Image Acquisition Planning for Image-based 3D Reconstruction Using a Robotic Arm

2021 ◽  
Author(s):  
Rachel Hyo Son ◽  
Kevin Han
Keyword(s):  
3D Reconstruction ◽  
Image Acquisition ◽  
Robotic Arm ◽  
Acquisition Planning

scholarly journals Robotic Arm Based 3D Reconstruction Test Automation

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 7206-7213 ◽  
Author(s):  
Debdeep Banerjee ◽  
Kevin Yu ◽  
Garima Aggarwal
Keyword(s):  
3D Reconstruction ◽  
Robotic Arm ◽  
Test Automation

scholarly journals Detection of Defective Regions in 3D Reconstruction to Support Image Acquisition

2017 ◽  
Vol 10 (5) ◽  
pp. 402-409
Author(s):  
Seiya ITO ◽  
Naoshi KANEKO ◽  
Takeshi YOSHIDA ◽  
Kazuhiko SUMI
Keyword(s):  
3D Reconstruction ◽  
Image Acquisition

scholarly journals UAV-Based Gigapixel Panoramic Image Acquisition Planning with Ray Casting-Based Overlap Constraints

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Shuhang Zhang ◽  
Chun Liu ◽  
Yuan Zhou
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Low Cost ◽  
Image Acquisition ◽  
Single Camera ◽  
Ray Casting ◽  
Panoramic Image ◽  
Panoramic Imaging ◽  
Aerial Vehicles ◽  
Acquisition Planning ◽  
Large Observation

Panoramic imaging is information-rich, low-cost, and effective. In panoramic image acquisition, unmanned aerial vehicles (UAVs) have a natural advantage that owes to their flexibility and relatively large observation ranges. Using a panoramic gimbal and a single camera may be the most common means of capturing gigapixel panoramas. In order to manage the constraints of UAV power and facilitate the use of a variety of camera lenses, an effective and flexible method for planning UAV gigapixel panorama acquisitions is required. To address this need, a panoramic image acquisition planning method is proposed in this paper. The method defines image overlaps via a ray casting procedure and then generates an acquisition plan according to the constraints of horizontal and vertical overlap thresholds. This method ensures the completeness of the panorama by maintaining the overlap between adjacent images. Two experiments, including simulated and field cases, were performed to evaluate the proposed method through comparisons with an existing panorama acquisition plan. Results showed that the proposed method can capture complete panoramas with fewer images.

Quick (Six seconds) image acquisition for left ventricular 3D reconstruction

1995 ◽  
Vol 8 (3) ◽  
pp. 386
Author(s):  
J Buithieu ◽  
M Belohlavek ◽  
T Behrenbeck ◽  
D.A. Foley ◽  
J.F. Greenleaf ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Image Acquisition ◽  
Left Ventricular

Scanning x-ray fluorescence microscopy and principal component analysis

1992 ◽  
Vol 50 (2) ◽  
pp. 1752-1753
Author(s):  
Brian Cross
Keyword(s):  
Principal Component Analysis ◽  
Fluorescence Microscopy ◽  
Spatial Resolution ◽  
High Speed ◽  
Image Acquisition ◽  
Principal Component ◽  
Fluorescence Microscope ◽  
Acquisition Rate ◽  
X Ray ◽  
Speed Up

A relatively new entry, in the field of microscopy, is the Scanning X-Ray Fluorescence Microscope (SXRFM). Using this type of instrument (e.g. Kevex Omicron X-ray Microprobe), one can obtain multiple elemental x-ray images, from the analysis of materials which show heterogeneity. The SXRFM obtains images by collimating an x-ray beam (e.g. 100 μm diameter), and then scanning the sample with a high-speed x-y stage. To speed up the image acquisition, data is acquired "on-the-fly" by slew-scanning the stage along the x-axis, like a TV or SEM scan. To reduce the overhead from "fly-back," the images can be acquired by bi-directional scanning of the x-axis. This results in very little overhead with the re-positioning of the sample stage. The image acquisition rate is dominated by the x-ray acquisition rate. Therefore, the total x-ray image acquisition rate, using the SXRFM, is very comparable to an SEM. Although the x-ray spatial resolution of the SXRFM is worse than an SEM (say 100 vs. 2 μm), there are several other advantages.

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