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The explosive growth of the underwater images make the demand for automatic accurate object detection more and more urgent. In this paper, we introduced a deep but lightweight neural network to detect fishes. It achieved the state-of-the-art accuracy for fish detection on the dataset of ImageCLEF, which includes 24,277 fish images belonging to 12 classes. Compared with the common used detection network,...
In this paper, we introduce a deep residual network to classify images of plankton. The Plankton Dataset, which consists of 30,336 plankton images of 121 classes, was used for a data science competition hosted on the Kaggle platform1. We finally achieved a top-5 accuracy of 95.8% and a nearly real-frame rate of 9.1ftps, which is close to the accuracy of the No.1 team (over 98%, 1.4ftps) 2 in the competition...
The stock market is an important component in the current economic market. And stock price prediction has recently garnered significant interest among investment brokers, individual investors and researchers. In general, stock market is very complex nonlinear dynamic system. Accordingly, accurate prediction of stock market is a very challenging task, owing to the inherent noisy environment and high...
Underwater object recognition is in great demand, while the research is far from enough. The unrestricted natural environment makes it a challenging task. We propose a framework to recognize fish from videos captured by underwater cameras deployed in the ocean observation network. First, we extract the foreground via sparse and low-rank matrix decomposition. Then, a deep architecture is used to extract...
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