Sub-pixel Edge Detection Based on Curve Fitting

Xu, Guosheng

doi:10.1109/icic.2009.205

Cited by 38 publications

(31 citation statements)

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“…methods reconstructing image intensity function [13] which determine subpixel edge position based on properties of function modeling image intensity at the edge; these methods however are in minority, due to lack of characteristic points of image intensity function at the edge; 2. methods reconstructing image first derivative function [14], [15] which retrieve image gradient function at the edge based on gradient sample values provided by operators like Sobel, Prewitt [14] or Canny [15] -most commonly second order polynomial is fitted into gradient sample values in a small (3 -5 pixels) neighborhood; several approaches using wavelet transform instead of image first derivative have also been proposed [19], [20]; 3. methods reconstructing image second derivative function [16]- [18] which reconstruct continuous image 2nd derivative function at the edge based on sample values provided by operator LoG; most commonly image derivative function is linearly interpolated in the neighborhood where the 2nd image derivative function changes its sign [16], [17] then coordinates of the zerocrossings of the reconstructed derivative function determine edge position with subpixel accuracy.…”

Section: Reconstructive Methodsmentioning

confidence: 99%

Gaussian-Based Approach to Subpixel Detection of Blurred and Unsharp Edges

Fabijańska

2014

Annals of Computer Science and Information Systems

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Abstract-In this paper the problem of edge detection with subpixel accuracy is considered. In particular, the precise detection of significantly blurred edges is regarded. A new method for subpixel edge detection is introduced. The method attempts to reconstruct image gradient function at the edge using the Gaussian function. The results of subpixel edge detection in the artificially created and the real images obtained by the introduced approach are presented and compared with the results of previously proposed methods. In particular, the moment based methods, the gravity center method and the parabola fitting method are considered in the comparison. The presented results prove the robustness of the introduced approach against the averaging and the Gaussian blur. Additionally, the comparison shows, that the introduced approach outperforms the existing state-of-art methods for subpixel edge detection. I. INTRODUCTIONDGE detection is the problem of crucial importance in image processing. Edges define location and geometric features of objects present in the scene. Therefore, in a typical vision system, edge detection is performed during low level processing and provides information for operations performed in the following stages, such as quantitative analysis, target recognition or image coding etc.Recently, the requirements for edge detection accuracy rapidly increase. Satellite remote sensing, telemetry, photogrammetry, medical image analysis, industrial inspection, geometrical measurement and other applications where accuracy is at premium require precision of tenths or even hundredths of pixel.The traditional, well-established methods for edge detection such as gradient operators (Sobel, Prewitt, Roberts), Canny edge detector, operator LoG etc. all belong to pixel level. Therefore, they are mostly insufficient for practical applications of modern machine vision. Due to their low precision of edge location and extracted wider edges, these approaches more and more often have difficulties in meeting the actual accuracy requirements of vision systems. Therefore, the development of subpixel techniques for edge detection has become one of the hotspots of the current This work was not supported by any organization research in image processing. Some work has already been done on this problem. However, the major methods for subpixel edge detection are still to be developed. It should be also underlined that while there has been substantial work performed on the detection of clear and well defined edges at subpixel accuracy, a little has been done on the subpixel edge detection in low contrast images containing blurred, noisy and unsharp edges [1] [2]. This paper presents a new method which is a step forward through introducing subpixel analysis into edge detection. In particular, it considers precise edge detection of significantly blurred edges. As the already proposed methods deal mostly with sharp, regular and well defined edges the introduced approach can be considered like a novelty. This paper is organized as f...

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Section: Reconstructive Methodsmentioning

confidence: 99%

Gaussian-Based Approach to Subpixel Detection of Blurred and Unsharp Edges

Fabijańska

2014

Annals of Computer Science and Information Systems

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“…As an example, the approach by Xu (2009a) can be given. The method approximates image intensity at the edge using second order polynomial.…”

Section: Reconstructive Methodsmentioning

confidence: 99%

A survey of subpixel edge detection methods for images of heat-emitting metal specimens

Fabijańska

2012

International Journal of Applied Mathematics and Computer Science

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In this paper the problem of accurate edge detection in images of heat-emitting specimens of metals is discussed. The images are provided by the computerized system for high temperature measurements of surface properties of metals and alloys. Subpixel edge detection is applied in the system considered in order to improve the accuracy of surface tension determination. A reconstructive method for subpixel edge detection is introduced. The method uses a Gaussian function in order to reconstruct the gradient function in the neighborhood of a coarse edge and to determine its subpixel position. Results of applying the proposed method in the measurement system considered are presented and compared with those obtained using different methods for subpixel edge detection.

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“…The centre extraction methods are aimed at obtaining the centre positions of light stripes. They include the methods of extreme value [13,14], threshold [15][16][17], directional template [18][19][20], grey centroid [21,22], curve fitting [23,24] and Hessian matrix [25][26][27]. Below we discuss in brief these methods and point to their advantages and shortcomings.…”

Section: Introductionmentioning

confidence: 99%

“…The curve-fitting method [23,24] outlines the grey-scale distribution of the transverse section of the light stripe, employing a Gaussian curve or a parabola. The central point of the transverse section is a local maximum of the fitted curve.…”

Section: Introductionmentioning

confidence: 99%

Adaptive centre extraction method for structured light stripes

Hu¹,

Zhu²,

Hu³

et al. 2017

Ukr. J. Phys. Opt.

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Abstract. In the vision systems based on structured light, accurate extracting of centre positions of a light stripe represents one of the key points in solving the whole measurement task. Usually, high radiant illumination and objects with abundant surface textures decrease the detection precision. To address these problems, we suggest an adaptive centre-extraction method. First, it improves the image contrast using a novel adaptive threshold-based power transformation. Second, pixel-level centre points are obtained with our adaptive dual-threshold Canny's edge detection method. Finally, subpixel-level centre points are extracted using a Hessian matrix for a limited number of pixels. Our experiments prove robustness and practicability of the method, which can cope with complex surface textures of projected objects and high radiant illumination.

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Sub-pixel Edge Detection Based on Curve Fitting

Cited by 38 publications

References 0 publications

Gaussian-Based Approach to Subpixel Detection of Blurred and Unsharp Edges

Gaussian-Based Approach to Subpixel Detection of Blurred and Unsharp Edges

A survey of subpixel edge detection methods for images of heat-emitting metal specimens

Adaptive centre extraction method for structured light stripes

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