The Research of Real-Time Image Acquisition and Sobel Edge Detection with FPGA

2011 ◽  
Vol 467-469 ◽  
pp. 703-708
Author(s):  
Yang Xu ◽  
Ping Li ◽  
Jian Jun Yuan ◽  
Min Xiang
Keyword(s):  
Edge Detection ◽  
Real Time ◽  
High Speed ◽  
Design Method ◽  
Image Acquisition ◽  
Detection Algorithm ◽  
The Real ◽  
Real Time Image ◽  
Sobel Edge Detection ◽  
Time Image

As the real-time image acquiring and processing need to be dealt with high speed, a image acquisition and preprocessing system is discussed in this paper. It is built on FPGA( field programmable gate array ) with pipelined and parallel technology. The configurable macro function modules provided by Altera company achieve the Sobel edge detection algorithm. The real-time display the image after edge detection works properly and The new design method shorten the development cycle.

scholarly journals Kinect-Based Real-Time Acquisition Algorithm of Crop Growth Depth Images

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Shan Hua ◽  
Minjie Xu ◽  
Zhifu Xu ◽  
Hongbao Ye ◽  
Cheng quan Zhou
Keyword(s):  
Real Time ◽  
Image Acquisition ◽  
Depth Map ◽  
Crop Growth ◽  
Image Generation ◽  
The Real ◽  
Real Time Image ◽  
Time Image ◽  
Time Acquisition ◽  
Generation Technology

Kinect 3D sensing real-time acquisition algorithm that can meet the requirements of fast, accurate, and real-time acquisition of image information of crop growth laws has become the trend and necessary means of digital agricultural production management. Based on this, this paper uses Kinect real-time image generation technology to try to monitor and study the depth map of crop growth law in real time, use Kinect to obtain the algorithm of crop growth depth map, and conduct investigation and research. Real-time image acquisition research on crop growth trends provides a basis for in-depth understanding of the application of Kinect real-time image generation technology in research. Kinect image real-time acquisition algorithm is a very important information carrier in agricultural information engineering. The research results show that the real-time Kinect depth image acquisition algorithm can obtain good 3D image data information and can provide valuable data basis for the 3D reconstruction of the later crop growth model, growth status analysis, and real-time monitoring of crop diseases. The data shows that, using Kinect, the real-time feedback speed of crop growth observation can be increased by 45%, the imaging accuracy is improved by 37%, and the related operation steps are simplified by 30%. The survey results show that the crop yield can be increased by about 12%.

Verification Real-Time Image Acquisition System (VERITAS)

2014 ◽  
Vol 90 (1) ◽  
pp. S892-S893
Author(s):  
J. Rottmann ◽  
D. Kozono ◽  
R. Mak ◽  
A. Chen ◽  
F.L. Hacker ◽  
...  
Keyword(s):  
Real Time ◽  
Image Acquisition ◽  
Acquisition System ◽  
Real Time Image ◽  
Image Acquisition System ◽  
Time Image

Catching the Right Bus, Part VI: Some Options for Real-Time Image Acquisition

1995 ◽  
Vol 9 (5) ◽  
pp. 496
Author(s):  
Gary Forrest ◽  
Scott McKinney ◽  
James R. Matey
Keyword(s):  
Real Time ◽  
Image Acquisition ◽  
Real Time Image ◽  
The Right ◽  
Time Image

Real-Time Image Mosaicing System Using a High-Frame-Rate Video Sequence

2015 ◽  
Vol 27 (1) ◽  
pp. 12-23 ◽  
Author(s):  
Qingyi Gu ◽  
◽  
Sushil Raut ◽  
Ken-ichi Okumura ◽  
Tadayoshi Aoyama ◽  
...  
Keyword(s):  
Real Time ◽  
High Speed ◽  
Video Sequence ◽  
Frame Rate ◽  
Image Mosaicing ◽  
Current Frame ◽  
High Frame Rate ◽  
Harris Corner ◽  
Real Time Image ◽  
Time Image

<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00270001/02.jpg"" width=""300"" />Synthesized panoramic images</div> In this paper, we propose a real-time image mosaicing system that uses a high-frame-rate video sequence. Our proposed system can mosaic 512 × 512 color images captured at 500 fps as a single synthesized panoramic image in real time by stitching the images based on their estimated frame-to-frame changes in displacement and orientation. In the system, feature point extraction is accelerated by implementing a parallel processing circuit module for Harris corner detection, and hundreds of selected feature points in the current frame can be simultaneously corresponded with those in their neighbor ranges in the previous frame, assuming that frame-to-frame image displacement becomes smaller in high-speed vision. The efficacy of our system for improved feature-based real-time image mosaicing at 500 fps was verified by implementing it on a field-programmable gate array (FPGA)-based high-speed vision platform and conducting several experiments: (1) capturing an indoor scene using a camera mounted on a fast-moving two-degrees-of-freedom active vision, (2) capturing an outdoor scene using a hand-held camera that was rapidly moved in a periodic fashion by hand. </span>

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