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https://www.um.edu.mt/library/oar/handle/123456789/29162
Title: | A deep learning approach for detecting and correcting highlights in endoscopic images |
Authors: | Rodriguez-Sanchez, Antonio Chea, Daly Azzopardi, George Stabinger, Sebastian |
Keywords: | Image processing Imaging systems -- Algorithms Imaging systems in medicine |
Issue Date: | 2017 |
Publisher: | Institute of Electrical and Electronics Engineers |
Citation: | Rodriguez-Sanchez, A., Chea, D., Azzopardi, G., & Stabinger, S. (2017). A deep learning approach for detecting and correcting highlights in endoscopic images.Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA, 2017), IEEE, Montreal, 1-6. |
Abstract: | The image of an object changes dramatically depending on the lightning conditions surrounding that object. Shadows, reflections and highlights can make the object very difficult to be recognized for an automatic system. Additionally, images used in medical applications, such as endoscopic images and videos contain a large amount of such reflective components. This can pose an extra difficulty for experts to analyze such type of videos and images. It can then be useful to detect - and possibly correct - the locations where those highlights happen. In this work we designed a Convolutional Neural Network for that task. We trained such a network using a dataset that contains groundtruth highlights showing that those reflective elements can be learnt and thus located and extracted. We then used that trained network to localize and correct the highlights in endoscopic images from the El Salvador Atlas Gastrointestinal videos obtaining promising results. |
URI: | https://www.um.edu.mt/library/oar//handle/123456789/29162 |
Appears in Collections: | Scholarly Works - FacICTAI |
Files in This Item:
File | Description | Size | Format | |
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A_deep_learning_approach_for_detecting_and_correcting_highlights_in_endoscopic_ images_2017.pdf Restricted Access | 972.35 kB | Adobe PDF | View/Open Request a copy |
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