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020 _a9783030286033
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024 7 _a10.1007/978-3-030-28603-3
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245 1 0 _aRGB-D Image Analysis and Processing
_h[electronic resource] /
_cedited by Paul L. Rosin, Yu-Kun Lai, Ling Shao, Yonghuai Liu.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aXI, 524 p. 178 illus., 152 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
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490 1 _aAdvances in Computer Vision and Pattern Recognition,
_x2191-6594
505 0 _aPart I RGB-D Data Acquisition and Processing -- RGB-D Sensors: Data Acquisition -- Dealing with Missing Depth: Recent Advances in Depth Image Completion and Estimation -- Depth Super-resolution With Color Guidance: A Review -- RGB-D Sensors Data Quality Assessment and Improvement for Advanced Applications -- 3D Reconstruction from RGB-D Data -- RGB-D Odometry and SLAM -- Enhancing 3D Capture with Multiple Depth Camera Systems: A State-of-the-Art Report -- Part II RGB-D Data Analysis -- RGB-D Image-based Object Detection: from Traditional Methods to Deep Learning Techniques -- RGB-S Salient Object Detection: A Review -- Foreground Detection and segmentation in RGB-D Images -- Instance- and Category-level 6d Object Pose Estimation -- Part III RGB-D Applications -- Semantic RGB-D Perception for Cognitive Service Robots -- RGB-D Sensors and Signal Processing for Fall Detection -- RGB-D Interactive Systems on Serious Games for Motor Rehabilitation Therapy and Therapeutic Measurements -- Real-Time Hand Pose Estimation using Depth Camera.-RGB-D Object Classification for Autonomous Driving Perception -- People Counting in Crowded Environment and Re-identification -- References -- Index .
520 _aThis book focuses on the fundamentals and recent advances in RGB-D imaging as well as covering a range of RGB-D applications. The topics covered include: data acquisition, data quality assessment, filling holes, 3D reconstruction, SLAM, multiple depth camera systems, segmentation, object detection, salience detection, pose estimation, geometric modelling, fall detection, autonomous driving, motor rehabilitation therapy, people counting and cognitive service robots. The availability of cheap RGB-D sensors has led to an explosion over the last five years in the capture and application of colour plus depth data. The addition of depth data to regular RGB images vastly increases the range of applications, and has resulted in a demand for robust and real-time processing of RGB-D data. There remain many technical challenges, and RGB-D image processing is an ongoing research area. This book covers the full state of the art, and consists of a series of chapters by internationally renowned experts in the field. Each chapter is written so as to provide a detailed overview of that topic. RGB-D Image Analysis and Processing will enable both students and professional developers alike to quickly get up to speed with contemporary techniques, and apply RGB-D imaging in their own projects.
650 0 _aComputer vision.
650 0 _aUser interfaces (Computer systems).
650 0 _aHuman-computer interaction.
650 0 _aArtificial intelligence.
650 1 4 _aComputer Vision.
650 2 4 _aUser Interfaces and Human Computer Interaction.
650 2 4 _aArtificial Intelligence.
700 1 _aRosin, Paul L.
_eeditor.
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_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aLai, Yu-Kun.
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_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aShao, Ling.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aLiu, Yonghuai.
_eeditor.
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710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
_z9783030286040
776 0 8 _iPrinted edition:
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830 0 _aAdvances in Computer Vision and Pattern Recognition,
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856 4 0 _uhttps://doi.org/10.1007/978-3-030-28603-3
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