• Publications
  • Influence
Rapid object detection using a boosted cascade of simple features
TLDR
A machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates and the introduction of a new image representation called the "integral image" which allows the features used by the detector to be computed very quickly. Expand
Robust real-time face detection
TLDR
A frontal face detection system which achieves detection and false positive rates which are equivalent to the best published results and a method for combining successively more complex classifiers in a cascade structure which dramatically increases the speed of the detector by focusing attention on promising regions of the image. Expand
Robust Real-Time Face Detection
TLDR
A new image representation called the “Integral Image” is introduced which allows the features used by the detector to be computed very quickly and a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions. Expand
Robust Real-time Object Detection
TLDR
A visual object detection framework that is capable of processing images extremely rapidly while achieving high detection rates is described, with the introduction of a new image representation called the “Integral Image” which allows the features used by the detector to be computed very quickly. Expand
Alignment by Maximization of Mutual Information
  • Paul A. Viola, W. Wells
  • Mathematics, Computer Science
  • International Journal of Computer Vision
  • 1 September 1997
TLDR
A new information-theoretic approach is presented for finding the pose of an object in an image that works well in domains where edge or gradient-magnitude based methods have difficulty, yet it is more robust than traditional correlation. Expand
Detecting pedestrians using patterns of motion and appearance
TLDR
This paper describes a pedestrian detection system that integrates image intensity information with motion information, and is the first to combine both sources of information in a single detector. Expand
Multiple Instance Boosting for Object Detection
TLDR
MILBoost adapts the feature selection criterion of MILBoost to optimize the performance of the Viola-Jones cascade to show the advantage of simultaneously learning the locations and scales of the objects in the training set along with the parameters of the classifier. Expand
Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade
TLDR
A new variant of AdaBoost is proposed as a mechanism for training the simple classifiers used in the cascade in domains where the distribution of positive and negative examples is highly skewed (e.g. face detection or database retrieval). Expand
Fast Multi-view Face Detection
TLDR
A multi-view detector presented in this pa-per is a combination of Viola-Jones detectors, each detectortrained on face data taken from a single viewpoint, which appears that a monolithic approach to face detection is unlearnable with existing classifier trained on all poses. Expand
Fast pose estimation with parameter-sensitive hashing
TLDR
A new algorithm is introduced that learns a set of hashing functions that efficiently index examples relevant to a particular estimation task, and can rapidly and accurately estimate the articulated pose of human figures from a large database of example images. Expand
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