For in-vivo diagnostics of cancer and pre-cancer in the stomach, there is no endoscopic procedure offering both high sensitivity and high specificity. Our data suggest that multispectral or hyperspectral imaging may be helpful to solve this problem. It is successfully applied to the detection and analysis of easily reachable carcinomas, ex-vivo samples of hollow organ mucosal carcinomas and als...

This paper introduces a new orthogonal transfonn the multivariate change detection (MeD) transfonn based on an established multivariate statistical technique canonical correlation analysis. The theory for canonical correlation analysis is sketched and modified to be more directly applicable in our context. As opposed to traditional univariate change detection schemes our scheme transforms two s...

Multispectral pedestrian detection has received extensive attention in recent years as a promising solution to facilitate robust human target detection for around-the-clock applications (e.g. security surveillance and autonomous driving). In this paper, we demonstrate illumination information encoded in multispectral images can be utilized to significantly boost performance of pedestrian detect...

The aim of this research is to determine an accuracy assessment of different multispectral gradient-based edge detectors. We will present and evaluate three different approaches: the mean-, the maximumand the multispectral gradient approach. The mean approach determines the overall gradient as the arithmetic mean of all (band-wise) gradient vectors, whereas the maximum approach selects the grad...

Spectral imaging including machine vision and multispectral imaging can provide a rapid, nondestructive means to assess agricultural commodities for their quality and safety for human consumption. These nondestructive techniques generally rely on reflectance measurements; the most commonly used spectral regions range through the visible (Vis) to the near infrared (NIR). Another optical sensing ...

Data simulation is widely used in remote sensing to produce imagery for a new sensor in the design stage, for scale issues of some special applications, or for testing of novel algorithms. Hyperspectral data could provide more abundant information than traditional multispectral data and thus greatly extend the range of remote sensing applications. Unfortunately, hyperspectral data are much more...

The issues connected with the problem of choosing adequate algorithms of the Earth remote sensing multispectral data recognition are considered. The system of supervised classification based on Bayesian strategy of maximum likelihood for normally distributed vectors of features is represented. The system of cluster analysis including the algorithm of K means and clusterization based on the anal...

Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, Agricultural Research Service, United States Department of Agriculture, 10300 Baltimore Ave, Beltsville, MD 20705 National Academy of Agricultural Science, Rural Development Administration, Suwon 441-100, Republic of Korea Department of Biosystems Machinery Engineering, Chungnam National University, Dae...

The goal of this study was to design a reliable method to quantify and visualize the anatomical distribution of cerebrospinal fluid (CSF) intracranially. The method should be clinically applicable and based on multispectral analysis of three-dimensional (3D) magnetic resonance images. T1-weighted, T2-weighted and proton density-weighted fast 3D gradient pulse sequences were used to form high re...

When performing post-classification comparison using images of different sensors, change detection is still possible even if images have different resolutions. However, in this case, change pixels are detected in the pixel size of coarser resolution image. This problem can be solved using higher resolution aerial photographs or panchromatic images if available. In multi sensor image fusion, ima...