Edge-detection method using binary morphology on hexagonal images

Mostafa, Kazi; Chiang, John Y.; Her, Innchyn

doi:10.1179/1743131x14y.0000000098

Cited by 15 publications

(6 citation statements)

References 8 publications

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“…Commonly used edge detection algorithms include global extraction methods based on energy minimisation criterion (such as fuzzy theory and neural networks) and edge derivative methods that use differential operators (such as Canny, Prewitt, LOG and Robert operators). However, the traditional single-edge detection algorithm has many problems, including a too-wide detection range, poor antinoise interference and prolonged calculation time [40,41,42]. Meeting the target requirements of lane detection is difficult.…”

Section: Edge Detection and Inverse Perspective Transformationmentioning

confidence: 99%

Lane Detection Algorithm for Intelligent Vehicles in Complex Road Conditions and Dynamic Environments

Cao

Song

et al. 2019

Sensors

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Lane detection is an important foundation in the development of intelligent vehicles. To address problems such as low detection accuracy of traditional methods and poor real-time performance of deep learning-based methodologies, a lane detection algorithm for intelligent vehicles in complex road conditions and dynamic environments was proposed. Firstly, converting the distorted image and using the superposition threshold algorithm for edge detection, an aerial view of the lane was obtained via region of interest extraction and inverse perspective transformation. Secondly, the random sample consensus algorithm was adopted to fit the curves of lane lines based on the third-order B-spline curve model, and fitting evaluation and curvature radius calculation were then carried out on the curve. Lastly, by using the road driving video under complex road conditions and the Tusimple dataset, simulation test experiments for lane detection algorithm were performed. The experimental results show that the average detection accuracy based on road driving video reached 98.49%, and the average processing time reached 21.5 ms. The average detection accuracy based on the Tusimple dataset reached 98.42%, and the average processing time reached 22.2 ms. Compared with traditional methods and deep learning-based methodologies, this lane detection algorithm had excellent accuracy and real-time performance, a high detection efficiency and a strong anti-interference ability. The accurate recognition rate and average processing time were significantly improved. The proposed algorithm is crucial in promoting the technological level of intelligent vehicle driving assistance and conducive to the further improvement of the driving safety of intelligent vehicles.

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Section: Edge Detection and Inverse Perspective Transformationmentioning

confidence: 99%

Lane Detection Algorithm for Intelligent Vehicles in Complex Road Conditions and Dynamic Environments

Cao

Song

et al. 2019

Sensors

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“…Among them, the focus of the global discrete grid system based on polyhedrons is the grid cell structure, which could be triangles, quadrilaterals, or hexagons. Different grid cell structures have their own advantages and disadvantages, which have a certain impact on further geospatial operations [9,10]. Compared with triangular and quadrilateral grids, hexagonal grids have the same topological relationship, which is conducive to achieving the highest spatial sampling efficiency of spatial analysis functions such as proximity and connectivity, which is helpful for data visualization.…”

Section: Introductionmentioning

confidence: 99%

A Precision Evaluation Index System for Remote Sensing Data Sampling Based on Hexagonal Discrete Grids

Yao

et al. 2021

IJGI

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With the rapid development of earth observation, satellite navigation, mobile communication, and other technologies, the order of magnitude of the spatial data we acquire and accumulate is increasing, and higher requirements are put forward for the application and storage of spatial data. As a new form of data management, the global discrete grid can be used for the efficient storage and application of large-scale global spatial data, which is a digital multiresolution georeference model that helps to establish a new model of data association and fusion. It is expected to make up for the shortcomings in the organization, processing, and application of current spatial data. There are different types of grid systems according to the grid division form, including global discrete grids with equal latitude and longitude, global discrete grids with variable latitude and longitude, and global discrete grids based on regular polyhedrons. However, there is no accuracy evaluation index system for remote sensing images expressed on the global discrete grid to solve this problem. This paper is dedicated to finding a suitable way to express remote sensing data on discrete grids, as well as establishing a suitable accuracy evaluation system for modeling remote sensing data based on hexagonal grids to evaluate modeling accuracy. The results show that this accuracy evaluation method can evaluate and analyze remote sensing data based on hexagonal grids from multiple levels, and the comprehensive similarity coefficient of the images before and after conversion is greater than 98%, which further proves the availability of the hexagonal-grid-based remote sensing data of remote sensing images. This evaluation method is generally applicable to all raster remote sensing images based on hexagonal grids, and it can be used to evaluate the availability of hexagonal grid images.

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“…Hexagonal grids have enjoyed renewed interest these last couple of years (Middleton and Sivaswamy (2005)) with, in particular applications in 3D printing filling with honeycomb structures (Compton and Lewis (2014), Gibson et al (2014)), networks (Kayaturan and Vernitski (2016), Stojmenovic (1995)), image processing (Grigoryan and Agaian (2016), Karkishchenko and Mnukhin (2017), Mostafa and Her (2016)), computer vision (He and Jia (2005), Veni and Narayanankutty (2014)), etc. Our motivation for lossless rotations in hexagonal grids came from recent work by K. al.…”

Section: Introductionmentioning

confidence: 99%

Shear Based Bijective Digital Rotation in Hexagonal Grids

Andrès

Largeteau-Skapin

Zrour

2021

Lecture Notes in Computer Science

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In this paper, a new bijective digital rotation algorithm for the hexagonal grid is proposed. The method is based on an original decomposition of rotations into shear transforms. It works for any angle with an hexagonal centroid as rotation center and is easily invertible. The algorithm achieves an average distance between the digital rotated point and the continuous rotated point of about 0.42 (for 1.0 the distance between two neighboring hexagons).

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Edge-detection method using binary morphology on hexagonal images

Cited by 15 publications

References 8 publications

Lane Detection Algorithm for Intelligent Vehicles in Complex Road Conditions and Dynamic Environments

Lane Detection Algorithm for Intelligent Vehicles in Complex Road Conditions and Dynamic Environments

A Precision Evaluation Index System for Remote Sensing Data Sampling Based on Hexagonal Discrete Grids

Shear Based Bijective Digital Rotation in Hexagonal Grids

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