We have constructed a frontal face detection system which achieves detection and false positive rates which are equivalent to the best published results [7, 5, 6, 4, 1]. This face detection system is most clearly distinguished from previous approaches in its ability to detect faces extremely

This paper describes a visual object detection framework that is capable of processing images extremely rapidly while achieving high detection rates. There are three key contributions. The first is the introduction of a new image representation called the “Integral Image ” which allows the features

-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the 1 norm. This suggests the possibility of a principled approach to robust principal component

Abstract. In this paper, we present a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Ro-bust Features). It approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can

the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and thus its utility in detecting the modes of the density. The equivalence of the mean shift procedure to the Nadaraya–Watson estimator from kernel regression and the robust M

-sirable properties: the set is closed under 1. continuous (and thus projective) transformation of image coordinates and 2. monotonic transformation of im-age intensities. An efficient (near linear complexity) and practically fast de-tection algorithm (near frame rate) is presented for an affinely-invariant stable

We study the question of feature sets for robust visual object recognition, adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of Histograms of Oriented Gradient (HOG) descriptors significantly

sequence have been identified and localized. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Given a single image, the goal of face detection is to identify all image regions which contain a face regardless

This paper is about models with agents whose doubts about model specification cause them to value decision rules that perform well across a set of models. Agents fear difficult-to-detect misspecifications of the state transition law, difficult to detect because they are partly masked by the random

A sonorant detection scheme using Mel-frequency cepstral coefficients and support vector machines (SVMs) is presented and tested in a variety of noise conditions. Adapting the classifier threshold using an estimate of the noise level is used to bias the classifier to effectively compensate