@inproceedings{Van_Huy_Pham_70119965,
	title = {Deep {Feature} {Extraction} for {Panoramic} {Image} {Stitching}},
	volume = {12034 LNAI},
	isbn = {978-3-030-42057-4},
	url = {http://doi.org/10.1007/978-3-030-42058-1%5C_12},
	doi = {10.1007/978-3-030-42058-1_12},
	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.},
	booktitle = {Lecture {Notes} in {Computer} {Science} (including subseries {Lecture} {Notes} in {Artificial} {Intelligence} and {Lecture} {Notes} in {Bioinformatics})},
	author = {Hoang, Van Dung and Tran, Diem Phuc and Nhu, Nguyen Gia and Pham, The Anh and Pham, Van Huy},
	year = {2020},
	note = {ISSN: 16113349},
	keywords = {Deep learning, Feature extraction, Feature matching, Image stitching},
	pages = {141--151},
}

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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.","booktitle":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","author":[{"propositions":[],"lastnames":["Hoang"],"firstnames":["Van","Dung"],"suffixes":[]},{"propositions":[],"lastnames":["Tran"],"firstnames":["Diem","Phuc"],"suffixes":[]},{"propositions":[],"lastnames":["Nhu"],"firstnames":["Nguyen","Gia"],"suffixes":[]},{"propositions":[],"lastnames":["Pham"],"firstnames":["The","Anh"],"suffixes":[]},{"propositions":[],"lastnames":["Pham"],"firstnames":["Van","Huy"],"suffixes":[]}],"year":"2020","note":"ISSN: 16113349","keywords":"Deep learning, Feature extraction, Feature matching, Image stitching","pages":"141–151","bibtex":"@inproceedings{Van_Huy_Pham_70119965,\n\ttitle = {Deep {Feature} {Extraction} for {Panoramic} {Image} {Stitching}},\n\tvolume = {12034 LNAI},\n\tisbn = {978-3-030-42057-4},\n\turl = {http://doi.org/10.1007/978-3-030-42058-1%5C_12},\n\tdoi = {10.1007/978-3-030-42058-1_12},\n\tabstract = {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.},\n\tbooktitle = {Lecture {Notes} in {Computer} {Science} (including subseries {Lecture} {Notes} in {Artificial} {Intelligence} and {Lecture} {Notes} in {Bioinformatics})},\n\tauthor = {Hoang, Van Dung and Tran, Diem Phuc and Nhu, Nguyen Gia and Pham, The Anh and Pham, Van Huy},\n\tyear = {2020},\n\tnote = {ISSN: 16113349},\n\tkeywords = {Deep learning, Feature extraction, Feature matching, Image stitching},\n\tpages = {141--151},\n}\n\n","author_short":["Hoang, V. D.","Tran, D. P.","Nhu, N. G.","Pham, T. A.","Pham, V. 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