TY - CHAP
T1 - RGB-D Image-Based Object Detection
T2 - From Traditional Methods to Deep Learning Techniques
AU - Ward, Isaac Ronald
AU - Laga, Hamid
AU - Bennamoun, Mohammed
PY - 2019/10/27
Y1 - 2019/10/27
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85074605992&partnerID=8YFLogxK
UR - https://link.springer.com/book/10.1007/978-3-030-28603-3#toc
U2 - 10.1007/978-3-030-28603-3_8
DO - 10.1007/978-3-030-28603-3_8
M3 - Chapter
AN - SCOPUS:85074605992
SN - 9783030286026
T3 - Advances in Computer Vision and Pattern Recognition
SP - 169
EP - 201
BT - RGB-D Image Analysis and Processing
A2 - Rosin, Paul L.
A2 - Lai, Yu-Kun
A2 - Shao, Ling
A2 - Liu, Yonghuai
PB - Springer
CY - Netherlands
ER -