Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing; 28th International C Igor V. Tetko,Věra K?rková,Fabian Thei

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Severe Convective Weather Classification in Remote Sensing Images by Semantic Segmentations how to recognize severe convection weather accurately and effectively, and it is also an important issue in government climate risk management. However, most existing methods extract features from satellite data by classifying individual pixels instead of using tightly integrated spatial informati

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Action Recognition Based on Divide-and-Conquert of redundant information, compared with dense sampling, sparse sampling network can also achieve good results. Due to sparse sampling’s limitation of access to information, this paper mainly discusses how to further improve the learning ability of the model based on sparse sampling. We proposed a

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In-Silico Staining from Bright-Field and Fluorescent Images Using Deep Learningus and costly, it damages tissue and suffers from inconsistencies. Recently deep learning approaches have been successfully applied to predict fluorescent markers from bright-field images [.,.,.]. These approaches can save costs and time and speed up the classification of tissue properties. However,

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A Lightweight Neural Network for Hard Exudate Segmentation of Fundus Imagete, a special kind of lesion in the fundus image, is treated as the basis to evaluate the severity level of DR. Therefore, it is crucial to segment hard exudate exactly. However, the segmentation results of existing deep learning-based segmentation methods are rather coarse due to successive pooling

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https://doi.org/10.1007/978-3-030-30508-6artificial intelligence; classification; clustering; computational linguistics; computer networks; Human-

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978-3-030-30507-9Springer Nature Switzerland AG 2019

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/162645.jpg