title: Adaptive bacteria colony picking in unstructured environments using intensity histogram and unascertained LS-SVM classifier.
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.
authors: School of Mechatronic Engineering & Automation, Shanghai University, M8 Building, 149 Yanchang Road, ZhaBei District, Shanghai 200072, China.
authors: Kun
authors: K
authors: Zhang
authors: School of Mechatronic Engineering & Automation, Shanghai University, M8 Building, 149 Yanchang Road, ZhaBei District, Shanghai 200072, China.
authors: Minrui
authors: M
authors: Fei
authors: School of Mechatronic Engineering & Automation, Shanghai University, M8 Building, 149 Yanchang Road, ZhaBei District, Shanghai 200072, China.
authors: Xin
authors: X
authors: Li
authors: The Institute of Electronics, Communications and Information Technology, Queen's University Belfast, Belfast, UK.
authors: Huiyu
authors: 0000-0003-1634-9840
authors: H
authors: Zhou
mesh_terms: D001419:Bacteria / Q000302:isolation & purification*; D016018:Least-Squares Analysis; D060388:Support Vector Machine