Edge Detection Using the Magnitude of the Gradient (original) (raw)

An edge contour extraction technique

Gradient operators are used in image processing and computer vision to detect edges and estimate their local orientation. Most operators have to process the entire image. In this paper, an edge contour extraction technique is presented. The proposed technique is based on the gradient magnitude and a quantisation of gradient directions. Its distinctive aspect consists in that the technique does not process the entire image to extract edge contours. It detects an edge region and locates an edge. The pixel coordinates where the edge is located are used as an anchor for tracking an edge contour. Edges in a contour have a common quantised gradient direction. Experimental validation using ground-truth edge images is presented. The proposed technique is compared to the Sobel and the Canny operators. It produces results similar to the Canny edge detector, in terms of true positive rate, false positive rate and false positive edge probability.

A novel Approach to Gradient Edge Detection

Journal of Computer Science and Technology

Edges characterize boundaries and are therefore a problem of fundamental importance in image processing. Image Edge detection significantly reduces the amount of data and filters out useless information, while preserving the important structural properties in an image. The aim of our proposed method is to obtain thin edges so that the result is more suitable for further applications such as boundary detection image segmentation, object identification and so on. In this study, we propose a new two approaches based on gradient edge detection method . we only focus on traditional Canny Edge Detection in our methods and introduce convolution masks to obtain better edges. The proposed project is implemented using MATLAB 7.0.

A Study on Image Edge Detection Using the Gradients

2012

A study on image edge detection using gradients is presented in this paper. In image processing and image analysis edge detection is one of the most common operations. Edges form the outline of an object and also it is the boundary between an object and the background. Detecting accurate edges are very important for analyzing the basic properties associated with an image such as area, perimeter, and shape. The software tool that has been used is MATLAB 7.0.

An Efficient Gradient based Algorithm for Improving Performance of Image Edge Detection

International Journal of Computer Applications, 2014

Quality and execution time are two important factors for evaluation of edge detection algorithms. In these algorithms, there is a trade-off between quality and execution time. Some algorithms only concentrate on quality and some of them are fast and low quality. Efficient methods try to achieve high quality in a low time. This research concentrates on improvement of gradient based edge detection that is fast method and appropriate for real-time processing. The proposed algorithm reduces execution time by removing many pixels from computations. It calculates gradient and angle class of remaining pixels in a very efficient way so that it reinforces quality and locality of edges. The results of this algorithm indicated improvement of performance in comparison to Canny and LOG algorithms.

Study and Analysis of Edge Detection Techniques in Digital Images

This work provides a review of various techniques which have been presented in literature for detection of edges in digital images. Various techniques have been proposed over the years using linear and nonlinear gradient operators. Apart from these operators, techniques such as fuzzy logic have also been used for edge extraction. These detection techniques have also been used for various applications such as image restoration, segmentation, object detection and so on.

Comparative Study and Analysis of Various Edge Detection Algorithms in Digital Image Processing

In the field of image processing, edge detection is an important step for extracting relevant and meaningful information from digital images. The main goal of edge detection techniques is to obtain and detect thin edges of the objects present in the image, so that the result is more suitable for further processing and analysis such as boundary detection, image segmentation, motion detection/estimation, texture analysis, object identification, feature detection, implementing various transformations and so on. We tested six edge detection algorithms that use different methods for detecting edges and compared their results under a variety of situations to determine a generally preferable technique under different sets of conditions. This data could then be used to create a multi-edge-detector system, which analyses the scene and runs the edge detector best suited for the current set of data. For each of these edge detectors we considered two different ways of implementation, the one using intensity only and the other coupling to it, the colour information. We also considered one additional edge detector which takes a different philosophy to edge detection. Rather than trying to find the ideal edge detector to apply to traditional photographs, it would be more efficient to merely change the method of photography to one which is more conducive to edge detection. It makes use of a camera that takes multiple images in rapid succession under different lighting conditions. It has been observed that the Canny"s edge detection algorithm performs better than all these operators under almost all scenarios. Evaluation of the images showed that under noisy conditions Canny, LoG(Laplacian of Gaussian), Robert, Prewitt, Sobel exhibit better performance, respectively. It has been observed that Canny"s edge detection algorithm is computationally more expensive compared to LoG(Laplacian of Gaussian), Sobel, Prewitt and Robert"s cross operator.

A study of Edge Detection Techniques

An edge is a sharp discontinuity or a significant change in local intensity of an image. The process for tracking the edge of various components in an image is referred to as edge detection. Edge detector operators is classified into two basic types, namely, Gradient based or first order derivative based edge detectors and second order derivative based edge detectors. This paper describes these two algorithms in detail along with stepwise image examples.

A Review on Edge Detection Technique in Image Processing Techniques

Edge detection refers to the process of identifying and locating sharp discontinuities in an image. The discontinuities are abrupt changes in pixel intensity scene. Traditional method of edge detection involves convolving the image with an operator (2-D filter) which is constructed to be sensitive to large gradients. Edge detectors form a collection of very important local image processing method to locate sharp changes in the intensity function. Edge detection is an important technique in many image processing applications such as object recognition, motion analysis, pattern recognition, medical image processing etc. This paper shows the comparison of edge detection techniques under different conditions showing advantages and disadvantages of the selected algorithms. This was done under Matlab. Further work would be to develop a novel algorithm using the working on the disadvantages and advantages of the existing one to create a novel edge detector.

An Overview of Various Edge Detection Techniques used in Image Processing

—This paper presents an effective comparison between various edge detection techniques. Edges represent the object boundaries and this way they are crucial for filtering of unnecessary data.This is the reason behind edge detection being an essential component in many computer image processing subfields such as classification, feature extraction, pattern recognition etc.We compare Morphological Gradient Edge Detector, Sobel edge detector and Laplacian of Gaussian edge detector. It is found that Sobel filter exhibits better results thanothers in terms of PSNR values.

Study and comparison of various image edge detection techniques

International Journal of Image Processing …, 2009

Edges characterize boundaries and are therefore a problem of fundamental importance in image processing. Image Edge detection significantly reduces the amount of data and filters out useless information, while preserving the important structural properties in an image. Since edge detection is in the forefront of image processing for object detection, it is crucial to have a good understanding of edge detection algorithms. In this paper the comparative analysis of various Image Edge Detection techniques is presented. The software is developed using MATLAB 7.0. It has been shown that the Canny's edge detection algorithm performs better than all these operators under almost all scenarios. Evaluation of the images showed that under noisy conditions Canny, LoG( Laplacian of Gaussian), Robert, Prewitt, Sobel exhibit better performance, respectively. 1. It has been observed that Canny's edge detection algorithm is computationally more expensive compared to LoG( Laplacian of Gaussian), Sobel, Prewitt and Robert's operator.