A Review of the Edge Detection Technology

Hou, Shouming; Jia, Chao-Lan; Wang, Ya-Bing; Brown, Mackenzie

doi:10.55011/staiqc.2021.1203

Cited by 19 publications

(5 citation statements)

References 61 publications

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“…Recent research has explored the use of DL methods for both edge extraction (see review [24]), and end-to-end background removal (see review [25]). However, many of these methods as they are currently implemented are either inaccurate such as extracting only bounding boxes [26] or are developed for specific applications and as such would not generalise well across any possible measurement artefact [27].…”

Section: Discussionmentioning

confidence: 99%

Autonomous image background removal for accurate and efficient close-range photogrammetry

Eastwood

Leach

Piano

2022

Meas. Sci. Technol.

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Close-range photogrammetry can be used to reconstruct dense point clouds of an object with very high surface coverage, making it useful for manufacturing metrology tasks such as part inspection and validation. However, compared to competing techniques, data processing times can be slow. In this paper we present a method to autonomously remove the background from the images within a photogrammetric dataset. We show that using masked images directly in the reconstruction results in much lower data processing times, with lower memory utilisation. Furthermore, we show that the point density on the object surface is increased while the number of superfluous background points is reduced. Finally, a set of reconstruction results are compared to a set of tactile coordinate measurements. Reconstructions with the background removed are shown to have a standard deviation in the point to mesh distance of up to 30 µm lower than if the background is not removed. This improvement in standard deviation is likely due to the static background, relative to the object on the rotation stage, causing triangulation errors when points are detected and matched on this background data. The proposed approach is shown to be robust over several example artefacts and can, therefore, be implemented to improve the measurement efficiency and measurement results of photogrammetry coordinate measurement systems.

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Section: Discussionmentioning

confidence: 99%

Autonomous image background removal for accurate and efficient close-range photogrammetry

Eastwood

Leach

Piano

2022

Meas. Sci. Technol.

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“…Compared with the other two types of methods, pixel-based methods require no complex initialization settings and require less computation (Yang et al , 2022), so they are widely used in practical applications. This study focuses on pixel- and deep learning-based methods, and readers are referred to Hou et al (2021) for other types of methods.…”

Section: Introductionmentioning

confidence: 99%

A satellite component contour extraction method for lightweight space mobile platforms

Zhu

Liang

2023

AEAT

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Purpose Owing to the complex space environment and limited computing resources, traditional and deep learning-based methods cannot complete the task of satellite component contour extraction effectively. To this end, this paper aims to propose a high-quality real-time contour extraction method based on lightweight space mobile platforms. Design/methodology/approach A contour extraction method that combines two edge clues is proposed. First, Canny algorithm is improved to extract preliminary contours without inner edges from the depth images. Subsequently, a new type of edge pixel feature is designed based on surface normal. Finally, surface normal edges are extracted to supplement the integrity of the preliminary contours for contour extraction. Findings Extensive experiments show that this method can achieve a performance comparable to that of deep learning-based methods and can achieve a 36.5 FPS running rate on mobile processors. In addition, it exhibits better robustness under complex scenes. Practical implications The proposed method is expected to promote the deployment process of satellite component contour extraction tasks on lightweight space mobile platforms. Originality/value A pixel feature for edge detection is designed and combined with the improved Canny algorithm to achieve satellite component contour extraction. This study provides a new research idea for contour extraction and instance segmentation research.

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“…Edge identification is used to find objects that are useful for various applications, including biometrics, industrial applications, and medical image processing. Research on edge identification is ongoing and helps with more advanced picture analysis [1] [6].…”

Section: Introductionmentioning

confidence: 99%

“…Jules firstly proposed the concept of edge identification in 1959. Since then, edge identification has been diffusely applied for various practical requests [6].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the best Edge Filters in Image Processing Based on the Color Fabric Texture

Ibrahim

2022

JES

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With the development and complexity of life, the need to improve images appeared, especially when used in field such as industry, which affect the life of citizens, such as the manufacture of fabrics. Precision is required in the production of these fabrics especially when it comes to the colors and patterns of these fabrics. Edge identification is the first step in many digital image-processing applications. Edge identification greatly decreases the data quantity, undesirable filters or unimportant data and provides the important data into the image. This paper presents a practical study to compare different edge detectors to determine which edge detector achieves better results, which in turn reflects the best pattern in the fabric. These detectors are Canny, Roberts, Laplace and Gabor. A database of thirty color JPG images collected from the Internet was arranged and a quality scale was used to compare filter detectors. The system MATLAB2020 was used to program the proposed work. The results enhancement was measured by the quality coefficient. This coefficient estimated as follows for Roberts filter (44.27-51.09); Gabor filter (43.46-44.48); Canny filter (44.46-52.05); and Laplace filter (44.71-5.40). Therefore, it turns out that the Gabor filter is the best of these filters in defining the edges that were used in defining the pattern.

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A Review of the Edge Detection Technology

Cited by 19 publications

References 61 publications

Autonomous image background removal for accurate and efficient close-range photogrammetry

Autonomous image background removal for accurate and efficient close-range photogrammetry

A satellite component contour extraction method for lightweight space mobile platforms

Evaluation of the best Edge Filters in Image Processing Based on the Color Fabric Texture

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