Publication

Multispectral Interest Points for RGB-NIR Image Registration

Sabine Süsstrunk, Matthew Brown, Damien Firmenich
2011
Conference Papers

Abstract

This paper explores the use of joint colour and near-infrared (NIR) information for feature based matching and image registration. In particular, we investigate multispectral generalisations of two popular interest point detectors (Harris and difference of Gaussians), and show that these give a marked improvement in performance when the extra NIR channel is available. We also look at the problem of multimodal RGB to NIR registration, and propose a variant of the SIFT descriptor that gives improved performance.

Official source

https://infoscience.epfl.ch/entities/publication/52660a19-5e3e-4600-820f-c32dde9fc767

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Sabine Süsstrunk, Matthew Brown, Damien Firmenich
2011
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/52660a19-5e3e-4600-820f-c32dde9fc767

About this result

Related concepts (13)

Image registration

Image registration is the process of transforming different sets of data into one coordinate system. Data may be multiple photographs, data from different sensors, times, depths, or viewpoints. It is used in computer vision, medical imaging, military automatic target recognition, and compiling and analyzing images and data from satellites. Registration is necessary in order to be able to compare or integrate the data obtained from these different measurements.

Scale-invariant feature transform

The scale-invariant feature transform (SIFT) is a computer vision algorithm to detect, describe, and match local features in images, invented by David Lowe in 1999. Applications include object recognition, robotic mapping and navigation, , 3D modeling, gesture recognition, video tracking, individual identification of wildlife and match moving. SIFT keypoints of objects are first extracted from a set of reference images and stored in a database.

Blob detection

In computer vision, blob detection methods are aimed at detecting regions in a that differ in properties, such as brightness or color, compared to surrounding regions. Informally, a blob is a region of an image in which some properties are constant or approximately constant; all the points in a blob can be considered in some sense to be similar to each other. The most common method for blob detection is convolution.

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