Pregled bibliografske jedinice broj: 1262271
Color image segmentation based on thresholding for advanced driver assistance systems
Color image segmentation based on thresholding for advanced driver assistance systems // 2022 IEEE Zooming Innovaton in Consumer Technologies Conference (ZINC)
Novi Sad, Srbija, 2022. str. 271-277 doi:10.1109/ZINC55034.2022.9840722 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1262271 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Color image segmentation based on thresholding for
advanced driver assistance systems
Autori
Budak, Luka ; Grbić, Ratko ; Četić, Nenad ; Kaštelan, Ivan
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
2022 IEEE Zooming Innovaton in Consumer Technologies Conference (ZINC)
/ - , 2022, 271-277
ISBN
978-1-6654-8374-2
Skup
2022 IEEE Zooming Innovation in Consumer Technologies Conference (ZINC)
Mjesto i datum
Novi Sad, Srbija, 25.05.2022. - 26.05.2022
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
ADAS ; image segmentation ; image thresholding ; VisionSDK ; TDA2xx
Sažetak
Many Advanced Driver Assistance Systems (ADASs) rely on an image obtained by a camera that is mounted on a vehicle. To get useful information in real-time, the acquired image is processed with different computer vision algorithms running on the vehicle's embedded platform. The common preprocessing task is the image segmentation based on color which is often used in lane detection or traffic light/sign recognition algorithms to extract key regions. In this paper, we focus on a color image segmentation based on thresholding. While being very simple, its effectiveness largely depends on the details of the implementation such as the chosen color space or characteristics of the used embedded platform. We provide details regarding PC implementation and ADAS development board implementation as well as the details regarding optimizations that are carried out to achieve smaller execution time on PC and board's Texas Instruments TDA2xx System-On-Chip. The image segmentation mean processing time is reported for three different resolutions and three different color models (RGB, HSV, YUV) for both PC and ADAS development board. The obtained results can help in planning and allocating resources on the vehicle's embedded platform for such computer vision tasks.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika, Računarstvo
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek
Profili:
Ratko Grbić
(autor)