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RGB-D image-based Object Detection: From traditional methods to deep learning techniques

Ward, I.R., Laga, H.ORCID: 0000-0002-4758-7510 and Bennamoun, M. (2019) RGB-D image-based Object Detection: From traditional methods to deep learning techniques. In: Rosin, P., Lai, Y.K., Shao, L. and Lui, Y., (eds.) RGB-D Image Analysis and Processing. Springer International Publishing, pp. 169-201.

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Object detection from RGB images is a long-standing problem in image processing and computer vision. It has applications in various domains including robotics, surveillance, human-computer interaction, and medical diagnosis. With the availability of low cost 3D scanners, a large number of RGB-D object detection approaches have been proposed in the past years. This chapter provides a comprehensive survey of the recent developments in this field. We structure the chapter into two parts; the focus of the first part is on techniques that are based on hand-crafted features combined with machine learning algorithms. The focus of the second part is on the more recent work, which is based on deep learning. Deep learning techniques, coupled with the availability of large training datasets, have now revolutionized the field of computer vision, including RGB-D object detection, achieving an unprecedented level of performance. We survey the key contributions, summarize the most commonly used pipelines, discuss their benefits and limitations, and highlight some important directions for future research.

Item Type: Book Chapter
Murdoch Affiliation(s): Information Technology, Mathematics and Statistics
Publisher: Springer International Publishing
Copyright: © 2019 Springer Nature Switzerland AG
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