Subpixel measurements using a moment-based edge operator

Lyvers, E.P.; Mitchell, O. Robert; Akey, Mark L.; Reeves, Anthony P.

doi:10.1109/34.41367

Cited by 299 publications

(122 citation statements)

References 10 publications

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“…Geometric moment approach developed by Lyvers [9] fits moments into 2D model of an ideal edge. This model is described by four parameters which indicate subpixel edge position.…”

Section: B Moment Based 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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“…Geometric moment approach developed by Lyvers [9] fits moments into 2D model of an ideal edge. This model is described by four parameters which indicate subpixel edge position.…”

Section: B Moment Based 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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“…Its uses range from edge detection [23,24,25], background normalization [26], shape [27] and surface topography [28], to image segmentation procedures involving detection of corners, curves, valleys, and ridges [29,30]. The facet model principle is based on the minimization of the error between the image thought of as a piecewise continuous gray level intensity surface and the observed data from the physical scene [31].…”

Section: Facet Modelmentioning

confidence: 99%

<title>Facet model and mathematical morphology for surface characterization</title>

et al. 1999

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This paper describes an algorithm for the automatic segmentation and representation of surface structures and non-uniformities in an industrial setting. The automatic image processing and analysis algorithm is developed as part of a complete on-line web characterization system of a papermaking process at the wet end. The goal is to: (1) link certain types of structures on the surface of the web to known machine parameter values, and (2) find the connection between detected structures at the beginning of the line and defects seen on the final product. Images of the pulp mixture (slurry), carried by a fast moving table, are obtained using a stroboscopic light and a CCD camera. This characterization algorithm succeeded where conventional contrast and edge detection techniques failed due to a poorly controlled environment. The images obtained have poor contrast and contain noise caused by a variety of sources.After a number of enhancement steps, conventional segmentation methods still failed to detect any structures and are consequently discarded. Techniques tried include the Canny edge detector, the Sobel, Roberts, and Prewitt's filters, as well as zero crossings. The facet model algorithm, is then applied to the images with various parameter settings and is found to be successful in detecting the various topographic characteristics of the surface of the slurry. Pertinent topographic elements are retained and a filtered image computed. Carefully tailored morphological operators are then applied to detect and segment regions of interest. Those regions are then selected according to their size, elongation, and orientation. Their bounding rectangles are computed and represented. Also addressed in this paper are aspects of the real time implementation of this algorithm for on-line use. The algorithm is tested on over 500 images of slurry and is found to segment and characterize nonuniformities on all 500 images.

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“…Currently, the sub-pixel method can be divided into Interpolation method (Zhang and Wu, 2006), Matrix method (Lyvers and Mitchell, 1989;Chong and Raveendran, 2003;Tabatabai and Mitchell, 1984), and Fitting method (Shang and Chen, 2011;Chong and Raveendran, 2013). Among them, Matrix method is sensitive to the image noise; Fitting method model is complex, solving slower; the accuracy of the Interpolation method is best, and the calculation is relatively least.…”

Section: Introductionmentioning

confidence: 99%

Sub-pixel Edge Detection of Cubic Spline in HSV Color Space

2017

Revista Tecnica De La Facultad De Ingenieria Universidad Del Zu

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In the process of image position and detection, the pixel-level edge image sometimes cannot meet the demand, which requires more precise sub-pixel edge detection technology. This article detected the pixel-level edge image using the sin cos HSV  color distance model in HSV space firstly, then obtained the sub-pixel edge image using Cubic spline to interpolate, and made a comparative study with B-spline to their results. The results showed that the entropy of the sub-pixel image is richer than the pixel-level image, and Cubic spline is better than B-spline.

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Subpixel measurements using a moment-based edge operator

Cited by 299 publications

References 10 publications

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

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

<title>Facet model and mathematical morphology for surface characterization</title>

Sub-pixel Edge Detection of Cubic Spline in HSV Color Space

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