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Three-Dimensional Correspondence
(1998-12-01)
This paper describes the problem of three-dimensional object correspondence and presents an algorithm for matching two three-dimensional colored surfaces using polygon reduction and the minimization of an energy function. ...
A Security Kernel Based on the Lambda-Calculus
(1996-03-13)
Cooperation between independent agents depends upon establishing adegree of security. Each of the cooperating agents needs assurance that the cooperation will not endanger resources of value to that agent. In a computer ...
Direct Object Recognition Using No Higher Than Second or Third Order Statistics of the Image
(1995-12-01)
Novel algorithms for object recognition are described that directly recover the transformations relating the image to its model. Unlike methods fitting the typical conventional framework, these new methods do not ...
Edge and Mean Based Image Compression
(1996-11-01)
In this paper, we present a static image compression algorithm for very low bit rate applications. The algorithm reduces spatial redundancy present in images by extracting and encoding edge and mean information. Since ...
Recognizing 3D Object Using Photometric Invariant
(1995-04-22)
In this paper we describe a new efficient algorithm for recognizing 3D objects by combining photometric and geometric invariants. Some photometric properties are derived, that are invariant to the changes of illumination ...
Probabilistic Independence Networks for Hidden Markov Probability Models
(1996-03-13)
Graphical techniques for modeling the dependencies of randomvariables have been explored in a variety of different areas includingstatistics, statistical physics, artificial intelligence, speech recognition, image ...
Factorial Hidden Markov Models
(1996-02-09)
We present a framework for learning in hidden Markov models with distributed state representations. Within this framework, we derive a learning algorithm based on the Expectation--Maximization (EM) procedure for maximum ...
Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks
(1996-02-09)
Sigmoid type belief networks, a class of probabilistic neural networks, provide a natural framework for compactly representing probabilistic information in a variety of unsupervised and supervised learning problems. ...
Model-Based Matching by Linear Combinations of Prototypes
(1996-12-01)
We describe a method for modeling object classes (such as faces) using 2D example images and an algorithm for matching a model to a novel image. The object class models are "learned'' from example images that we call ...
Model-Based Matching of Line Drawings by Linear Combinations of Prototypes
(1996-01-18)
We describe a technique for finding pixelwise correspondences between two images by using models of objects of the same class to guide the search. The object models are 'learned' from example images (also called ...