Learning Colours from Textures by Effective Representation of Images

Li, J; Chen, Z; Ma, Z

Learning Colours from Textures by Effective Representation of Images

Li, J

Chen, Z Ma, Z

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Publisher:: International Frequency Sensor Association (IFSA) Publishing
Publication Type:: Chapter
Citation:: Advances in Signal Processing: Reviews, 2018, 1 pp. 277 - 304
Issue Date:: 2018-01-01

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Field	Value	Language
dc.contributor.author	Li, J https://orcid.org/0000-0002-1336-2241	en_US
dc.contributor.author	Chen, Z	en_US
dc.contributor.author	Ma, Z	en_US
dc.contributor.editor	Yurish, SY	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	Advances in Signal Processing: Reviews, 2018, 1 pp. 277 - 304	en_US
dc.identifier.isbn	978-84-09-04329-3	en_US
dc.identifier.uri	http://hdl.handle.net/10453/124621
dc.description.abstract	Arguably the majority of existing image and video analytics are done based on the texture. However, the other important aspect, colours, must also be considered for comprehensive analytics. Colours do not only make images feel more vivid to viewers, they also contains important visual clues of the image [20, 54, 24]. Although a modern point-and-shoot digital camera can easily capture colour images, there are circumstances where we need to recover the chromatic information in an image. For example, photography in the old days was monochrome and provided only gray-scale images. Adding colours can rejuvenate these old pictures and make them more adorable as personal memoir or more accessible as archival documents for public or educational purposes. For a colour image, re-coloursation may be necessary if the white balance was poorly set when shooting the picture. In this case, a particular colour channel can be severely over- or under- exposure, and makes infeasible to adjust the white balance based on the recorded colours. A possible rescue of the picture is to keep only the luminance and re-colourise the image. Another example of the application of colourisation arises from the area of specialised imaging, where the sensors capture signals that are out of the visible spectrum of light, e.g. X-ray, MRI, near infrared images. Pseudo colours for these images make them more readily for interpretation by human experts, and can also indicate potentially interesting regions.	en_US
dc.publisher	International Frequency Sensor Association (IFSA) Publishing	en_US
dc.relation.ispartof	Advances in Signal Processing: Reviews	en_US
dc.title	Learning Colours from Textures by Effective Representation of Images	en_US
dc.type	Chapter
utslib.citation.volume	1	en_US
utslib.location	Spain	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.place-of-publication	Spain	en_US
pubs.publication-status	Published	en_US
pubs.volume	1	en_US

Abstract:

Arguably the majority of existing image and video analytics are done based on the texture. However, the other important aspect, colours, must also be considered for comprehensive analytics. Colours do not only make images feel more vivid to viewers, they also contains important visual clues of the image [20, 54, 24]. Although a modern point-and-shoot digital camera can easily capture colour images, there are circumstances where we need to recover the chromatic information in an image. For example, photography in the old days was monochrome and provided only gray-scale images. Adding colours can rejuvenate these old pictures and make them more adorable as personal memoir or more accessible as archival documents for public or educational purposes. For a colour image, re-coloursation may be necessary if the white balance was poorly set when shooting the picture. In this case, a particular colour channel can be severely over- or under- exposure, and makes infeasible to adjust the white balance based on the recorded colours. A possible rescue of the picture is to keep only the luminance and re-colourise the image. Another example of the application of colourisation arises from the area of specialised imaging, where the sensors capture signals that are out of the visible spectrum of light, e.g. X-ray, MRI, near infrared images. Pseudo colours for these images make them more readily for interpretation by human experts, and can also indicate potentially interesting regions.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/124621