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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.

Active Object Recognition Conditioned by Probabalistic Evidence and Entropy Maps

Arbel, Tal January 1999 (has links)
No description available.

Pose estimation using the EM algorithm

Moss, Simon January 2002 (has links)
No description available.

Colour object search

Walcott, P. A. January 1998 (has links)
No description available.

Finite elements for image analysis

Karaolani, Persephoni January 1994 (has links)
No description available.

Building and updating a library of three-dimensional objects

Jaitly, Rahul January 1997 (has links)
No description available.

The contribution of meaning in forming holistic and segmented based visual representations

Smith, Wendy January 2000 (has links)
No description available.

Acquisition of range data using blurred images

Whitehouse, J. C. January 1986 (has links)
No description available.

Part-whole interaction in the recognition of meaningful parts in generic objects

McSherry, Dominic January 1998 (has links)
No description available.

Recognition and Localization of Overlapping Parts from Sparse Data

Grimson, W. Eric L., Lozano-Perez, Tomas 01 June 1985 (has links)
This paper discusses how sparse local measurements of positions and surface normals may be used to identify and locate overlapping objects. The objects are modeled as polyhedra (or polygons) having up to six degreed of positional freedom relative to the sensors. The approach operated by examining all hypotheses about pairings between sensed data and object surfaces and efficiently discarding inconsistent ones by using local constraints on: distances between faces, angles between face normals, and angles (relative to the surface normals) of vectors between sensed points. The method described here is an extension of a method for recognition and localization of non-overlapping parts previously described in [Grimson and Lozano-Perez 84] and [Gaston and Lozano-Perez 84].

Recognizing 3D Ojbects of 2D Images: An Error Analysis

Grimson, W. Eric, Huttenlocher, Daniel P., Alter, T. D. 01 July 1992 (has links)
Many object recognition systems use a small number of pairings of data and model features to compute the 3D transformation from a model coordinate frame into the sensor coordinate system. With perfect image data, these systems work well. With uncertain image data, however, their performance is less clear. We examine the effects of 2D sensor uncertainty on the computation of 3D model transformations. We use this analysis to bound the uncertainty in the transformation parameters, and the uncertainty associated with transforming other model features into the image. We also examine the impact of the such transformation uncertainty on recognition methods.

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