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Active Reconstruction

Visual Reconstruction (1987)
By Andrew Blake and Andrew Zisserman

Visual Reconstruction presents a unified and highly original approach to the treatment of continuity in vision. Two concepts, which were new at the time of publication, are introduced, analyzed, and illustrated.

Visual Reconstruction MIT Press

Active Vision

Active Vision (1992)
Edited by Andrew Blake and Alan Yuille

Active Vision explores important themes emerging from the active vision paradigm at the time of publication, which had only recently become an established area of machine vision. In four parts the contributions look in turn at tracking, control of vision heads, geometric and task planning, and architectures and applications, presenting research that marked a turning point for both the tasks and the processes of computer vision.

Active Vision MIT Press

Active Contours

Active Contours (1998)
By Andrew Blake and Michael Isard

Active Contours is about the computer analysis of moving video images. It develops geometric and probabilistic models for shapes and their dynamics. The models are applied to the real-time analysis of shapes in motion, and addresses issues of learning, temporal filtering and the problems of visual clutter. Numerous applications are illustrated from computer graphics animation, user-interface design, medical imaging, automated surveillance and robotics.

Active Contours Springer-Verlag

Markov Random Fields for Vision and Image Processing (2011)
Edited by Andrew Blake, Pushmeet Kohli and Carsten Rother

Markov Random Fields for Vision and Image Processing demonstrates the power of the Markov random field (MRF) in vision, treating the MRF both as a tool for modeling image data and, utilizing recently developed algorithms, as a means of making inferences about images. These inferences concern underlying image and scene structure as well as solutions to such problems as image reconstruction, image segmentation, 3D vision, and object labeling. It offers key findings and state-of-the-art research on both algorithms and applications.

Markov Random Fields for Vision and Image Processing MIT Press