Object recognition using subspace methods

Lecture notes in computer sciencePublished 1 January 1996

Daniel P. Huttenlocher, Ryan Lilien, Clark F. Olson

DOI10.1007/bfb0015564

Source URL

Citations21

SJR quartileQ2

SJR score0.35

SNIP0.55

TL;DR

A new recognition method that uses a subspace representation to approximate the comparison of binary images (e.g. intensity edges) using the Hausdorff fraction is described, which enables an image to be searched efficiently for any of the objects in an image database.

Abstract

In this paper we describe a new recognition method that uses a subspace representation to approximate the comparison of binary images (e.g. intensity edges) using the Hausdorff fraction. The technique is robust to outliers and occlusion, and thus can be used for recognizing objects that are partly hidden from view and occur in cluttered backgrounds. We report some simple recognition experiments in which novel views of objects are classified using both a standard SSD-based eigenspace method and our Hausdorff-based method. These experiments illustrate how our method performs better when the background is unknown or the object is partially occluded. We then consider incorporating the method into an image search engine, for locating instances of objects under translation in an image. Results indicate that all but a small percentage of image locations can be ruled out using the eigenspace, without eliminating correct matches. This enables an image to be searched efficiently for any of the objects in an image database.

Keywords

Computer ScienceEngineering

Face recognition using eigenfaces

5,211 Citations2002Matthew Turk, Alex Pentland

An approach to the detection and identification of human faces is presented, and a working, near-real-time face recognition system which tracks a subject's head and then recognizes the person by comparing characteristics of the face to those of known individuals is described.

IEEE Transactions on Pattern Analysis and Machine IntelligenceComparing images using the Hausdorff distance

4,474 Citations1993D.P. Huttenlocher, G.A. Klanderman +1 more

Efficient algorithms for computing the Hausdorff distance between all possible relative positions of a binary image and a model are presented and it is shown that the method extends naturally to the problem of comparing a portion of a model against an image.

IEEE Transactions on Pattern Analysis and Machine IntelligenceApplication of the Karhunen-Loeve procedure for the characterization of human faces

2,454 Citations1990Michael Kirby, L. Sirovich

The use of natural symmetries (mirror images) in a well-defined family of patterns (human faces) is discussed within the framework of the Karhunen-Loeve expansion, which results in an extension of the data and imposes even and odd symmetry on the eigenfunctions of the covariance matrix.

International Journal of Computer VisionVisual learning and recognition of 3-d objects from appearance

1,891 Citations1995Hiroshi Murase, Shree K. Nayar

A near real-time recognition system with 20 complex objects in the database has been developed and a compact representation of object appearance is proposed that is parametrized by pose and illumination.

View-based and modular eigenspaces for face recognition

1,813 Citations1994Pentland, Moghaddam +1 more

A modular eigenspace description technique is used which incorporates salient features such as the eyes, nose and mouth, in an eigenfeature layer, which yields higher recognition rates as well as a more robust framework for face recognition.

Locating objects using the Hausdorff distance

107 Citations2002W.J. Rucklidge

This work develops a rasterised approach to the search and a number of techniques that allow it to quickly locate all transformations of the model that satisfy two quality criteria; it can also quickly locate only the best transformation.