OpenCV based road sign recognition on Zynq

Russell, Matthew; Fischaber, Scott

doi:10.1109/indin.2013.6622951

Cited by 24 publications

(10 citation statements)

References 12 publications

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“…4, noise removal image). The filtered image was the input for circle detection using the Hough transform [21][22][23] . The advantage of the Hough transform is that after detecting a circle, the information about its location and radius is known [ Fig.…”

Section: Particle Size Measurementmentioning

confidence: 99%

Shrinkage characteristics of a novel lower contractile acrylic pattern resin

Hori

Sekine

et al. 2019

Dent. Mater. J.

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Forming models and brazing parts, both of which require high accuracy, are greatly affected the polymerization shrinkage of pattern resin. In 2018, a lower-shrinkage autopolymerizing pattern resin (PRK) was introduced. In this work, we compared the rate of polymerization shrinkage between PRK with that of three autopolymerizing resins -GC Pattern Resin (GPR), Pi-Ku Plast (PIK), and Fixpeed (FIX)-as controls. The shrinkage percentages at 10 min were 7.26±0.88 for PRK, 10.78±2.28 for GPR, 8.03±1.08% for PIK, and 7.46±1.25 for FIX. The shrinkage of PRK was significantly lower than that of GPR. The lower-shrinkage autopolymerizing resin contains some multifunctional monomer and indicated that the amount of monomer was accordingly reduced from the result of polymer size and abundance ratio. Our results suggested that the monomer component and the polymer particle size were factors that contribute to reducing contraction of the resins.

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Section: Particle Size Measurementmentioning

confidence: 99%

Shrinkage characteristics of a novel lower contractile acrylic pattern resin

Hori

Sekine

et al. 2019

Dent. Mater. J.

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“…Regarding the specific Italian context in which this work is concerned, there are noticeable results by Lombardi et al [24]. A study comparing various visual descriptors is present in Russell and Fischaber [25] while Ding et al [26] develop a system for detection and identification using the SURF (speeded-up robust features) [27] algorithm and GPGPU (general-purpose gpu) programming model. In Lin et al [28], there is a hybrid approach based on adaptive image pre-processing models and two fuzzy inference schemes checking light illumination and the red color amount of a frame image.…”

Section: Related Workmentioning

confidence: 99%

Detection and Validation of Tow-Away Road Sign Licenses through Deep Learning Methods

Balducci

Impedovo

Pirlo

2018

Sensors

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This work presents the practical design of a system that faces the problem of identification and validation of private no-parking road signs. This issue is very important for the public city administrations since many people, after receiving a code that identifies the signal at the entrance of their private car garage as valid, forget to renew the code validity through the payment of a city tax, causing large money shortages to the public administration. The goal of the system is twice since, after recognition of the official road sign pattern, its validity must be controlled by extracting the code put in a specific sub-region inside it. Despite a lot of work on the road signs’ topic having been carried out, a complete benchmark dataset also considering the particular setting of the Italian law is today not available for comparison, thus the second goal of this work is to provide experimental results that exploit machine learning and deep learning techniques that can be satisfactorily used in industrial applications.

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“…Some of the current image analysis systems use machine learning, others search for objects seen in pictures in a pre-made database [3]. During software development, human nervous system and brain processing ability are often taken into consideration [4].…”

Section: Planning and Principle Of Operationmentioning

confidence: 99%

The Implementation of an Opencv-Based Traffic Sign Identifier Videoanalyst Software

Balázs

Szilágyi

Apagyi

et al. 2018

Műszaki Tudományos Közlemények

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Nowdays, accidents tend to happen because our attention is being split up by the ever-growing influx of information, losing the focus from the driving, traffic signs, and other signals. The consequences of these minor or major accidents weight down on our shoulders. During our project, we tried to eliminate, or help this issue, using present technology, improving upon that, trying to avoid these accidents. Our task consisted on implementing a software, that could identify traffic signs from any video streams.

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OpenCV based road sign recognition on Zynq

Cited by 24 publications

References 12 publications

Shrinkage characteristics of a novel lower contractile acrylic pattern resin

Shrinkage characteristics of a novel lower contractile acrylic pattern resin

Detection and Validation of Tow-Away Road Sign Licenses through Deep Learning Methods

The Implementation of an Opencv-Based Traffic Sign Identifier Videoanalyst Software

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