Details
| Original language | English |
|---|---|
| Title of host publication | Real-Time Image Processing and Deep Learning 2021 |
| Editors | Nasser Kehtarnavaz, Matthias F. Carlsohn |
| Number of pages | 14 |
| Volume | 11736 |
| ISBN (electronic) | 9781510643093 |
| Publication status | Published - 12 Apr 2021 |
| Event | Real-Time Image Processing and Deep Learning 2021 - Virtual, Online Duration: 12 Apr 2021 → 16 Apr 2021 |
Publication series
| Name | Proceedings of SPIE - The International Society for Optical Engineering |
|---|---|
| Volume | 11736 |
| ISSN (Print) | 0277-786X |
| ISSN (electronic) | 1996-756X |
Abstract
Keywords
- Circle Detection, Embedded, Hough-Transform, Image Processing, Optimization, Real-Time, Smartphone
ASJC Scopus subject areas
- Materials Science(all)
- Electronic, Optical and Magnetic Materials
- Physics and Astronomy(all)
- Condensed Matter Physics
- Mathematics(all)
- Applied Mathematics
- Engineering(all)
- Electrical and Electronic Engineering
- Computer Science(all)
- Computer Science Applications
Cite this
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Real-Time Image Processing and Deep Learning 2021. ed. / Nasser Kehtarnavaz; Matthias F. Carlsohn. Vol. 11736 2021. 117360F (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11736).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research
}
TY - GEN
T1 - Real time circle detection by simplified Hough transform on smartphones
AU - Schneider, Viktor J.
N1 - Publisher Copyright: © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
PY - 2021/4/12
Y1 - 2021/4/12
N2 - Real time circle detection requires a considerable amount of computing power, especially with growing image size. This paper presents a modified version of the Hough transform with a dedicated and streamlined pre- processing stage to detect circles in video images in real-time using mid-range performance smartphones. Hough transform for detection of co-circular line pixels requires a 3-dimensional data space instead of 2 dimensions for detection of co-linear pixels. This dimensional complexity and the fact that Hough transform in general requires computational expensive pre-processing, make optimizations for hand-held or embedded systems inevitable. Multiple modifications for tuning the algorithms by trading mathematical accuracy against processing speed are shown in this paper, which improve the overall computational performance, significantly. Some of these optimizations allow e.g. to replace the edge detection process completely by a simple but smart thresholding and pixel-wise neighbourhood inspection, using pre-calculated lookup tables instead of complex calculations and restricting the Hough space in size and precision. These modifications where implemented and tested on both desktop and mobile devices for comparison but without any support by the GPU. Benchmarks showed that more than 60 FPS on desktops and more than 20 FPS on mobile devices are achievable for processing full HD resolution images, which allows implementations meeting the real time constraints and deadlines specified by a concrete application of an ambulant water quality analysis scenario.
AB - Real time circle detection requires a considerable amount of computing power, especially with growing image size. This paper presents a modified version of the Hough transform with a dedicated and streamlined pre- processing stage to detect circles in video images in real-time using mid-range performance smartphones. Hough transform for detection of co-circular line pixels requires a 3-dimensional data space instead of 2 dimensions for detection of co-linear pixels. This dimensional complexity and the fact that Hough transform in general requires computational expensive pre-processing, make optimizations for hand-held or embedded systems inevitable. Multiple modifications for tuning the algorithms by trading mathematical accuracy against processing speed are shown in this paper, which improve the overall computational performance, significantly. Some of these optimizations allow e.g. to replace the edge detection process completely by a simple but smart thresholding and pixel-wise neighbourhood inspection, using pre-calculated lookup tables instead of complex calculations and restricting the Hough space in size and precision. These modifications where implemented and tested on both desktop and mobile devices for comparison but without any support by the GPU. Benchmarks showed that more than 60 FPS on desktops and more than 20 FPS on mobile devices are achievable for processing full HD resolution images, which allows implementations meeting the real time constraints and deadlines specified by a concrete application of an ambulant water quality analysis scenario.
KW - Circle Detection
KW - Embedded
KW - Hough-Transform
KW - Image Processing
KW - Optimization
KW - Real-Time
KW - Smartphone
UR - http://www.scopus.com/inward/record.url?scp=85109130268&partnerID=8YFLogxK
U2 - 10.1117/12.2588773
DO - 10.1117/12.2588773
M3 - Conference contribution
SN - 978-151064309-3
VL - 11736
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Real-Time Image Processing and Deep Learning 2021
A2 - Kehtarnavaz, Nasser
A2 - Carlsohn, Matthias F.
T2 - Real-Time Image Processing and Deep Learning 2021
Y2 - 12 April 2021 through 16 April 2021
ER -