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Articles 1 - 6 of 6
Full-Text Articles in Physical Sciences and Mathematics
Globally Distributed Content Delivery, John Dilley, Bruce Maggs, Jay Parikh, Harald Prokop, Ramesh Sitaraman, Bill Weihl
Globally Distributed Content Delivery, John Dilley, Bruce Maggs, Jay Parikh, Harald Prokop, Ramesh Sitaraman, Bill Weihl
Ramesh Sitaraman
No abstract provided.
Learning From One Example In Machine Vision By Sharing Probability Densities, Erik Learned-Miller
Learning From One Example In Machine Vision By Sharing Probability Densities, Erik Learned-Miller
Erik G Learned-Miller
Human beings exhibit rapid learning when presented with a small number of images of a new object. A person can identify an object under a wide variety of visual conditions after having seen only a single example of that object. This ability can be partly explained by the application of previously learned statistical knowledge to a new setting. This thesis presents an approach to acquiring knowledge in one setting and using it in another. Specifically, we develop probability densities over common image changes. Given a single image of a new object and a model of change learned from a different …
Nullspace Composition Of Control Laws For Grasping, Robert Platt, Andrew Fagg, Roderic Grupen
Nullspace Composition Of Control Laws For Grasping, Robert Platt, Andrew Fagg, Roderic Grupen
Roderic Grupen
Much of the tradition in robot grasping is rooted in geometrical, planning-based approaches in which it assumed that object and finger geometries are well modeled a priori. Some recent approaches have chosen instead to deal with objects of unknown geometry. These techniques treat grasping as an active sensory-driven problem. At any given time, finger contacts are incrementally displaced along the objects local surface using a single control law. In this paper, we extend this approach by allowing multiple control laws to be active simultaneously. Three control laws are combined by projecting the actions of subordinate control laws into other control …
Transform-Invariant Image Decomposition With Similarity Templates, Chris Stauffer, Erik Learned-Miller, Kinh Tieu
Transform-Invariant Image Decomposition With Similarity Templates, Chris Stauffer, Erik Learned-Miller, Kinh Tieu
Erik G Learned-Miller
Recent work has shown impressive transform-invariant modeling and clustering for sets of images of objects with similar appearance. We seek to expand these capabilities to sets of images of an object class that show considerable variation across individual instances (e.g. pedestrian images) using a representation based on pixel-wise similarities, similarity templates. Because of its invariance to the colors of particular components of an object, this representation enables detection of instances of an object class and enables alignment of those instances. Further, this model implicitly represents the regions of color regularity in the class-specific image set enabling a decomposition of that …
Automatic Segmentation And Indexing Of Specialized Databases, Madirakshi Das, R. Manmatha
Automatic Segmentation And Indexing Of Specialized Databases, Madirakshi Das, R. Manmatha
R. Manmatha
The aim of this work is to index images based on color, in domain specific databases using colors computed from the object of interest only, instead of using the whole image. The main problem in this task is the segmentation of the region of interest from the background. Viewing segmentation as a figure/ground segregation problem leads to a new approach--successful elimination of the background leaves the figure or object of interest. The background elements are eliminated using general observations true for any photograph where there is a single, prominent object of interest. First, we form a hypothesis about possible background …
Modeling Score Distributions For Meta Search, R. Manmatha, T. Rath, F. Feng
Modeling Score Distributions For Meta Search, R. Manmatha, T. Rath, F. Feng
R. Manmatha
In this paper the score distributions of a number of text search engines are modeled. It is shown empirically that the score distributions on a per query basis may be modeled using an exponential distribution for the set of non-relevant documents and a normal distribution for the set of relevant documents. Experiments show that this model fits TREC-3 and TREC-4 data for a wide variety of different search engines including INQUERY a probabilistic search engine, SMART a vector space engine, and search engines based on latent semantic indexing and language modeling. The model also works when search engines index other …