As human visual attention is naturally biased towards foreground objects in a scene, it can be used to extract salient objects in video clips. In this work, we proposed a weakly supervised learning based video saliency detection algorithm utilizing eye fixations information from multiple subjects. Our main idea is to extend eye fixations to saliency regions step by step. First, visual seeds are collected using multiple color space geodesic distance based seed region mapping with filtered and extended eye fixations. This operation helps raw fixation points spread to the most likely salient regions, namely, visual seed regions. Second, in order to seize the essential scene structure from video sequences, we introduce the total variance based pairwise interaction model to learn the potential pairwise relationship between foreground and background within a frame or across video frames. In this vein, visual seed regions eventually grow into salient regions. Compared with previous approaches the generated saliency maps has two most outstanding properties: integrity and purity, which are conductive to segment the foreground and significant to the follow-up tasks. Extensive quantitative and qualitative experiments on various video sequences demonstrate that the proposed method outperforms state-of-theart image and video saliency detection algorithms.
Fishing net cleanliness plays a critical role for aquaculture industry as bio-fouled nets restrict the flow of water through the net leading to a build-up of toxins and reduced oxygen levels within the pen, thereby putting the fish under increased stress. In this paper, we proposed an underwater fishing Net Health State Estimation (NHSE) method, which can automatically analyze the degree of fouling on the net through underwater image analysis using remotely operated vehicles (ROV) images, and calculate a blocking percentage metric of each net opening. The level of fouling estimated through this method help the operators decide on the need of cleaning or maintenance schedule. There are mainly six modules in the proposed NHSE method, namely user interaction, distortion correction, underwater image dehazing, marine growth segmentation, net-opening structure analysis, and blocked percentage estimation. To evaluate the proposed NHSE method, we collected and labeled several underwater images in Mulroy Bay, Ireland with pixel-wise annotations. In order to verify the universality and robustness of the algorithm, we simulated and built a virtual fishing farm, and, on this basis, collected and labeled fishing net images under different environmental conditions. Seven evaluation metrics are introduced to demonstrate the effectiveness and advantages of the proposed method.
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