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Proceedings Paper

Contaminant detection on poultry carcasses using hyperspectral data: Part II. Algorithms for selection of sets of ratio features
Author(s): Songyot Nakariyakul; David P. Casasent
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Paper Abstract

We consider new methods to select useful sets of ratio features in hyperspectral data to detect contaminant regions on chicken carcasses using data provided by ARS (Athens, GA). A ratio feature is the ratio of the response at each pixel for two different wavebands. Ratio features perform a type of normalization and can thus help reduce false alarms, if a good normalization algorithm is not available. Thus, they are of interest. We present a new algorithm for the general problem of such feature selection in high-dimensional data. The four contaminant types of interest are three types of feces from different gastrointestinal regions (duodenum, ceca, and colon) and ingesta (undigested food) from the gizzard. To select the best two sets of ratio features from this 492-band HS data requires an exhaustive search of more than seven billion combinations of two sets of ratio features, which is very excessive. Thus, we propose our new fast ratio feature selection algorithm that requires evaluation of a much fewer number of sets of ratio features and is capable of giving quasi-optimal or optimal sets of ratio features. This new feature selection method has not been previously presented. It is shown to offer promise for an excellent detection rate and a low false alarm rate for this application. Our tests use data with different feed types and different contaminant types.

Paper Details

Date Published: 12 October 2007
PDF: 12 pages
Proc. SPIE 6761, Optics for Natural Resources, Agriculture, and Foods II, 67610S (12 October 2007); doi: 10.1117/12.734593
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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