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19 September 2019 Semantic segmentation of panoramic images using a synthetic dataset
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Abstract
Panoramic images have advantages in information capacity and scene stability due to their large field of view (FoV). In this paper, we propose a method to synthesize a new dataset of panoramic image. We managed to stitch the images taken from different directions into panoramic images, together with their labeled images, to yield the panoramic semantic segmentation dataset denominated as SYNTHIA-PANO. For the purpose of finding out the effect of using panoramic images as training dataset, we designed and performed a comprehensive set of experiments. Experimental results show that using panoramic images as training data is beneficial to the segmentation result. In addition, it has been shown that by using panoramic images with a 180 degree FoV as training data the model has better performance. Furthermore, the model trained with panoramic images also has a better capacity to resist the image distortion. Our codes and SYNTHIA-PANO dataset are available: https://github.com/Francis515/SYNTHIA-PANO.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yuanyou Xu, Kaiwei Wang, Kailun Yang, Dongming Sun, and Jia Fu "Semantic segmentation of panoramic images using a synthetic dataset", Proc. SPIE 11169, Artificial Intelligence and Machine Learning in Defense Applications, 111690B (19 September 2019); https://doi.org/10.1117/12.2532494
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