Rapid object detection using a boosted cascade of simple features
@article{Viola2001RapidOD, title={Rapid object detection using a boosted cascade of simple features}, author={Paul A. Viola and Michael J. Jones}, journal={Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001}, year={2001}, volume={1}, pages={I-I} }
This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. [] Key Method The first is the introduction of a new image representation called the "integral image" which allows the features used by our detector to be computed very quickly.
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References
SHOWING 1-10 OF 25 REFERENCES
A general framework for object detection
- Computer ScienceSixth International Conference on Computer Vision (IEEE Cat. No.98CH36271)
- 1998
A general trainable framework for object detection in static images of cluttered scenes based on a wavelet representation of an object class derived from a statistical analysis of the class instances and a motion-based extension to enhance the performance of the detection algorithm over video sequences is presented.
Training support vector machines: an application to face detection
- Computer ScienceProceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition
- 1997
A decomposition algorithm that guarantees global optimality, and can be used to train SVM's over very large data sets is presented, and the feasibility of the approach on a face detection problem that involves a data set of 50,000 data points is demonstrated.
A statistical method for 3D object detection applied to faces and cars
- Computer ScienceProceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662)
- 2000
Using this method, this work has developed the first algorithm that can reliably detect human faces with out-of-plane rotation and the first algorithms thatCan reliably detect passenger cars over a wide range of viewpoints.
Example-Based Learning for View-Based Human Face Detection
- Computer ScienceIEEE Trans. Pattern Anal. Mach. Intell.
- 1998
An example-based learning approach for locating vertical frontal views of human faces in complex scenes and shows empirically that the distance metric adopted for computing difference feature vectors, and the "nonface" clusters included in the distribution-based model, are both critical for the success of the system.
Statistical Pattern Recognition
- Computer ScienceTechnometrics
- 2003
This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates…
Neural Network-Based Face Detection
- Computer ScienceIEEE Trans. Pattern Anal. Mach. Intell.
- 1998
A neural network-based face detection system that arbitrates between multiple networks to improve performance over a single network using a bootstrap algorithm, which eliminates the difficult task of manually selecting non-face training examples.
Joint Induction of Shape Features and Tree Classifiers
- Computer ScienceIEEE Trans. Pattern Anal. Mach. Intell.
- 1997
A very large family of binary features for two-dimensional shapes determined by inductive learning during the construction of classification trees is introduced, which makes it possible to narrow the search for informative ones at each node of the tree.
Summed-area tables for texture mapping
- Environmental ScienceSIGGRAPH '84
- 1984
Texture-map computations can be made tractable through use of precalculated tables which allow computational costs independent of the texture density, and the cost and performance of the new technique is compared to previous techniques.