A
Andrew Zisserman
Researcher at University of Oxford
Publications - 808
Citations - 312028
Andrew Zisserman is an academic researcher from University of Oxford. The author has contributed to research in topics: Convolutional neural network & Real image. The author has an hindex of 167, co-authored 808 publications receiving 261717 citations. Previous affiliations of Andrew Zisserman include University of Edinburgh & Microsoft.
Papers
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Book ChapterDOI
Single Axis Geometry by Fitting Conics
TL;DR: A new approach for recovering 3D geometry from an uncalibrated image sequence of a single axis (turntable) motion based on fitting a conic locus to corresponding image points over multiple views is described.
Proceedings ArticleDOI
Discriminative Sub-categorization
Minh Hoai,Andrew Zisserman +1 more
TL;DR: A new model for discriminative sub-categorization which determines cluster membership for positive samples whilst simultaneously learning a max-margin classifier to separate each cluster from the negative samples is introduced.
Book ChapterDOI
SpineNet: Automatically Pinpointing Classification Evidence in Spinal MRIs
TL;DR: A Convolutional Neural Network architecture and training scheme to predict multiple radiological scores on multiple slice sagittal MRIs and the prediction of a heat-map of evidence hotspots for each score is described.
Book ChapterDOI
Camera Calibration Using Multiple Images
TL;DR: This paper describes a method for camera calibration which takes a sequence of images of a calibration plane rotating around a fixed axis and shows no requirement for any exact positioning of the camera or calibration plane.
Posted Content
Learning to Navigate in Cities Without a Map
Piotr Mirowski,Matthew Koichi Grimes,Mateusz Malinowski,Karl Moritz Hermann,Keith Anderson,Denis Teplyashin,Karen Simonyan,Koray Kavukcuoglu,Andrew Zisserman,Raia Hadsell +9 more
TL;DR: In this paper, a dual pathway architecture is proposed to learn to navigate multiple cities and to traverse to target destinations that may be kilometres away by integrating general policies with locale-specific knowledge.