A fast and robust circle detection method using perpendicular bisector of chords

Luo, Liangshun; Xu, De; Zhang, Zhengtao; Zhang, Juan; Qu, Wentai

doi:10.1109/ccdc.2013.6561432

Cited by 7 publications

(5 citation statements)

References 4 publications

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“…Finally, the identified boundaries of micro-nanoparticles are determined through circle Hough transform (CHT). In general, there are several circle detection methods, including the perpendicular bisector method using the bisectors of chords [ 31 ], the least-squares method [ 32 ], the genetic algorithm [ 33 ], and CHT [ 34 ]. Among them, CHT is the most widely employed method in automatic particle size estimation.…”

Section: Methodsmentioning

confidence: 99%

Cooling Performance Prediction of Particle-Based Radiative Cooling Film Considering Particle Size Distribution

Lim,

Jung,

Rho

et al. 2024

Micromachines

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Here, we present a novel protocol concept for quantifying the cooling performance of particle-based radiative cooling (PBRC). PBRC, known for its high flexibility and scalability, emerges as a promising method for practical applications. The cooling power, one of the cooling performance indexes, is the typical quantitative performance index, representing its cooling capability at the surface. One of the primary obstacles to predicting cooling power is the difficulty of simulating the non-uniform size and shape of micro-nanoparticles in the PBRC film. The present work aims to develop an accurate protocol for predicting the cooling power of PBRC film using image processing and regression analysis techniques. Specifically, the protocol considers the particle size distribution through circle object detection on SEM images and determines the probability density function based on a chi-square test. To validate the proposed protocol, a PBRC structure with PDMS/Al2O3 micro-nanoparticles is fabricated, and the proposed protocol precisely predicts the measured cooling power with a 7.8% error. Through this validation, the proposed protocol proves its potential and reliability for the design of PBRC.

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Section: Methodsmentioning

confidence: 99%

Cooling Performance Prediction of Particle-Based Radiative Cooling Film Considering Particle Size Distribution

Lim,

Jung,

Rho

et al. 2024

Micromachines

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“…We use the Kalman filter to detect a curve lane in the far-field of view. This Kalman filter algorithm includes two different methods, the first method is based on the parabola model [23], and the second method is based on the circle model [24]. This method shown in Figure 2 can estimate parameters of the road turning and find geometric shapes based on the mathematical model and the Kalman filter.…”

Section: Methodsmentioning

confidence: 99%

“…For the curve line, the second-best fit equation is the circle equation, shown in Figure 7. In this part, we consider curve line detection algorithms [24] based on the Kalman filter and circle equation. From the circle equation r 2 = (x − h) 2 + (y − k) 2 we need to define circle radius r and the center of circle (h, k).…”

Section: Curve Lane Detection Based On Kalman Filter and Circle Equationmentioning

confidence: 99%

“…For the line same calculation runs to estimate the equation of line, expressed in form of equation 22and (23). Based on L 1 , L 2 lines we can calculate the center of the circle expressed in Equations (24) and (25). The intersection of these two lines indicates the center of the circle.…”

Section: Curve Lane Detection Based On Kalman Filter and Circle Equationmentioning

confidence: 99%

“…Also, we use two different models for a curve lane in the Kalman filter. One is the parabola model [23], another is the circle model [24]. The Kalman-based linear parabolic lane detection is already tested on consecutive video frames using the parabolic model by K.H.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Highly Curved Lane Detection Algorithms Based on Kalman Filter

2020

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The purpose of the self-driving car is to minimize the number casualties of traffic accidents. One of the effects of traffic accidents is an improper speed of a car, especially at the road turn. If we can make the anticipation of the road turn, it is possible to avoid traffic accidents. This paper presents a cutting edge curve lane detection algorithm based on the Kalman filter for the self-driving car. It uses parabola equation and circle equation models inside the Kalman filter to estimate parameters of a using curve lane. The proposed algorithm was tested with a self-driving vehicle. Experiment results show that the curve lane detection algorithm has a high success rate. The paper also presents simulation results of the autonomous vehicle with the feature to control steering and speed using the results of the full curve lane detection algorithm.

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