Accelerated feature detectors for real-time vision based application
Spatial resolution is a very important quality metric to measure digital images. The higher the resolution of the image, the more image details provided. It is projected to convert the low-resolution(LR) image or video stream to high-resolution(HR) ones before they are displayed/used for analysis or...
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sg-ntu-dr.10356-705552023-03-03T20:32:41Z Accelerated feature detectors for real-time vision based application Biyani, Divesh Lam Siew Kei School of Computer Science and Engineering DRNTU::Engineering::Computer science and engineering Spatial resolution is a very important quality metric to measure digital images. The higher the resolution of the image, the more image details provided. It is projected to convert the low-resolution(LR) image or video stream to high-resolution(HR) ones before they are displayed/used for analysis or pattern extraction. All these requirements are hopeful to be satisfied in an inexpensive manner by using super-resolution(SR) technique. To do this, different algorithms are to be looked at and in the past decade there have been many image processing algorithms. The best algorithm should be identified that suits the purpose. Recently a lot of interpolation algorithms have been proposed, but many of these are highly computationally expensive and so cannot be used for real-time applications. Apart from being computationally expensive, many of these algorithms are also space expensive, that is they take up a lot of memory and cannot completely reside in the RAM. In view of real-time applications, I would like to identify an algorithm that is both computationally inexpensive and memory efficient and use it for Super-Resolution. Bachelor of Engineering (Computer Engineering) 2017-04-27T06:57:23Z 2017-04-27T06:57:23Z 2017 Final Year Project (FYP) http://hdl.handle.net/10356/70555 en Nanyang Technological University 37 p. application/pdf |
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DRNTU::Engineering::Computer science and engineering Biyani, Divesh Accelerated feature detectors for real-time vision based application |
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Spatial resolution is a very important quality metric to measure digital images. The higher the resolution of the image, the more image details provided. It is projected to convert the low-resolution(LR) image or video stream to high-resolution(HR) ones before they are displayed/used for analysis or pattern extraction. All these requirements are hopeful to be satisfied in an inexpensive manner by using super-resolution(SR) technique. To do this, different algorithms are to be looked at and in the past decade there have been many image processing algorithms. The best algorithm should be identified that suits the purpose. Recently a lot of interpolation algorithms have been proposed, but many of these are highly computationally expensive and so cannot be used for real-time applications. Apart from being computationally expensive, many of these algorithms are also space expensive, that is they take up a lot of memory and cannot completely reside in the RAM. In view of real-time applications, I would like to identify an algorithm that is both computationally inexpensive and memory efficient and use it for Super-Resolution. |
author2 |
Lam Siew Kei |
author_facet |
Lam Siew Kei Biyani, Divesh |
format |
Final Year Project |
author |
Biyani, Divesh |
author_sort |
Biyani, Divesh |
title |
Accelerated feature detectors for real-time vision based application |
title_short |
Accelerated feature detectors for real-time vision based application |
title_full |
Accelerated feature detectors for real-time vision based application |
title_fullStr |
Accelerated feature detectors for real-time vision based application |
title_full_unstemmed |
Accelerated feature detectors for real-time vision based application |
title_sort |
accelerated feature detectors for real-time vision based application |
publishDate |
2017 |
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http://hdl.handle.net/10356/70555 |
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1759855828673757184 |