Detection of Intensity Changes with Subpixel Accuracy Using Laplacian-Gaussian Masks

Huertas, A.; Medioni, Gérard

doi:10.1109/tpami.1986.4767838

Cited by 393 publications

(154 citation statements)

References 11 publications

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“…Some of the most popular edge detectors are Sobel [40] and Prewitt [41] based on the rst-order derivative of the pixel intensities or the Laplacian-ofGaussian (LoG) [42,43] edge detector that uses instead the second-order di erential operators to detect the location of edges. However, these algorithms tend to be sensitive to noise, which is actually a high frequency phenomenon.…”

Section: Related Workmentioning

confidence: 99%

A generalized entropy-based two-phase threshold algorithm for noisy medical image edge detection

Elaraby

Moratal

2017

Scientia Iranica

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Abstract. Edge detection in medical imaging is a signi cant task for object recognition of human organs and is considered a pre-processing step in medical image segmentation and reconstruction. This article proposes an e cient approach based on generalized Hill entropy to nd a good solution for detecting edges under noisy conditions in medical images. The proposed algorithm uses a two-phase thresholding: rstly, a global threshold calculated by means of generalized Hill entropy is used to separate the image into object and background. Afterwards, a local threshold value is determined for each part of the image. The nal edge map image is a combination of these two separate images based on the three calculated thresholds. The performance of the proposed algorithm is compared to Canny and Tsallis entropy using sets of medical images corrupted by various types of noise. We used Pratt's Figure Of Merit (PFOM) as a quantitative measure for an objective comparison. Experimental results indicated that the proposed algorithm displayed superior noise resilience and better edge detection than Canny and Tsallis entropy methods for the four di erent types of noise analyzed, and thus it can be considered as a very interesting edge detection algorithm on noisy medical images.

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Section: Related Workmentioning

confidence: 99%

A generalized entropy-based two-phase threshold algorithm for noisy medical image edge detection

Elaraby

Moratal

2017

Scientia Iranica

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“…There are Sobel, Laplacian, LoG(Laplacian of Gaussian) and Canny edge detectors that have the accuracy of the pixel level. Based on the results extracted by edge detection of pixel level, it is possible to detect the edge at the subpixel level, such as by interpolation and by the method that models the intensity of images by a parametric model [9][10]. In this study, we first detected the edge by the LoG operator of the pixel level and then conducted the subpixel level detection using facet modeling.…”

Section: Edge Detection Algorithmmentioning

confidence: 99%

In-line Critical Dimension Measurement System Development of LCD Pattern Proposed by Newly Developed Edge Detection Algorithm

Park¹,

Lee²,

Pahk³

2013

Journal of the Optical Society of Korea

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As the essential techniques for the CD (Critical Dimension) measurement of the LCD pattern, there are various modules such as an optics design, auto-focus [1][2][3][4], and precise edge detection. Since the operation of image enhancement to improve the CD measurement repeatability, a ring type of the reflected lighting optics is devised. It has a simpler structure than the transmission light optics, but it delivers the same output. The edge detection is the most essential function of the CD measurements. The CD measurement is a vital inspection for LCDs [5][6] and semiconductors [7][8] to improve the production yield rate, there are numbers of techniques to measure the CD. So in this study, a new subpixel algorithm is developed through facet modeling, which complements the previous sub-pixel edge detection algorithm. Currently this CD measurement system is being used in LCD manufacturing systems for repeatability of less than 30 nm.

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“…This edge-based segmentation process does not exhibit topology control, but ensures that a single, closed contour with no holes is obtained from an image. Traditional image processing and segmentation techniques based on edges or regions [3,11,4,22]; pattern recognition methods [23,5,13] and model-based such as Markovian Random Fields (MRF) and Fractals [6,25,19] do not exploit topological properties either.…”

Section: Related Workmentioning

confidence: 99%

Topological triangle characterization with application to object detection from images

Nonato

Lizier

Batista

et al. 2008

Image and Vision Computing

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Detection of Intensity Changes with Subpixel Accuracy Using Laplacian-Gaussian Masks

Cited by 393 publications

References 11 publications

A generalized entropy-based two-phase threshold algorithm for noisy medical image edge detection

A generalized entropy-based two-phase threshold algorithm for noisy medical image edge detection

In-line Critical Dimension Measurement System Development of LCD Pattern Proposed by Newly Developed Edge Detection Algorithm

Topological triangle characterization with application to object detection from images

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