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Keypoint Recognition using Random Forests and Random Ferns

Related publications (58)

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Tasting Families of Features for Image Classification

François Fleuret, Charles Dubout

Using multiple families of image features is a very efficient strategy to improve performance in object detection or recognition. However, such a strategy induces multiple challenges for machine learning methods, both from a computational and a statistical ...

2011

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

Learning Pose Invariant and Covariant Classifiers from Image Sequences

Mustafa Özuysal

Object tracking and detection over a wide range of viewpoints is a long-standing problem in Computer Vision. Despite significant advance in wide-baseline sparse interest point matching and development of robust dense feature models, it remains a largely op ...

EPFL2010

Fast Bounding Box Estimation based Face Detection

Sébastien Marcel, Venkatesh Bala Subburaman

The sliding window approach is the most widely used technique to detect an object from an image. In the past few years, classifiers have been improved in many ways to increase the scanning speed. Apart from the classifier design (such as cascade), the scan ...

2010

Fast Bounding Box Estimation based Face Detection

Sébastien Marcel, Venkatesh Bala Subburaman

Idiap2010

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

An Alternative Scanning Strategy to Detect Faces

Sébastien Marcel, Venkatesh Bala Subburaman

The sliding window approach is the most widely used technique to detect faces in an image. Usually a classifier is applied on a regular grid and to speed up the scanning, the grid spacing is increased, which increases the number of miss detections. In this ...

2010

On Improving Face Detection Performance by Modelling Contextual Information

Sébastien Marcel, Cosmin Atanasoaei

In this paper we present a new method to enhance object detection by removing false alarms and merging multiple detections in a principled way with few parameters. The method models the output of an object classiï¬er which we consider as the context. A hie ...

Idiap2010

BRIEF: Binary Robust Independent Elementary Features

Pascal Fua, Vincent Lepetit, Christoph Strecha, Michael Calonder

We propose to use binary strings as an efficient feature point descriptor, which we call BRIEF. We show that it is highly discriminative even when using relatively few bits and can be computed using simple intensity difference tests. Furthermore, the descr ...

Springer-Verlag New York, Ms Ingrid Cunningham, 175 Fifth Ave, New York, Ny 10010 Usa2010

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