We formulate a probabilistic model of image generation and derive optimal inference
algorithms for finding objects and object features within this framework. The approach …

This thesis presents a learning based approach for detecting classes of objects and patterns
with variable image appearance but highly predictable image boundaries. It consists of two …

The tasks of visual object recognition and classification are natural and effortless for
biological visual systems, but exceedingly difficult to replicate in computer vision systems …

A vital aspect of human intelligence is the ability to compose increasingly complex concepts
out of simpler ideas, enabling both rapid learning and adaptation of knowledge. In this paper …

Patch-based appearance models are used in a wide range of computer vision ap-plications.
To learn such models it has previously been necessary to specify a suitable set of patch …

We have been developing Name-It, a system that associates faces and names in news
videos. First, as the only knowledge source, the system is given news videos which include …

Computer Vision: Principles, Algorithms, Applications, Learning (previously entitled
Computer and Machine Vision) clearly and systematically presents the basic methodology of …

We consider data that are images containing views of multiple objects. Our task is to learn
about each of the objects present in the images. This task can be approached as a factorial …

This paper presents a framework called Cresceptron for view-based learning, recognition
and segmentation. Specifically, it recognizes and segments image patterns that are similar …

We study the problem of detecting objects in still, gray-scale images. Our primary focus is the
development of a learning-based approach to the problem that makes use of a sparse, part …