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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.
View on IEEE
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).
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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.
Statistical Color Models with Application to Skin Detection
TLDR
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.
View on IEEE
Detecting Pedestrians Using Patterns of Motion and Appearance
TLDR
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).
View on IEEE
Regularization Theory and Neural Networks Architectures
TLDR
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.
View on MIT Press
A Multi-stream Bi-directional Recurrent Neural Network for Fine-Grained Action Detection
TLDR
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.
View on IEEE
Fully automatic pose-invariant face recognition via 3D pose normalization
TLDR
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.
View on IEEE
Multidimensional morphable models
TLDR
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.
View on IEEE
Face Recognition Using Boosted Local Features
TLDR
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.
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