light intensity) constraints that the brain uses to split image luminance into two distinct surfaces placed at different depths.
This research will enhance understanding of a central aspect of human vision. Additionally, it will contribute valuable knowledge that can be used to construct artificial vision systems for recovery of surface layouts from camera images.
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1 |
2006 — 2011 |
Decarlo, Douglas Stone, Matthew (co-PI) [⬀] Singh, Manish |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Depiction and Perception of Shape in Line Drawings @ Rutgers University New Brunswick
Depiction and Perception of Shape in Line Drawings Doug DeCarlo, Manish Singh and Matthew Stone Rutgers University
Abstract
Because they can focus on what is essential about a scene, line drawings are a fundamental way of conveying shape that are often preferred over realistic representations,. This research couples the development of computer-based methods to synthesize line drawings with experiments on human observers that reveal how line drawings are perceived and interpreted. The ultimate goal is to produce depictions of 3D shape that are easier to understand. By improving line drawings, this research improves a source of communicative imagery that could become ubiquitous---appearing in better maps, more flexible and customizable technical and medical illustration, more broadly accessible scientific visualization, and many other applications. At the same time, new interactive techniques promise to make computer representation of 3D objects accessible to a wider range of users for a wider range of tasks and on a wider range of platforms.
This research involves three interrelated thrusts: (1) Computational studies are used to characterize the ambiguity inherent in interpreting line drawings and to develop new line styles that aim to reduce this ambiguity; (2) Experimental studies that probe the visual perception of 3D shape are used to discover what shape information human viewers gather from line drawings; and (3) The investigators are developing applications that exploit a perceptual model derived from the experimental studies in order to guide the placement of lines in drawings, and to guide the interpretation of hand-drawn sketches for interactive model construction.
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1 |