Adaptive Bacteria Colony Picking in Unstructured Environments Using Intensity Histogram and Unascertained LS-SVM Classifier

Kun Zhang, Minrui Fei, Xin Li, Huiyu Zhou

Research output: Contribution to journalArticle

1 Citation (Scopus)
226 Downloads (Pure)

Abstract

Features analysis is an important task which can significantly affect the performance of automatic bacteria colony picking. Unstructured environments also affect the automatic colony screening. This paper presents a novel approach for adaptive colony segmentation in unstructured environments by treating the detected peaks of intensity histograms as a morphological feature of images. In order to avoid disturbing peaks, an entropy based mean shift filter is introduced to smooth images as a preprocessing step. The relevance and importance of these features can be determined in an improved support vector machine classifier using unascertained least square estimation. Experimental results show that the proposed unascertained least square support vector machine (ULSSVM) has better recognition accuracy than the other state-of-the-art techniques, and its training process takes less time than most of the traditional approaches presented in this paper.
Original languageEnglish
Article number928395
Number of pages10
JournalThe Scientific World Journal
Volume2014
DOIs
Publication statusPublished - 11 May 2014

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histogram
Least-Squares Analysis
Support vector machines
Bacteria
Classifiers
bacterium
Entropy
Screening
Teaching
segmentation
entropy
filter
Support Vector Machine
support vector machine

Cite this

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abstract = "Features analysis is an important task which can significantly affect the performance of automatic bacteria colony picking. Unstructured environments also affect the automatic colony screening. This paper presents a novel approach for adaptive colony segmentation in unstructured environments by treating the detected peaks of intensity histograms as a morphological feature of images. In order to avoid disturbing peaks, an entropy based mean shift filter is introduced to smooth images as a preprocessing step. The relevance and importance of these features can be determined in an improved support vector machine classifier using unascertained least square estimation. Experimental results show that the proposed unascertained least square support vector machine (ULSSVM) has better recognition accuracy than the other state-of-the-art techniques, and its training process takes less time than most of the traditional approaches presented in this paper.",
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Adaptive Bacteria Colony Picking in Unstructured Environments Using Intensity Histogram and Unascertained LS-SVM Classifier. / Zhang, Kun; Fei, Minrui; Li, Xin; Zhou, Huiyu.

In: The Scientific World Journal, Vol. 2014, 928395, 11.05.2014.

Research output: Contribution to journalArticle

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