Kim L. Boyer
Rensselaer Polytechnic Institute
127 Papers
1.3K Citations
Kim L. Boyer is an academic researcher from Rensselaer Polytechnic Institute. The author has contributed to research in topics: Image registration & Image segmentation. The author has an hindex of 28, co-authored 126 publications. Previous affiliations of Kim L. Boyer include Ohio State University & Bell Labs.
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Papers
Precision range image registration using a robust surface interpenetration measure and enhanced genetic algorithms
TL;DR: A new, hybrid genetic algorithm (GA) technique, including hill climbing and parallel-migration, combined with a new, robust evaluation metric based on surface interpenetration is presented, which offers much faster convergence than prior GA methods and ensures more precise alignments, even in the presence of significant noise, than mean squared error or other well-known robust cost functions.
263
Integration, inference, and management of spatial information using Bayesian networks: perceptual organization
Sudeep Sarkar,Kim L. Boyer +1 more
TL;DR: The formalism of Bayesian networks provides a very elegant solution, in a probabilistic framework, to the problem of integrating top-down and bottom-up visual processes, as well as serving as a knowledge base, to create a composite organization hypothesis.
151
A system to detect houses and residential street networks in multispectral satellite images
Cem ínsalan,Kim L. Boyer +1 more
TL;DR: A novel system to detect houses and street networks in IKONOS multispectral images with one meter panchromatic resolution with 4 m resolution in the spectral bands is introduced, indicating the usefulness of the system in detecting houses andStreet networks, hence generating automated maps.
116
Perceptual Organization for Artificial Vision Systems
Kim L. Boyer,Sudeep Sarkar +1 more
- 01 Mar 2000
TL;DR: In this paper, Boyer et al. proposed a Gestalt Model of Spatial Perception (GSPP) for image segmentation, which is based on the curve indicator random field (CRF).
79
•Book
Computer Perceptual Organization in Computer Vision
Sudeep Sarkar,Kim L. Boyer +1 more
- 01 Jul 1994
TL;DR: This book describes the design of a complete, flexible system for perceptual organization in computer vision using graph theoretic techniques, voting methods, and an extension of the Bayesian networks called perceptual inference networks (PINs).
77