The research of removing rain from pictures or videos has always been an important topic in the field of computer vision and image processing. Most noise reduction methods more or less remove texture details in rain-f...The research of removing rain from pictures or videos has always been an important topic in the field of computer vision and image processing. Most noise reduction methods more or less remove texture details in rain-free areas, resulting in an over-smoothing effect in the restored background. The research on image noise removal is very meaningful. We exploit the powerful generative power of a modified generative adversarial network (CGAN) by enforcing an additional condition that makes the derained image indistinguishable from its corresponding ground-truth clean image. An efficient and lightweight attention machine mechanism NAM is introduced in the generator, and an IDN-CGAN model is proposed to capture image salient features through attention operations. Taking advantage of the mutual information in different dimensions of the features to further suppress insignificant channels or pixels to ensure better visual quality, we also introduce a new fine-grained loss function in the generator-discriminator pair, predicting and real data degree of disparity to achieve improved results.展开更多
Fish behavior refers to various movements of fish. Fish behavior is closely related to the ecology of fish, physiological changes of fish, aquaculture and so on. Related applications will be expanded if fish behavior ...Fish behavior refers to various movements of fish. Fish behavior is closely related to the ecology of fish, physiological changes of fish, aquaculture and so on. Related applications will be expanded if fish behavior is analyzed properly. Traditional analysis of fish behavior mainly relies on the observation of human eyes. With the deepening and extension of application and the rapid development of computer technology, computer vision technology is increasingly used to analyze fish behaviors. This paper summarized the research status, research progress and main problems of fish behavior analysis by using computer vision and made forecast about future research.展开更多
With the development of fishery industry,accurate estimation of the number of fish in aquaculture waters is of great importance to fish behavior analysis,bait feeding and fishery resource investigation.In this paper,w...With the development of fishery industry,accurate estimation of the number of fish in aquaculture waters is of great importance to fish behavior analysis,bait feeding and fishery resource investigation.In this paper,we propose a method for fish density estimation based on the multi-scale context enhanced convolutional network,which could map a fish school image taken at any angle to a density map,and calculate the number of fish in the image finally.In order to eliminate the influence of camera perspective effect and image resolution on density estimation,multi-scale filters are utilized in a convolutional neural network to process fish image in parallel.And then,the context enhancement module is merged in the network structure to help the network understand the global context information of the image.Finally,different feature maps are merged together to construct the density map of fish school images,and finally get the number of fish in the image.In order to make the effectiveness of our method valid,we test the proposed method on DlouDataset.The results show that the proposed method has lower mean square error and mean absolute error,which is helpful to improve the accuracy of the fish counting in dense fish school images.展开更多
文摘The research of removing rain from pictures or videos has always been an important topic in the field of computer vision and image processing. Most noise reduction methods more or less remove texture details in rain-free areas, resulting in an over-smoothing effect in the restored background. The research on image noise removal is very meaningful. We exploit the powerful generative power of a modified generative adversarial network (CGAN) by enforcing an additional condition that makes the derained image indistinguishable from its corresponding ground-truth clean image. An efficient and lightweight attention machine mechanism NAM is introduced in the generator, and an IDN-CGAN model is proposed to capture image salient features through attention operations. Taking advantage of the mutual information in different dimensions of the features to further suppress insignificant channels or pixels to ensure better visual quality, we also introduce a new fine-grained loss function in the generator-discriminator pair, predicting and real data degree of disparity to achieve improved results.
基金Guangdong Province Key Laboratory of Popular High Performance Computers (SZU-GDPHPCL201805)Institute of Marine Industry Technology of Universities in Liaoning Province (2018-CY-34)+2 种基金National Natural Science Foundation of China (61701070)Liaoning Doctoral Start-up Fund (20180540090)China Postdoctoral Science Foundation (2018M640239).
文摘Fish behavior refers to various movements of fish. Fish behavior is closely related to the ecology of fish, physiological changes of fish, aquaculture and so on. Related applications will be expanded if fish behavior is analyzed properly. Traditional analysis of fish behavior mainly relies on the observation of human eyes. With the deepening and extension of application and the rapid development of computer technology, computer vision technology is increasingly used to analyze fish behaviors. This paper summarized the research status, research progress and main problems of fish behavior analysis by using computer vision and made forecast about future research.
基金This work is supported by Institute of Marine Industry Technology of Universities in Liaoning Province(2018-CY-34)National Natural Science Foundation of China(31972846)+1 种基金China Postdoctoral Science Foundation(2018M640239)Acknowledgement for the Data Support from National Marine Science Data Center(Dalian),National Science&Technology Resource Sharing Service Platform of China(http://odc.dlou.edu.cn/).
文摘With the development of fishery industry,accurate estimation of the number of fish in aquaculture waters is of great importance to fish behavior analysis,bait feeding and fishery resource investigation.In this paper,we propose a method for fish density estimation based on the multi-scale context enhanced convolutional network,which could map a fish school image taken at any angle to a density map,and calculate the number of fish in the image finally.In order to eliminate the influence of camera perspective effect and image resolution on density estimation,multi-scale filters are utilized in a convolutional neural network to process fish image in parallel.And then,the context enhancement module is merged in the network structure to help the network understand the global context information of the image.Finally,different feature maps are merged together to construct the density map of fish school images,and finally get the number of fish in the image.In order to make the effectiveness of our method valid,we test the proposed method on DlouDataset.The results show that the proposed method has lower mean square error and mean absolute error,which is helpful to improve the accuracy of the fish counting in dense fish school images.