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Thomas O. Binford

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10 papers
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10

UAI Conference 1989 Conference Paper

Model-Based Influence Diagrams for Machine Vision

  • Tod S. Levitt
  • John Mark Agosta
  • Thomas O. Binford

We show an approach to automated control of machine vision systems based on incremental creation and evaluation of a particular family of influence diagrams that represent hypotheses of imagery interpretation and possible subsequent processing decisions. In our approach, model-based machine vision techniques are integrated with hierarchical Bayesian inference to provide a framework for representing and matching instances of objects and relationships in imagery and for accruing probabilities to rank order conflicting scene interpretations. We extend a result of Tatman and Shachter to show that the sequence of processing decisions derived from evaluating the diagrams at each stage is the same as the sequence that would have been derived by evaluating the final influence diagram that contains all random variables created during the run of the vision system.

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ICRA Conference 1988 Conference Paper

Generic models for robot navigation

  • David J. Kriegman
  • Thomas O. Binford

The authors introduce the concept of an explicit generic building model. This abstract model is valid for a broad class of buildings. It uses mechanisms that have been used to build generic models for other object classes, including machine screws and generalized cylinders. These methods extend the generality of object classes which can be implemented, compared to previous implementations of object classes. Using a combination of epipolar stereo vision from the mobile robot along with monocular vision, the generic model is instantiated. This instantiation can be used for robot navigation. >

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ICRA Conference 1988 Conference Paper

Using a cylindrical tactile sensor for determining curvature

  • Ronald S. Fearing
  • Thomas O. Binford

A description is given of a device for determining curvatures using a cylindrical tactile sensor. The finger touches an unknown convex surface. Principal curvatures, normal force and location are determined from a 4*4 window of strain measurements. Sensor strains are predicted by convolving the spatial impulse response of the rubber skin with the assumed surface pressure distribution derived from a Hertz contact model. Gradient search finds the parameters of the convex second-order shape and the force that best fit the sensor data. Experiments show radius estimation within 10%, orientation within 2. 5 degrees, and sub-tactel localization of 3% of the element spacing. Accuracy limits due to sensor noise are derived. >

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ICRA Conference 1987 Conference Paper

A mobile robot: Sensing, planning and locomotion

  • David J. Kriegman
  • Ernst E. Triendl
  • Thomas O. Binford

A mobile robot architecture must include sensing, planning, and locomotion which are tied together by a model or map of the world based on sensor information, apriori knowledge and generic models. The architecture of a Stanford's autonomous mobile robot is described including its distributed computing system, locomotion, and sensing. Additionally, some of the issues in the representation of a world model are explored. Sensor models are used to update the world model in a uniform manner, and uncertainty reduction is discussed.

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ICRA Conference 1987 Conference Paper

Operational space motion specification in AL robot language

  • Chunsheng Cai
  • Thomas O. Binford

A new version of the AL language system has been implemented, with emphasis on the specifications for an object's motion in operational space under trajectory planning, and flexible grasping. This paper discusses specifications of object geometry, Cartesian trajectory, control frame calculation from the trajectory and geometry, and motion constraints with respect to the control frame. A simple inverse of a Jacobian matrix linking the control frame to joint space variables is also presented.

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AAAI Conference 1983 Conference Paper

Learning Physical Descriptions From Functional Definitions, Examples, and Precedents

  • Patrick H. Winston
  • Thomas O. Binford

It is too hard to tell vision systems what things look like. It is easier to talk about purpose and what things are for. Consequently, we want vision systems to use functional descriptions to identify things, when necessary, and we want them to learn physical descriptions for themselves, when possible, This paper describes a theory that explains how to make such systems work. The theory is a synthesis of two sets of ideas: ideas about learning from precedents and exercises developed at MIT and ideas about physical description developed at Stanford. The strength of the synthesis is illustrated by way of representative experiments. All of these experiments have been performed with an implemented system.

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