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Fast Keypoint Recognition in Ten Lines of Code

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Alternative search techniques for face detection using location estimation and binary features

Venkatesh Bala Subburaman

The sliding window approach is the most widely used technique to detect objects 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 the cascade), the sc ...

ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE2012

Scale Invariant Features and Polar Descriptors in Omnidirectional Imaging

Pascal Frossard, Zafer Arican

We propose a method to compute scale invariant features in omnidirectional images. We present a formulation based on Riemannian geometry for the definition of differential operators on non-Euclidian manifolds that describe the mirror and lens structure in ...

Institute of Electrical and Electronics Engineers2012

A Real-Time Deformable Detector

Pascal Fua, François Fleuret, Karim Ali, David Hasler

We propose a new learning strategy for object detection. 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 base ...

Institute of Electrical and Electronics Engineers2012

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

Removing Shadows from Images Using Color and Near-infrared

Sabine Süsstrunk, Neda Salamati, Arthur Germain

Shadows often introduce errors in the performance of computer vision algorithms, such as object detection and tracking. This paper proposes a method to remove shadows from real images based on a probability shadow map. The probability shadow map identifies ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

Fast human detection from joint appearance and foreground feature subset covariances

Jean-Marc Odobez

We present a fast method to detect humans from stationary surveillance videos. It is based on a cascade of LogitBoost classifiers which use covariance matrices as object descriptors. We have made several contributions. First, our method learns the correlat ...

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

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

Analysis of Multiview Omnidirectional Images in a Spherical Framework

Zafer Arican

With the increasing demand of information for more immersive applications such as Google Street view or 3D movies, the efficient analysis of visual data from cameras has gained more importance. This visual information permits to extract some crucial inform ...

EPFL2010