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Proceedings Paper

Contaminant detection on poultry carcasses using hyperspectral data: Part I. Algorithms for selection of individual wavebands
Author(s): Songyot Nakariyakul; David P. Casasent
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Paper Abstract

Contaminant detection on chicken carcasses is an important product inspection application. The four contaminant types of interest contain three types of feces from different gastrointestinal regions (duodenum, ceca, and colon) and ingesta (undigested food) from the gizzard. Use of automated or semi-automated inspection systems for detecting fecal contaminant regions is of great interest. Hyperspectral data provided by ARS (Athens, GA) were used to examine detection of contaminants on carcasses. We address quasi-optimal algorithms for selecting a set of spectral bands (wavelengths) in hyperspectral data for on-line contaminant detection (feature selection). We introduce our new improved forward floating selection (IFFS) algorithm and compare its performance to that of other state-of-the-art feature selection algorithms. Our initial results indicate that our method gives an excellent detection rate and performs better than other feature selection algorithms. We also show that combination feature selection algorithms perform worse.

Paper Details

Date Published: 12 October 2007
PDF: 12 pages
Proc. SPIE 6761, Optics for Natural Resources, Agriculture, and Foods II, 67610R (12 October 2007); doi: 10.1117/12.734582
Show Author Affiliations
Songyot Nakariyakul, Carnegie Mellon Univ. (United States)
David P. Casasent, Carnegie Mellon Univ. (United States)

Published in SPIE Proceedings Vol. 6761:
Optics for Natural Resources, Agriculture, and Foods II
Yud-Ren Chen; George E. Meyer; Shu-I Tu, Editor(s)

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