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Abstract
With the progress in the field of computer vision, we are moving closer and closer towards the ultimate aim of human like vision for machines. Scene understanding is an essential part of this research. It seeks the goal that any image should be as understandable and decipherable for computers as it is for humans. The stall in the progress of the different components of scene understanding, due to the limitations of the traditional algorithms, has now been broken by the induction of neural networks for computer vision tasks. The advancements in parallel computational hardware has made it possible to train very deep and complex neural network architectures. This has vastly improved the performances of algorithms for all the different components of scene understanding. This chapter analyses these contributions of deep learning and also presents the advancements of high level scene understanding tasks, such as caption generation for images. It also sheds light on the need to combine these individual components into an integrated system.
Original language | English |
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Title of host publication | Handbook of deep learning applications |
Editors | Valentina Emilia Balas, Sanjiban Sekhar Roy, Dharmendra Sharma, Pijush Samui |
Place of Publication | Cham |
Publisher | Springer |
Pages | 21-51 |
Number of pages | 31 |
ISBN (Electronic) | 9783030114794 |
ISBN (Print) | 9783030114787 |
DOIs | |
Publication status | Published - 19 Mar 2019 |
Publication series
Name | Smart Innovation, Systems and Technologies |
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Volume | 136 |
ISSN (Print) | 2190-3018 |
ISSN (Electronic) | 2190-3026 |
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Dive into the research topics of 'Deep learning for scene understanding'. Together they form a unique fingerprint.Projects
- 1 Finished
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Advanced 3D Computer Vision Algorithms for 'Find and Grasp' Future Robots
Bennamoun, M. (Investigator 01)
ARC Australian Research Council
1/01/15 → 31/12/20
Project: Research