Adaptive Bacteria Colony Picking in Unstructured Environments Using Intensity Histogram and Unascertained LS-SVM Classifier
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 trea...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/928395 |
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| author | Kun Zhang Minrui Fei Xin Li Huiyu Zhou |
| author_facet | Kun Zhang Minrui Fei Xin Li Huiyu Zhou |
| author_sort | Kun Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-caa96c4144a24ed0a57a32725d3535cb |
| institution | OA Journals |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-caa96c4144a24ed0a57a32725d3535cb2025-08-20T02:19:35ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/928395928395Adaptive Bacteria Colony Picking in Unstructured Environments Using Intensity Histogram and Unascertained LS-SVM ClassifierKun Zhang0Minrui Fei1Xin Li2Huiyu Zhou3School of Mechatronic Engineering & Automation, Shanghai University, M8 Building, 149 Yanchang Road, ZhaBei District, Shanghai 200072, ChinaSchool of Mechatronic Engineering & Automation, Shanghai University, M8 Building, 149 Yanchang Road, ZhaBei District, Shanghai 200072, ChinaSchool of Mechatronic Engineering & Automation, Shanghai University, M8 Building, 149 Yanchang Road, ZhaBei District, Shanghai 200072, ChinaThe Institute of Electronics, Communications and Information Technology, Queen’s University Belfast, Belfast, UKFeatures 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.http://dx.doi.org/10.1155/2014/928395 |
| spellingShingle | Kun Zhang Minrui Fei Xin Li Huiyu Zhou Adaptive Bacteria Colony Picking in Unstructured Environments Using Intensity Histogram and Unascertained LS-SVM Classifier The Scientific World Journal |
| title | Adaptive Bacteria Colony Picking in Unstructured Environments Using Intensity Histogram and Unascertained LS-SVM Classifier |
| title_full | Adaptive Bacteria Colony Picking in Unstructured Environments Using Intensity Histogram and Unascertained LS-SVM Classifier |
| title_fullStr | Adaptive Bacteria Colony Picking in Unstructured Environments Using Intensity Histogram and Unascertained LS-SVM Classifier |
| title_full_unstemmed | Adaptive Bacteria Colony Picking in Unstructured Environments Using Intensity Histogram and Unascertained LS-SVM Classifier |
| title_short | Adaptive Bacteria Colony Picking in Unstructured Environments Using Intensity Histogram and Unascertained LS-SVM Classifier |
| title_sort | adaptive bacteria colony picking in unstructured environments using intensity histogram and unascertained ls svm classifier |
| url | http://dx.doi.org/10.1155/2014/928395 |
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