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Matthew Cooper - computer vision


overview -> computer vision research


My graduate research focused on algorithms and analysis for computer vision using pattern theory and information theory and made two main contributions:

extending pattern theoretic object templates to accommodate surface appearance variability:

Object templates are derived from geometric CAD models of ground-based vehicles. The variability in the pose of objects in a three-dimensional scene is represented by the rigid transformations of rotation and translation. We formulate pose recognition as the estimation of the group action on the template that best accounts for the observed imagery. Standard statistical criteria for this estimation such as minimum-mean-squared-error (MMSE) or maximum-a-posteriori probability (MAP) are used.

We further equip these rigid templates with a random field defined on the three-dimensional object surface. This scalar field was used initially to account for variations in the radiant intensity of the object surface in forward-looking infrared radar (FLIR) imagery [Cooper et al, 1997]. Different regions on a vehicle's surface have different temperatures according to the vehicle's operational characteristics. This produces variation in object appearance. While the object surface is represented by a high-dimensional set of vertices, a Karhunen-Loève expansion of the random field provides a low-dimensional set of basis functions that accurately account for the dominant variations in appearance.

This approach has also been used to address diffuse illumination variability in computer vision [Cooper, et al., 2001]. Ongoing efforts to commercialize this technology are being conducted by Animetrics, Inc.

information-theoretic analysis of computer vision:

Formulating recognition via statistical inference allows for principled performance analysis of various recognition problems. We use mutual information to quantitatively determine:

  1. the amount of information a specific senor (e.g. camera) supplies about unknown object configuration parameters
  2. the information gain associated with the combination of mutliple sensors.

Such analysis is invaluable in the design of recognition systems and allows for a systematic cost/benefit assessment of sensor deployment options. Finally, the use of entropy measures and Fano's inequality also provides bounds on recognition error. We also provided asymptotic analysis of these measures. These results are summarized in [Cooper and Miller, 2000].