Robust edge detection in noisy images

Lim, Dong Hoon

doi:10.1016/j.csda.2004.10.005

Cited by 63 publications

(40 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the same time the corners get sharper and can be defined easily [3]. An edge in an image is a significant local change in the image intensity, usually associated with a discontinuity in either the image intensity or the first derivative of the image intensity.…”

Section: Introductionmentioning

confidence: 99%

Edge detection methods

Amer

Abushaala

2015

2015 2nd World Symposium on Web Applications and Networking (WSWAN)

View full text Add to dashboard Cite

Edge detection is a type of image segmentation techniques which determines the presence of an edge or line in an image and outlines them in an appropriate way. The main purpose of edge detection is to simplify the image data in order to minimize the amount of data to be processed. Generally, an edge is defined as the boundary pixels that connect two separate regions with changing image amplitude attributes such as different constant luminance and tristimulus values in an image. In this paper, we present methods for edge segmentation of images; we used five techniques for this category; Sobel operator technique, Prewitt technique, Laplacian technique, Canny technique, Roberts technique, and they are compared with one another so as to choose the best technique for edge detection segment image. These techniques applied on one image to choose base guesses for segmentation or edge detection image. In this paper an attempt is made to study the performance of most commonly used edge detection techniques for image segmentation and also the comparison of these techniques is carried out with an experiment by using MATLAB software. We will use the edges to find congruence between objects.

show abstract

Section: Introductionmentioning

confidence: 99%

Edge detection methods

Amer

Abushaala

2015

2015 2nd World Symposium on Web Applications and Networking (WSWAN)

View full text Add to dashboard Cite

show abstract

“…Statistical approaches, such as those proposed by Bovik et al, 3 Lim and Jang, 4 and Lim,5 offer an alternative to gradient techniques. The concept behind these statistical approaches is to examine the distribution of intensity values in the neighbourhood of a given pixel and determine if the pixel should be classified as an edge.…”

Section: Introductionmentioning

confidence: 99%

Edge detection based on morphological amoebas

Lee

Kim

et al. 2012

The Imaging Science Journal

View full text Add to dashboard Cite

Detecting the edges of objects within images is critical for quality image processing. We present an edge-detecting technique that uses morphological amoebas that adjust their shape based on variation in image contours. We evaluate the method both quantitatively and qualitatively for edge detection of images, and compare it to classic morphological methods. Our amoeba-based edge-detection system performed better than the classic edge detectors.Comment: To appear in The Imaging Science Journa

show abstract

“…Since the input images are noisy, we use a robust edge detector [16]. Then for each patch P i , we obtain a color edge map E a.…”

Section: Rank Estimationmentioning

confidence: 99%