James L. Crowley

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This paper addresses the problem of estimating face orientation from automatic detection of salient facial structures using learned robust features. Face imagettes are detected using color and described using a weighted sum of locally normalized Gaussian receptive fields. Robust face features are learned by clustering the Gaussian derivative responses(More)
The appearance of an object is composed of local structure. This local structure can be described and characterized by a vector of local features measured by local operators such as Gaussian derivatives or Gabor filters. This article presents a technique where appearances of objects are represented by the joint statistics of such local neighborhood(More)
This paper presents a technique to determine the identity of objects in a scene using histograms of the responses of a vector of local linear neighborhood operators (receptive elds). This technique can be used to determine the most probable objects in a scene, independent of the object's position, image-plane orientation and scale. In this paper we describe(More)
This paper describes a system for dynamically maintaining a description of the limits to free space for a mobile robot using a belt of ultrasonic range devices. These techniques are based on the principle of explicitly representing the uncertainty of the vehicle position as well as the uncertainty inherent in the sensing process. A model is presented for(More)
This paper defines a multiple resolution representation for the two-dimensional gray-scale shapes in an image. This representation is constructed by detecting peaks and ridges in the difference of lowpass (DOLP) transform. Descriptions of shapes which are encoded in this representation may be matched efficiently despite changes in size, orientation, or(More)
This paper addresses the problem of estimating head pose over a wide range of angles from low-resolution images. Faces are detected using chrominance-based features. Grey-level normalized face imagettes serve as input for linear auto-associative memory. One memory is computed for each pose using a Widrow-Hoff learning rule. Head pose is classified with a(More)