Edge Detection In Gray Images Research Articles

Color Image Edge Detection Based on Fuzzy Rule Based Approach

Edge detection method is one of the important techniques in Image Segmentation, which is used to find out the objects in the input image in exact manner. An edge is the boundary between an object and background and it indicates the boundary between overlapping objects. One of the most commonly used operation analysis is edge detection, which is used for enhancing and detecting edges in the image. It removes useless data, noise and frequencies while preserving the important structural properties in an image. Fuzzy Logic techniques have been used in image understanding applications such as detection of edges, feature extraction, classification, and clustering. Fuzzy logic possess the ability to mimic the human mind to employ modes of reasoning that are approximate rather than exact form effectively. Edge detection in gray images has lot of methods to locate edges and it has several set of algorithms to represent it. But the images produce more information in scenes i.e., color images have few numbers of methods to segment it. So, this paper represent color image segmentation methods in the literature and getting to prepare novel segmentation method by extracting the color channels in the RGB image with combined form of masking, filtering and Thresholding methods. This paper discuss about RGB color model and fuzzy membership functions method and particularly explain about the usage of fuzzy membership functions which are used to create different combination of mask with some sort of rules based on RGB channel extraction to scan the separated channel image and include Threshold and filtering concepts for further to produce the output image in well enhanced way.

A Novel Approach for Color Image Feature Extraction using Swarm Intelligence

This paper reports research into representing edge detectors, with the help of swarm intelligence [1]. Other gradient-based detectors may not produce the same edge pixels if applied onto the same image. Hence, the need of an edge detector arises, which is precise in detecting edges in majority of the common types of edges. The idea behind Swarm intelligence arises from the insects, bird flocks, fish schools, and wildebeest herds etc. They all have some common features, they move in groups which are having a special behavior. Their coordination is so good and it observed that as some centralized controller dictates all movement. With this background, swarm intelligence relates with ants which may not be very clever individually, but ant colonies are capable of searching, making plans, and optimizing routes to food. Ant colonies are so good at finding the shortest path from one location to another, that an algorithm was developed based on their behavior called as Ant Colony Optimization. Keeping this idea in mind the paper extends previous work of an ant algorithm which is used for edge detection of gray image. Here, ACO is applied for feature extraction and edge pattern detection of a color image. An adaptive threshold histogram method is introduced to the ACO. An image is distributed separately as a combination of three primary colors Red(R), Green (G) and Blue (B). Based on these color intensities three separate histograms are generated and their average mean value is computed which is the threshold value. Thus, a threshold value is generated dynamically every time. General Terms Edge Detection, Feature Extraction, Swarm Intelligence, pheromone updation and evaporation

Simple Fuzzy Rule Based Edge Detection

Most of the edge detection methods available in literature are gradient based, which further apply thresholding, to find the final edge map in an image. In this paper, we propose a novel method that is based on fuzzy logic for edge detection in gray images without using the gradient and thresholding. Fuzzy logic is a mathematical logic that attempts to solve problems by assigning values to an imprecise spectrum of data in order to arrive at the most accurate conclusion possible. Here, the fuzzy logic is used to conclude whether a pixel is an edge pixel or not. The proposed technique begins by fuzzifying the gray values of a pixel into two fuzzy variables, namely the black and the white. Fuzzy rules are defined to find the edge pixels in the fuzzified image. The resultant edge map may contain some extraneous edges, which are further removed from the edge map by separately examining the intermediate intensity range pixels. Finally, the edge map is improved by finding some left out edge pixels by defining a new membership function for the pixels that have their entire 8-neighbourhood pixels classified as white. We have compared our proposed method with some of the existing standard edge detector operators that are available in the literature on image processing. The quantitative analysis of the proposed method is given in terms of entropy value.

A novel multi-scale edge detection technique based on wavelet analysis with application in multiphase flows

A novel non-linear weighted statistical algorithm for multi-scale edge detection of gray image based on wavelet analysis is proposed to extract interface in multiphase flows. In this method, local modulus maximum of gray gradient along the phase angle direction is regarded as the possible edge (interface) of an image. A two-dimensional discrete Gaussian function with zero average value is adopted as smoothening filter. The influences of filter length, scale and threshold on the extracted edges are discussed in detail. In order to realize multi-scale detection, the local maximums of the wavelet transform modulus calculated under a series of the wavelet transform scales are used to detect the possible edge (interface) of an image. A modified Gaussian function is suggested to weight non-linearly those possible edges (interfaces) detected on different scales and a statistical function is proposed to synthesize the possibility of each pixel being edge over all scales. Compared with Canny operator, this new algorithm can extract the edge perfectly even with strong noise and light reflection, which shows the potential to extract the interface in multiphase flows.