The Chan-Vese model is very popular for image segmentation. Technically, it combines the reduced Mumford-Shah model and level set method (LSM). This segmentation problem is solved interchangeably by computing a gradient descent flow and expensively and tediously re-initializing a level set function (LSF). Though many approaches have been proposed to overcome the re-initialization problem, the low efficiency for this segmentation problem is still not solved effectively. In this paper, we first investigate the relationship between the L 1 -based total variation (TV) regularizer term of Chan-Vese model and the constraint on LSF and then propose a new technique to solve the re-initialization problem. In detail, four fast projection methods are proposed, i.e., split Bregman projection method (SBPM), augmented Lagrangian projection method (ALPM), dual split Bregman projection method (DSBPM), and dual augmented Lagrangian projection method (DALPM). These four methods without re-initialization are faster than the existing approaches. Finally, extensive numerical experiments on synthetic and real images are presented to validate the effectiveness and efficiency of these four proposed methods.
Deep-sea debris is a globally growing problem that negatively impacting biological and chemical ecosystems. More seriously, the debris is likely to persist in the deep sea for long periods. Fortunately, with the help of the debris detection system the submersibles can clean up the debris. An excellent classifier is critical to the debris detection system. Therefore the objective of this study is to determine whether deep convolutional neural networks can distinguish the differences of debris and natural deep-sea environment, so as to effectively achieve deep-sea debris identification. First, a real deep-sea debris images (DDI) dataset is constructed for further classification research based on an online deep-sea debris database owned by the Japan Agency for Marine-Earth Science and Technology (JAMSTEC). Second, the hybrid Shuffle-Xception network is constructed to classify the deep-sea image as metal, glass, plastic, rubber, fishing net &rope, natural debris, and cloth. Furthermore, five common convolutional neural networks (CNNs) frameworks are also employed to implement the classification process. Finally, the identification experiments are carried out to validate the performance of the proposed methodology. The results demonstrate that the proposed method is superior to the state-of-art CNN methods and has the potential for deep-sea debris identification.
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