Double regularization control based on level set evolution for tablet packaging image segmentation

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• 作者：Li Liu (1) (3)
  Ao-Lei Yang (1)
  Xiao-Wei Tu (1)
  Wen-Ju Zhou (2) (3)
  Min-Rui Fei (1)
  Jun Yue (3)
  
  1. Shanghai Key Laboratory of Power Station Automation Technology ; School of Mechatronic Engineering and Automation ; Shanghai University ; Shanghai ; 200072 ; People鈥檚 Republic of China
  3. School of Information Science and Electrical Engineering ; Ludong University ; Yantai ; 264025 ; People鈥檚 Republic of China
  2. School of Computer Science and Electronic Engineering ; University of Essex ; Colchester ; CO4 3SQ ; UK
  
• 关键词：Tablet packaging image ; Level set evolution ; Image segmentation ; Curvatures ; Double regularization control (DRC)
• 刊名：Advances in Manufacturing
• 出版年：2015
• 出版时间：March 2015
• 年：2015
• 卷：3
• 期：1
• 页码：73-83
• 全文大小：6,509 KB
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• 刊物主题：Manufacturing, Machines, Tools; Control, Robotics, Mechatronics; Nanotechnology and Microengineering;
• 出版者：Springer Berlin Heidelberg
• ISSN：2195-3597

文摘

This paper proposes a novel double regularization control (DRC) method which is used for tablet packaging image segmentation. Since the intensities of tablet packaging images are inhomogenous, it is difficult to make image segmentation. Compared to methods based on level set, the proposed DRC method has some advantages for tablet packaging image segmentation. The local regional control term and the rectangle initialization contour are first employed in this method to quickly segment uneven grayscale images and accelerate the curve evolution rate. Gaussian filter operator and the convolution calculation are then adopted to remove the effects of texture noises in image segmentation. The developed penalty energy function, as regularization term, increases the constrained conditions based on the gradient flow conditions. Since the potential function is embedded into the level set of evolution equations and the image contour evolutions are bilaterally extended, the proposed method further improves the accuracy of image contours. Experimental studies show that the DRC method greatly improves the computational efficiency and numerical accuracy, and achieves better results for image contour segmentation compared to other level set methods.