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Research Article
Projection-Mapping-Based Object Pointing Using a High-Frame-Rate Camera-Projector System
Idaku Ishii
Hiroshima University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-7728-2363
License:
This work is licensed under a CC BY 4.0 License. Read Full License
An informative object pointing method using a spatiotemporal-modulated pattern projection is proposed to recognize and localize pointed objects by using a distantly located high-frame-rate vision system. We developed a prototype for projection-mapping-based object pointing that consists of an AI-camera-enabled projection (AiCP) system used as a transmitter, for informative projection mapping, and an HFR vision system operated as a receiver. The AiCP system detects multiple objects in real time at 30 fps with a CNN-based object detector, and simultaneously encodes and projects the recognition results of the detector as 480-Hz-modulated light patterns on to the objects to be pointed. The multiple 480-fps cameras can directly recognize and track the objects pointed at by the AiCP system without camera calibration or complex recognition methods by decoding the brightness signals of pixels in the images. To demonstrate the e ectiveness of our proposed method, several desktop experiments using miniature objects and scenes were conducted under various conditions.
Keywords
High-Speed Camera-Projector, Informative Projection Mapping, Real-Time Pattern Recognition, Visible Light Communication
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CURRENT STATUS: Under Revision
Version 1
Posted 18 Sep, 2020
No community comments so far
Editorial decision: Major revision
On 20 Nov, 2020
Review #2 received
Received 19 Nov, 2020
Reviewer #2 agreed
On 03 Nov, 2020
Review #1 received
Received 16 Oct, 2020
Reviewer #1 agreed
On 03 Oct, 2020
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Invitations sent on 30 Sep, 2020
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This preprint is under consideration at ROBOMECH Journal. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Projection-Mapping-Based Object Pointing Using a High-Frame-Rate Camera-Projector System
Idaku Ishii
Hiroshima University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-7728-2363
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Revision
Version 1
Posted 18 Sep, 2020
No community comments so far
Editorial decision: Major revision
On 20 Nov, 2020
Review #2 received
Received 19 Nov, 2020
Reviewer #2 agreed
On 03 Nov, 2020
Review #1 received
Received 16 Oct, 2020
Reviewer #1 agreed
On 03 Oct, 2020
Reviewers invited
Invitations sent on 30 Sep, 2020
Editor assigned
On 15 Sep, 2020
First submitted
On 14 Sep, 2020
Submission checks complete
On 14 Sep, 2020
Editor invited
On 14 Sep, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
An informative object pointing method using a spatiotemporal-modulated pattern projection is proposed to recognize and localize pointed objects by using a distantly located high-frame-rate vision system. We developed a prototype for projection-mapping-based object pointing that consists of an AI-camera-enabled projection (AiCP) system used as a transmitter, for informative projection mapping, and an HFR vision system operated as a receiver. The AiCP system detects multiple objects in real time at 30 fps with a CNN-based object detector, and simultaneously encodes and projects the recognition results of the detector as 480-Hz-modulated light patterns on to the objects to be pointed. The multiple 480-fps cameras can directly recognize and track the objects pointed at by the AiCP system without camera calibration or complex recognition methods by decoding the brightness signals of pixels in the images. To demonstrate the e ectiveness of our proposed method, several desktop experiments using miniature objects and scenes were conducted under various conditions.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.