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Cognitive-Developmental Learning for a Humanoid Robot: A Caregiver's Gift
(2004-09-26)
The goal of this work is to build a cognitive system for the humanoid robot, Cog, that exploits human caregivers as catalysts to perceive and learn about actions, objects, scenes, people, and the robot itself. This thesis ...
Error weighted classifier combination for multi-modal human identification
(2005-12-14)
In this paper we describe a technique of classifier combination used in a human identification system. The system integrates all available features from multi-modal sources within a Bayesian framework. The framework allows ...
Surface Reflectance Estimation and Natural Illumination Statistics
(2001-09-01)
Humans recognize optical reflectance properties of surfaces such as metal, plastic, or paper from a single image without knowledge of illumination. We develop a machine vision system to perform similar recognition tasks ...
Gait Dynamics for Recognition and Classification
(2001-09-01)
This paper describes a representation of the dynamics of human walking action for the purpose of person identification and classification by gait appearance. Our gait representation is based on simple features such as ...
Range Segmentation Using Visibility Constraints
(2001-09-01)
Visibility constraints can aid the segmentation of foreground objects observed with multiple range images. In our approach, points are defined as foreground if they can be determined to occlude some {em empty space} in the ...
Type-omega DPLs
(2001-10-16)
Type-omega DPLs (Denotational Proof Languages) are languages for proof presentation and search that offer strong soundness guarantees. LCF-type systems such as HOL offer similar guarantees, but their soundness relies heavily ...
Type-alpha DPLs
(2001-10-05)
This paper introduces Denotational Proof Languages (DPLs). DPLs are languages for presenting, discovering, and checking formal proofs. In particular, in this paper we discus type-alpha DPLs---a simple class of DPLs for ...
How do Humans Determine Reflectance Properties under Unknown Illumination?
(2001-10-21)
Under normal viewing conditions, humans find it easy to distinguish between objects made out of different materials such as plastic, metal, or paper. Untextured materials such as these have different surface reflectance ...
Learning Object-Independent Modes of Variation with Feature Flow Fields
(2001-09-01)
We present a unifying framework in which "object-independent" modes of variation are learned from continuous-time data such as video sequences. These modes of variation can be used as "generators" to produce a manifold of ...
Recognition of Surface Reflectance Properties from a Single Image under Unknown Real-World Illumination
(2001-10-21)
This paper describes a machine vision system that classifies reflectance properties of surfaces such as metal, plastic, or paper, under unknown real-world illumination. We demonstrate performance of our algorithm for ...