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arXiv 2020年

作者： Kumar, Nishant Gumhold, Stefan Chair of Computer Graphics and Visualisation Faculty of Computer Science Technische Universität Dresden Nöthnitzer Straße 46 Dresden01187 Germany

Image fusion helps in merging two or more images to construct a more informative single fused image. Recently, unsupervised learning based convolutional neural networks (CNN) have been utilized for different types of image fusion tasks such as medical image fusion, infrared-visible image fusion for autonomous driving as well as multi-focus and multi-exposure image fusion for satellite imagery. However, it is challenging to analyze the reliability of these CNNs for the image fusion tasks since no groundtruth is available. This led to the use of a wide variety of model architectures and optimization functions yielding quite different fusion results. Additionally, due to the highly opaque nature of such neural networks, it is difficult to explain the internal mechanics behind its fusion results. To overcome these challenges, we present a novel real-time visualization tool, named FuseVis, with which the end-user can compute per-pixel saliency maps that examine the influence of the input image pixels on each pixel of the fused image. We trained several image fusion based CNNs on medical image pairs and then using our FuseVis tool, we performed case studies on a specific clinical application by interpreting the saliency maps from each of the fusion methods. We specifically visualized the relative influence of each input image on the predictions of the fused image and showed that some of the evaluated image fusion methods are better suited for the specific clinical application. To the best of our knowledge, currently, there is no approach for visual analysis of neural networks for image fusion. Therefore, this work opens up a new research direction to improve the interpretability of deep fusion networks. The FuseVis tool can also be adapted in other deep neural network based image processing applications to make them interpretable. © 2020, CC BY.

关键词： Image fusion

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Wifbs: A web-based image feature benchmark system 21

Wifbs: A web-based image feature benchmark system

引用

21st International Conference on MultiMedia Modeling, MMM 2015

作者： Spehr, Marcel Grottel, Sebastian Gumhold, Stefan Chair of Computer Graphics and Visualisation Institute of Software and Multimedia Technology TU Dresden Nöthnitzer Strasse 46 Dresden01087 Germany

ISBN: (纸本)9783319144412

Automatic analysis of image data is of high importance for many applications. Given an image classification problem one needs three things: (i) Training data and tools to extract (ii) relevant visual information— usually image features—that can be used by (iii) classification algorithms. For given (i), a multitude of candidates present themselves for (ii) and (iii). Model selection becomes the main issue. We present a webbased feature benchmark system enabling system designers to streamline tool-chains to specific needs using available implementations of candidate tools. Our system features a modular architecture, remote and parallel computing, extensibility and—from a user’s standpoint—platform independence due to its web-based nature. Using Wifbs1, image features can be subjected to a sophisticated and unbiased model selection procedure to compose optimized pipelines for given image classification problems. © Springer International Publishing Switzerland 2015.

关键词： Image classification

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