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Journal ArticleDOI

Robust Recognition Using Eigenimages

TLDR
In this paper, instead of computing the coefficients by a projection of the data onto the eigenimages, they extract them by a robust hypothesize-and-test paradigm using subsets of image points.
About
This article is published in Computer Vision and Image Understanding.The article was published on 2000-04-01. It has received 308 citations till now.

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Citations
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Journal ArticleDOI

Robust Face Recognition via Sparse Representation

TL;DR: This work considers the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise, and proposes a general classification algorithm for (image-based) object recognition based on a sparse representation computed by C1-minimization.
Book

Computer Vision: Algorithms and Applications

TL;DR: Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images and takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene.
Book ChapterDOI

Real-time compressive tracking

TL;DR: A simple yet effective and efficient tracking algorithm with an appearance model based on features extracted from the multi-scale image feature space with data-independent basis that performs favorably against state-of-the-art algorithms on challenging sequences in terms of efficiency, accuracy and robustness.
Proceedings ArticleDOI

On-line Boosting and Vision

TL;DR: This paper proposes a novel on-line AdaBoost feature selection method and demonstrates the multifariousness of the method on such diverse tasks as learning complex background models, visual tracking and object detection.
Journal ArticleDOI

Linear Regression for Face Recognition

TL;DR: A novel approach of face identification by formulating the pattern recognition problem in terms of linear regression, using a fundamental concept that patterns from a single-object class lie on a linear subspace, and introducing a novel Distance-based Evidence Fusion (DEF) algorithm.
References
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Journal ArticleDOI

A mathematical theory of communication

TL;DR: This final installment of the paper considers the case where the signals or the messages or both are continuously variable, in contrast with the discrete nature assumed until now.
Journal ArticleDOI

Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

TL;DR: New results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form that provide the basis for an automatic system that can solve the Location Determination Problem under difficult viewing.
Journal ArticleDOI

Eigenfaces for recognition

TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
Book

An Introduction to Multivariate Statistical Analysis

TL;DR: In this article, the distribution of the Mean Vector and the Covariance Matrix and the Generalized T2-Statistic is analyzed. But the distribution is not shown to be independent of sets of Variates.
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