Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/154694
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dc.contributor.authorWo, Benjamin Shun Xianen_US
dc.date.accessioned2022-01-06T05:01:50Z-
dc.date.available2022-01-06T05:01:50Z-
dc.date.issued2021-
dc.identifier.citationWo, B. S. X. (2021). Deep machine learning based scene understanding. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/154694en_US
dc.identifier.urihttps://hdl.handle.net/10356/154694-
dc.description.abstractBenjamin, [4/1/2022 1:01 PM] Traditionally, the use of Artificial Intelligence is only reserved for complex industrial applications and is mostly used in niche markets. The development of Artificial Intelligence for niche applications was also expensive and time consuming. However, in the age of Artificial Intelligence, Machine Learning and the Internet of Things, such development costs have been lowered greatly and has become accessible to the masses. With the growing need for “smart” devices and applications, the field of Artificial Intelligence has also grown in popularity in recent years. The potential for Artificial Intelligence has yet to be fully tapped on, as researchers all around the world constantly work towards improving models and creating novel neural networks. Additionally, with more large corporations pushing for cutting-edge technology for commercial products, the innovation for Artificial Intelligence and Machine Learning will continue to propagate. One major field within the umbrella of Artificial Intelligence is Computer Vision, as it plays a crucial role in many applications such as self-driving transport, character recognition and facial recognition. However, Computer Vision requires intelligence to increase its effectiveness in many applications. Hence, Scene Understanding is a vital partner to Computer Vision. The combination of “Vision” and “Understanding” is a key contributor to the power of Computer Vision.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.relationA3315-202en_US
dc.subjectEngineering::Computer science and engineering::Computing methodologies::Artificial intelligenceen_US
dc.titleDeep machine learning based scene understandingen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorJiang Xudongen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Electrical and Electronic Engineering)en_US
dc.contributor.supervisoremailEXDJiang@ntu.edu.sgen_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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