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A Note on Object Class Representation and Categorical Perception
(1999-12-17)
We present a novel scheme ("Categorical Basis Functions", CBF) for object class representation in the brain and contrast it to the "Chorus of Prototypes" scheme recently proposed by Edelman. The power and flexibility ...
Learning-Based Approach to Real Time Tracking and Analysis of Faces
(1999-09-23)
This paper describes a trainable system capable of tracking faces and facialsfeatures like eyes and nostrils and estimating basic mouth features such as sdegrees of openness and smile in real time. In developing this ...
Pre-Attentive Segmentation in the Primary Visual Cortex
(1998-06-30)
Stimuli outside classical receptive fields have been shown to exert significant influence over the activities of neurons in primary visual cortexWe propose that contextual influences are used for pre-attentive visual ...
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. ...
Cooperative Physics of Fly Swarms: An Emergent Behavior
(1995-04-11)
We have simulated the behavior of several artificial flies, interacting visually with each other. Each fly is described by a simple tracking system (Poggio and Reichardt, 1973; Land and Collett, 1974) which summarizes ...
A Trainable Object Detection System: Car Detection in Static Images
(1999-10-13)
This paper describes a general, trainable architecture for object detection that has previously been applied to face and peoplesdetection with a new application to car detection in static images. Our technique is a ...
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 ...
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. ...
Amorphous Computing
(1999-08-29)
Amorphous computing is the development of organizational principles and programming languages for obtaining coherent behaviors from the cooperation of myriads of unreliable parts that are interconnected in unknown, ...
The Delta Tree: An Object-Centered Approach to Image-Based Rendering
(1997-05-02)
This paper introduces the delta tree, a data structure that represents an object using a set of reference images. It also describes an algorithm for generating arbitrary re-projections of an object by traversing its ...