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Detecting small objects in high resolution images with integral fisher score

conference contribution
posted on 2018-01-01, 00:00 authored by R Leyva, V Sanchez, Chang-Tsun LiChang-Tsun Li
Nowadays, big imaging data are very common in many fields of study. As a result, detecting small objects in very large images is challenging and computationally demanding. Taking advantage of the intrinsic cumulative properties of the Fisher Score, we propose the Integral Fisher Score (IFS) for low-complexity and accurate object detection in big imaging data. The IFS, which is a multi-dimensional extension of the Integral Image, allows computing the Fisher Vector associated with a spatial region using only four operations. This considerably reduces the computational cost of searching for a small query object on a very large target image. Evaluations for the detection of small object on high-resolution HUB telescope and digital pathology images show that IFS attains a high accuracy with short processing times.

History

Pagination

316-320

Location

Athens, Greece

Start date

2018-10-07

End date

2018-10-10

ISSN

1522-4880

ISBN-13

9781479970612

Language

eng

Publication classification

E1.1 Full written paper - refereed

Copyright notice

2018, IEEE

Editor/Contributor(s)

[Unknown]

Title of proceedings

ICIP 2018 : Proceedings of the 2018 25th IEEE International Conference on Image Processing

Event

IEEE Signal Processing Society. Conference (25th : 2018 : Athens, Greece)

Publisher

Institute of Electrical and Electronics Engineers

Place of publication

Piscataway, N.J.

Series

IEEE Signal Processing Society Conference

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