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Mathematical Problems in Engineering
Volume 2016 (2016), Article ID 6848360, 13 pages
Research Article

A Dynamic Feature-Based Method for Hybrid Blurred/Multiple Object Detection in Manufacturing Processes

Department of Information Technology and Communication, Shih Chien University, No. 200, University Road, Neimen, Kaohsiung 84550, Taiwan

Received 22 March 2016; Accepted 9 May 2016

Academic Editor: Zhike Peng

Copyright © 2016 Tsun-Kuo Lin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Vision-based inspection has been applied for quality control and product sorting in manufacturing processes. Blurred or multiple objects are common causes of poor performance in conventional vision-based inspection systems. Detecting hybrid blurred/multiple objects has long been a challenge in manufacturing. For example, single-feature-based algorithms might fail to exactly extract features when concurrently detecting hybrid blurred/multiple objects. Therefore, to resolve this problem, this study proposes a novel vision-based inspection algorithm that entails selecting a dynamic feature-based method on the basis of a multiclassifier of support vector machines (SVMs) for inspecting hybrid blurred/multiple object images. The proposed algorithm dynamically selects suitable inspection schemes for classifying the hybrid images. The inspection schemes include discrete wavelet transform, spherical wavelet transform, moment invariants, and edge-feature-descriptor-based classification methods. The classification methods for single and multiple objects are adaptive region growing- (ARG-) based and local adaptive region growing- (LARG-) based learning approaches, respectively. The experimental results demonstrate that the proposed algorithm can dynamically select suitable inspection schemes by applying a selection algorithm, which uses SVMs for classifying hybrid blurred/multiple object samples. Moreover, the method applies suitable feature-based schemes on the basis of the classification results for employing the ARG/LARG-based method to inspect the hybrid objects. The method improves conventional methods for inspecting hybrid blurred/multiple objects and achieves high recognition rates for that in manufacturing processes.