Object localisation using the Generative Template of Features

Authors:
Moray Allan;Christopher K. I. Williams
Affiliations:
School of Informatics, University of Edinburgh, 5 Forrest Hill, Edinburgh EH1 2QL, UK;School of Informatics, University of Edinburgh, 5 Forrest Hill, Edinburgh EH1 2QL, UK
Venue:
Computer Vision and Image Understanding
Year:
2009

Citing 21
Cited 1

Object recognition by computer: the role of geometric constraints

Object recognition by computer: the role of geometric constraints
Probabilistic approach to the Hough transform

Image and Vision Computing
Handwritten digit recognition with a back-propagation network

Advances in neural information processing systems 2
Using Generative Models for Handwritten Digit Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision: A Modern Approach

Computer Vision: A Modern Approach
A Probabilistic Approach to Object Recognition Using Local Photometry and Global Geometry

ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
An Affine Invariant Interest Point Detector

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Automated Scene Matching in Movies

CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
Object Recognition from Local Scale-Invariant Features

ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Robust Real-Time Face Detection

International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
Greedy learning of multiple objects in images using robust statistics and factorial learning

Neural Computation
Learning to Detect Objects in Images via a Sparse, Part-Based Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatial Priors for Part-Based Recognition Using Statistical Models

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Sparse Object Category Model for Efficient Learning and Exhaustive Recognition

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Learning Hierarchical Models of Scenes, Objects, and Parts

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Integrating Representative and Discriminative Models for Object Category Detection

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Learning Object Categories from Google"s Image Search

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Multi-Aspect Detection of Articulated Objects

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Sampling strategies for bag-of-features image classification

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Located hidden random fields: learning discriminative parts for object detection

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III

Demisting the Hough Transform for 3D Shape Recognition and Registration

International Journal of Computer Vision

Quantified Score

Hi-index	0.00

Visualization

Abstract

We introduce the Generative Template of Features (GTF), a parts-based model for visual object category detection. The GTF consists of a number of parts, and for each part there is a corresponding spatial location distribution and a distribution over 'visual words' (clusters of invariant features). The performance of the GTF is evaluated for object localisation, and it is shown that such a relatively simple model can give state-of-the-art performance. We also demonstrate how a Hough-transform-like method for object localisation can be derived from the GTF model.