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Randomized Trees for Real-Time Keypoint Recognition

Publications associées (34)

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FREAK: Fast Retina Keypoint

Pierre Vandergheynst, Alexandre Massoud Alahi, Raphaël Ortiz

A large number of vision applications rely on matching keypoints across images. The last decade featured an arms-race towards faster and more robust keypoints and association algorithms: Scale Invariant Feature Transform (SIFT), Speed-up Robust Feature (SU ...

Ieee2012

Face Detection using Ferns

Sébastien Marcel, Venkatesh Bala Subburaman

This paper discusses the use of ferns (a set of binary features) for face detection. The binary feature used here is the sign of pixel intensity difference. Ferns were first introduced for keypoint recognition and showed good performance, and improving the ...

Idiap2011

Robust, High-Speed Interest Point Matching for Real-Time Applications

Michael Calonder

A typical Computer Vision system needs to process vast amounts of data as captured by one or more cameras, constantly testing the capabilities of today's hardware. Yet such systems face an ever-growing computational load caused by the more and more demandi ...

EPFL2010

Fast Keypoint Recognition using Random Ferns

Pascal Fua, Vincent Lepetit, Michael Calonder, Mustafa Özuysal

While feature point recognition is a key component of modern approaches to object detection, existing approaches require computationally expensive patch preprocessing to handle perspective distortion. In this paper, we show that formulating the problem in ...

2010

Scalable real-time planar targets tracking for digilog books

Vincent Lepetit

We propose a novel 3D tracking method that supports several hundreds of pre-trained potential planar targets without losing real-time performance. This goes well beyond the state-of-the-art, and to reach this level of performances, two threads run in paral ...

Springer Verlag2010

Joint Pose Estimator and Feature Learning for Object Detection

Pascal Fua, François Fleuret, Karim Ali, David Hasler

A new learning strategy for object detection is presented. The proposed scheme forgoes the need to train a collection of detectors dedicated to homogeneous families of poses, and instead learns a single classifier that has the inherent ability to deform ba ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2009

Improved Phone Posterior Estimation Through k-NN and MLP-Based Similarity

Benjamin Picart

In this work, we investigate the possible use of k-nearest neighbour (kNN) classifiers to perform frame-based acoustic phonetic classification, hence replacing Gaussian Mixture Models (GMM) or MultiLayer Perceptrons (MLP) used in standard Hidden Markov Mod ...

Idiap2009

Haar Local Binary Pattern Feature for Fast Illumination Invariant Face Detection

Sébastien Marcel, Anindya Roy

Face detection is the first step in many visual processing systems like face recognition, emotion recognition and lip reading. In this paper, we propose a novel feature called Haar Local Binary Pattern (HLBP) feature for fast and reliable face detection, p ...

2009

Haar Local Binary Pattern Feature for Fast Illumination Invariant Face Detection

Sébastien Marcel, Anindya Roy

Idiap2009

Keypoint Signatures for Fast Learning and Recognition

Pascal Fua, Vincent Lepetit, Michael Calonder

Statistical learning techniques have been used to dramatically speed-up keypoint matching by training a classifier to recognize a specific set of keypoints. However, the training itself is usually relatively slow and performed offline. Although methods hav ...

2008