A fast and efficient algorithm for determining the connected orthogonal convex hulls

Linh, Nguyen Kieu; An, Phan Thanh; Hoài, Trần Văn

doi:10.1016/j.amc.2022.127183

Cited by 4 publications

(3 citation statements)

References 26 publications

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“…Addressing the challenges associated with the high complexity of point cloud surface fitting, where computational efficiency and fitting accuracy are difficult to guarantee simultaneously, this paper constructs a 3D surface point cloud's convex hull [19,20] as illustrated in figure 12(d) and calculates the centroid projection of the relay to precisely locate the contact surface between the relay and the ground, thereby determining the gap distance between the relay's lower surface and the ground.…”

Section: Point Cloud Gap Distancementioning

confidence: 99%

Gap measurement method based on projection lines and convex analysis of 3D points cloud

Pan,

Jiang,

Tang

et al. 2024

Meas. Sci. Technol.

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Accurate measurement of the gap between the lower surface of the relay and the ground is critical for ensuring the quality of the finished product. Traditional gap measurement methods have some shortcomings, such as low accuracy, poor robustness, and loss of depth clues in obscured areas. In this study, a novel gap measurement method based on computer vision is proposed, which includes a projection line model based on guided filtering and a 3D surface point cloud model based on a three dimensional plane reference.- The relay gap was measured by calculating the projection lines of the upper and lower surfaces of the gap with an error of ±0.016 mm. A 3D point cloud model captures the key features of the underside of the relay through image processing techniques, and combines convex hull and centroid estimation to construct a three-dimensional reference plane for the gap, which could achieve high-precision, real-time measurement of the gap (with an error less than ±0.0087 mm). The experimental measurement results show that the proposed method is better than the SelfConvNet method, which has a high measurement accuracy and strong anti-interference ability, and an accuracy rate of up to 99.5% in factory relay quality inspection experiments.

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Section: Point Cloud Gap Distancementioning

confidence: 99%

Gap measurement method based on projection lines and convex analysis of 3D points cloud

Pan,

Jiang,

Tang

et al. 2024

Meas. Sci. Technol.

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“…A continuación, se presenta el pseudocódigo de cada uno de ellos. a) Algoritmo QuickHull Este algoritmo fue sugerido por diversos investigadores a finales de los 70s [20], [21], y fue llamado QuickHull por Preparata y Shamos en 1985 [16], citado por [19], [22] y [23]. La complejidad del QuickHull según [20] es del orden O(nh), siendo n el número de puntos y h el número de vértices del CH.…”

Section: ) Cálculo Del Convex Hullunclassified

Implementación de algoritmos para calcular el Convex Hull

et al. 2022

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La geometría computacional es una disciplina enfocada en la resolución de problemas en el ámbito geométrico. En este contexto, el algoritmo para calcular el polígono convexo llamado Convex Hull (CH) es importante, debido a que es la base de muchos otros algoritmos. El objetivo de la investigación fue implementar algoritmos que calculan el CH incorporando modificaciones para reducir el tiempo de ejecución. El trabajo inició con la revisión bibliográfica acerca de geometría computacional y los algoritmos destacados en el cálculo del CH. Posteriormente, se realizó la implementación en JAVA de los algoritmos QuickHull, Gift Wrapping y Graham Scan en sus versiones originales; también se implementaron algunas versiones con modificaciones. Al finalizar la implementación, se ejecutaron pruebas para verificar los tiempos de ejecución. Finalmente, se comprobó que el algoritmo QuickHull es el más rápido entre las implementaciones realizadas en esta investigación. También se nota reducción en los tiempos de ejecución en las implementaciones modificadas con relación a las originales de los algoritmos Gift Wrapping y Graham Scan.

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“…The proposed pipeline is illustrated in figure 3. We use the orthogonally convex hulls generated from the -QUICKHULL algorithm [16] to segment full pothole candidates more precisely. However, a pothole shade candidate can only be transformed into a full pothole using convex hulls, as the orthogonally convex hulls will only capture the shade region.…”

mentioning

confidence: 99%

A modified pothole detection approach to capture their width

Thanh An,

Minh Phuong,

Le Quang

et al. 2024

Phys. Scr.

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Detecting potholes is an ongoing problem that requires extensive investigations as potholes significantly deteriorate urban beauty and can cause serious harm to commuters. Multiple techniques have been developed over the years to address that issue. However, there has been no formal way to define the size, particularly the width, of a pothole. This work presents a mathematical definition for the width of an arbitrary 2D pothole at a point inside. Then, the width of each pothole is calculated by taking the supremum of all width values at all points in a given subset of that pothole. We also extend the detection pipeline by Koch and Brilakis in 2011 to prove the practicality of these definitions. The obtained experimental results show that our modification improves the pothole segmentation. Consequently, it gives a more accurate estimation of pothole widths.

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A fast and efficient algorithm for determining the connected orthogonal convex hulls

Cited by 4 publications

References 26 publications

Gap measurement method based on projection lines and convex analysis of 3D points cloud

Gap measurement method based on projection lines and convex analysis of 3D points cloud

Implementación de algoritmos para calcular el Convex Hull

A modified pothole detection approach to capture their width

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