Representation of local geometry in the visual system
Biological Cybernetics
International Journal of Computer Vision
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Recognition without Correspondence using MultidimensionalReceptive Field Histograms
International Journal of Computer Vision
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
International Journal of Computer Vision
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Classifying Images of Materials: Achieving Viewpoint and Illumination Independence
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Selection of Scale-Invariant Parts for Object Class Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Integral Histogram: A Fast Way To Extract Histograms in Cartesian Spaces
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Integrating Representative and Discriminative Models for Object Category Detection
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Local Features for Object Class Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Fast Human Detection Using a Cascade of Histograms of Oriented Gradients
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
International Journal of Computer Vision
Learning object detection from a small number of examples: the importance of good features
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Techniques for still image scene classification and object detection
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Histogram features-based fisher linear discriminant for face detection
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Spatial Configuration of Local Shape Features for Discriminative Object Detection
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Boosted forest for human detection
Proceedings of the First International Conference on Internet Multimedia Computing and Service
Boosting part-sense multi-feature learners toward effective object detection
Computer Vision and Image Understanding
A human detection system for proxemics interaction
Proceedings of the 6th international conference on Human-robot interaction
Generic object class detection using feature maps
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Opponent colors for human detection
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Image-Based grasping point detection using boosted histograms of oriented gradients
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
Fast Human Pose Detection Using Randomized Hierarchical Cascades of Rejectors
International Journal of Computer Vision
Boosted translation-tolerable classifiers for fast object detection
Image and Vision Computing
Proceedings of the 20th ACM international conference on Multimedia
Journal of Visual Communication and Image Representation
Improving HOG with image segmentation: application to human detection
ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
Automated textual descriptions for a wide range of video events with 48 human actions
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Entropic selection of histogram features for efficient classification
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Object class detection: A survey
ACM Computing Surveys (CSUR)
Macrofeature layout selection for pedestrian localization and its acceleration using GPU
Computer Vision and Image Understanding
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We address the problem of visual object class recognition and localization in natural images. Building upon recent progress in the field we show how histogram-based image descriptors can be combined with a boosting classifier to provide a state of the art object detector. Among the improvements we introduce a weak learner for multi-valued histogram features and show how to overcome problems of limited training sets. We also analyze different choices of image features and address computational aspects of the method. Validation of the method on recent benchmarks for object recognition shows its superior performance. In particular, using a single set of parameters our approach outperforms all the methods reported in VOC05 Challenge for seven out of eight detection tasks and four object classes while providing close to real-time performance.