3D computer vision based on machine learning with deep neural networks: A review |
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| Authors: | Kailas Vodrahalli Achintya K. Bhowmik |
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| Affiliation: | 1. Department of Electrical Engineering and Computer Science, University of California, Berkeley, California, USA;2. Starkey Hearing Technologies, Berkeley, California, USA |
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| Abstract: | Recent advances in the field of computer vision can be attributed to the emergence of deep learning techniques, in particular convolutional neural networks. Neural networks, partially inspired by the brain's visual cortex, enable a computer to “learn” the most important features of the images it is shown in relation to a specific, specified task. Given sufficient data and time, (deep) convolutional neural networks offer more easily designed, more generalizable, and significantly more accurate end‐to‐end systems than is possible with previously employed computer vision techniques. This review paper seeks to provide an overview of deep learning in the field of computer vision with an emphasis on recent progress in tasks involving 3D visual data. Through a backdrop of the mammalian visual processing system, we hope to also provide inspiration for future advances in automated visual processing. |
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| Keywords: | computer vision artificial intelligence machine learning deep neural networks |
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