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Rapid object detection using a boosted cascade of simple features
- Paul A. Viola, Michael J. Jones
- Computer ScienceProceedings of the IEEE Computer Society…
- 8 December 2001
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.
Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade
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).
Fast Multi-view Face Detection
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.
Statistical Color Models with Application to Skin Detection
- Michael J. Jones, James M. Rehg
- Computer ScienceProceedings. IEEE Computer Society Conference on…
- 23 June 1999
This work describes the construction of color models for skin and non-skin classes from a dataset of nearly 1 billion labelled pixels and suggests that color can be a more powerful cue for detecting people in unconstrained imagery than was previously suspected.
Detecting Pedestrians Using Patterns of Motion and Appearance
- Paul A. Viola, Michael J. Jones, D. Snow
- Computer ScienceProceedings Ninth IEEE International Conference…
- 13 October 2003
This pedestrian detection system is the first to combine both sources of information in a single detector, and operates on low resolution images under difficult conditions (such as rain and snow).
Regularization Theory and Neural Networks Architectures
This paper shows that regularization networks encompass a much broader range of approximation schemes, including many of the popular general additive models and some of the neural networks, and introduces new classes of smoothness functionals that lead to different classes of basis functions.
A Multi-stream Bi-directional Recurrent Neural Network for Fine-Grained Action Detection
- Bharat Singh, Tim K. Marks, Michael J. Jones, Oncel Tuzel, Ming Shao
- Computer ScienceIEEE Conference on Computer Vision and Pattern…
- 27 June 2016
This paper presents a multi-stream bi-directional recurrent neural network for fine-grained action detection that significantly outperforms state-of-the-art action detection methods on both datasets.
Fully automatic pose-invariant face recognition via 3D pose normalization
- Akshay Asthana, Tim K. Marks, Michael J. Jones, K. Tieu, M. Rohith
- Computer ScienceInternational Conference on Computer Vision
- 6 November 2011
This paper proposes a 3D pose normalization method that is completely automatic and leverages the accurate 2D facial feature points found by the system and outperforms other comparable methods convincingly.
Multidimensional morphable models
- Michael J. Jones, T. Poggio
- Computer ScienceSixth International Conference on Computer Vision…
- 4 January 1998
An effective stochastic gradient descent algorithm is introduced that automaticaIly matches a model to a novel image by finding the parameters that minimize the error between the image generated by the model and the novel image.
Face Recognition Using Boosted Local Features
A new method for face recognition which learns a face similarity measure from example image pairs using a set of computationally efficient “rectangle” features which act on pairs of input images.