Hausdorff kernel for 3D object acquisition and detection

14Citations
Citations of this article
21Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Learningone class at a time can be seen as an effective solution to classification problems in which only the positive examples are easily identifiable. A kernel method to accomplish this goal consists of a representation stage - which computes the smallest sphere in feature space enclosingthe positive examples - and a classification stage - which uses the obtained sphere as a decision surface to determine the positivity of new examples. In this paper we describe a kernel well suited to represent, identify, and recognize 3D objects from unconstrained images. The kernel we introduce, based on Hausdorff distance, is tailored to deal with grey-level image matching. The effectiveness of the proposed method is demonstrated on several data sets of faces and objects of artistic relevance, like statues.

References Powered by Scopus

Comparing Images Using the Hausdorff Distance

3724Citations
N/AReaders
Get full text

Neural network-based face detection

2814Citations
N/AReaders
Get full text

Trainable system for object detection

1159Citations
N/AReaders
Get full text

Cited by Powered by Scopus

On the significance of real-world conditions for material classification

279Citations
N/AReaders
Get full text

Building kernels from binary strings for image matching

104Citations
N/AReaders
Get full text

Binet-Cauchy kernels on dynamical systems and its application to the analysis of dynamic scenes

95Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Barla, A., Odone, F., & Verri, A. (2002). Hausdorff kernel for 3D object acquisition and detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2353, pp. 20–33). Springer Verlag. https://doi.org/10.1007/3-540-47979-1_2

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 14

74%

Professor / Associate Prof. 4

21%

Researcher 1

5%

Readers' Discipline

Tooltip

Computer Science 13

76%

Engineering 3

18%

Agricultural and Biological Sciences 1

6%

Save time finding and organizing research with Mendeley

Sign up for free