Titlebook: Image Analysis; 22nd Scandinavian Co Rikke Gade,Michael Felsberg,Joni-Kristian K?m?r?in Conference proceedings 2023 The Editor(s) (if appli

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Fully Variational Noise-Contrastive Estimation variable models. Both terms in the underlying NCE loss, the one using data samples and the one using noise samples, can be lower-bounded as in variational Bayes, therefore we call this family of losses .. Variational autoencoders are a particular example in this family and therefore can be also und

headway

An Analysis of?Spatial-Spectral Dependence in?Hyperspectral Autoencodersrne imaging systems. Proximal hyperspectral imaging is also widely used in plant and agriculture science. It allows the remote capturing of leaf reflectance information in order to determine and classify plant health and disease. With the high information density in hyperspectral images, it becomes

hypnogram

緊張過度

Introducing Spatial Context in?Patch-Based Deep Learning for?Semantic Segmentation in?Whole Body MRItation often plays an important role. Nowadays, convolutional neural networks (CNN) are the go-to method for automated image segmentation. Unfortunately, with improved image resolution, whole-body scans can grow large in size, preventing CNN training on entire images due to GPU memory limitations. A

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Learning to?Pay Attention by?Paying Attentionroaches often introduce artifacts and especially fail to produce bone structures accurately because of the missing signal from bone regions in the source domain. To handle this limitation, we propose a novel approach inspired by the Attention U-Net that includes an additional bone segmentation-guide

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Correl-Net: Defect Segmentation in?Old Films Using Correlation Networkse formulated as a binary segmentation problem and solved using state-of-the-art segmentation networks such as . or .. While being very powerful at describing the spatial characteristics of defects, these methods fail to take into account the fact that defects are also temporal anomalies. We therefor

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