Abstract
Image stitching is an important task in image processing and computer vision. Image stitching is the process of combining multiple photographic images with overlapping fields of view to produce a segmented panorama, resolution image. It is widely used in object reconstruction, panoramic creating. In this paper, we present an approach based on deep learning for image stitching, which are applied to generate high resolution panoramic image supporting for virtual tour interaction. Different from most existing image matching methods, the proposed method extracts image features using deep learning approach. Our approach directly estimates locations of features between pairwise constraint of images by maximizing an image- patch similarity metric between images. A large dataset high resolution images and videos from natural tourism scenes were collected for training and evaluation. Experimental results illustrated that the deep feature approach outperforms.
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Hoang, VD., Tran, DP., Nhu, N.G., Pham, TA., Pham, VH. (2020). Deep Feature Extraction for Panoramic Image Stitching. In: Nguyen, N., Jearanaitanakij, K., Selamat, A., Trawiński, B., Chittayasothorn, S. (eds) Intelligent Information and Database Systems. ACIIDS 2020. Lecture Notes in Computer Science(), vol 12034. Springer, Cham. https://doi.org/10.1007/978-3-030-42058-1_12
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DOI: https://doi.org/10.1007/978-3-030-42058-1_12
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