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- Grelsson, Bertil, 1965-, et al.
(författare)
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HorizonNet for visual terrain navigation
- 2018
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Ingår i: Proceedings of 2018 IEEE International Conference on Image Processing, Applications and Systems (IPAS). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781728102474 - 9781728102467 - 9781728102481 ; , s. 149-155
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Konferensbidrag (refereegranskat)abstract
- This paper investigates the problem of position estimation of unmanned surface vessels (USVs) operating in coastal areas or in the archipelago. We propose a position estimation method where the horizon line is extracted in a 360 degree panoramic image around the USV. We design a CNN architecture to determine an approximate horizon line in the image and implicitly determine the camera orientation (the pitch and roll angles). The panoramic image is warped to compensate for the camera orientation and to generate an image from an approximately level camera. A second CNN architecture is designed to extract the pixelwise horizon line in the warped image. The extracted horizon line is correlated with digital elevation model (DEM) data in the Fourier domain using a MOSSE correlation filter. Finally, we determine the location of the maximum correlation score over the search area to estimate the position of the USV. Comprehensive experiments are performed in a field trial in the archipelago. Our approach provides promising results by achieving position estimates with GPS-level accuracy.
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| 2. |
- Sun, Yibao, et al.
(författare)
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Detection of Breast Tumour Tissue Regions in Histopathological Images using Convolutional Neural Networks
- 2018
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Ingår i: 2018 IEEE International Conference on Image Processing, Applications and Systems (IPAS). - : IEEE. - 9781728102474 ; , s. 98-103
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Konferensbidrag (refereegranskat)abstract
- Ductal carcinoma in situ (DCIS) is considered a pre-invasive breast cancer and sometimes it can develop into an invasive ductal carcinoma. The analysis of histopathological images to detect tumour border of DCIS could provide important information for better diagnosis of patients. We present a deep learning based system to automatically identify DCIS in histopathological images. Specifically, a convolutional neural network (CNN) is first trained to predict labels of small patches cropped out of a histopathological whole slide image. Next, a sliding window method is used to produce a probability map of DCIS. Finally, given the probability map, a tumor border of DCIS is produced and delineated with the method of Marching Cubes to facilitate pathologists' review and assessment. Evaluation of cross validation demonstrates that the CNN model of GoogleNet performs well in histology image patch classification with an overall accuracy of (98.46 +/- 0.40)% and identifies the DCIS tissue patches with a Fl-score of (97.40 +/- 1.18)% (mean +/- variance). Moreover, around 95.6% tumour tissue within the enclosed tumour regions can be identified by our developed method. Finally, the goal of tumor border detection can be well achieved with a few post-processing steps.
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