Edge Detection Approach Research Articles

FAIR: A Fast Algorithm for Document Image Restoration

We present, in this paper, the FAIR algorithm: a fast algorithm for document image restoration. This algorithm has been submitted to different contests where it showed good performance in comparison to the state of the art. In addition, this method is scale invariant and fast enough to be used in real-time applications. The method is based on a double-threshold edge detection approach that makes it possible to detect small details while remaining robust against noise. The performance of the proposition is evaluated on several types of degraded document images where considerable background noise or variation in contrast and illumination exist.

Study of edge detection task in dental panoramic radiographs

The purpose of this study is (1) to introduce a new approach for edge detection in orthopantograms (OPGs) and an improved automatic parameter selector for common edge detectors, (2) to present a comparison between our novel approach with common edge detectors and (3) to provide faster outputs without compromising quality. A new approach for edge detection based on statistical measures was introduced: (1) a set of N edge detection results is calculated from a given input image and a selected type of edge detector, (2) N correspondence maps are constructed from N edge detection results, (3) probabilities and average probabilities are computed, (4) an overall correspondence is evaluated for each correspondence map and (5) the correspondence map providing the best overall correspondence is taken as the result of edge detection procedure. A comparison with common edge detectors (the Roberts, Prewitt, Sobel, Laplacian of the Gaussian and Canny methods) with various parameter settings (304 combinations for each test image) was carried out. The methods were assessed objectively [edge mismatch error (EME), modified Hausdorff distance (MHD) and principal component analysis] and subjectively by experts in dentistry and based on time demands. The suitability of the new approach for edge detection in OPGs was confirmed by experts. The current conventional methods in edge detection in OPGs are inadequate (none of the tested methods reach an EME value or MHD value below 0.1). Our proposed approach for edge detection shows promising potential for its implementation in clinical dentistry. It enhances the accuracy of OPG interpretation and advances diagnosis and treatment planning.

A Nobel Hybrid Approach for Edge Detection

The objective of this paper is to present the hybrid approach for edge detection. Under this technique, edge detection is performed in two phase. In first phase, Canny Algorithm is applied for image smoothing and in second phase neural network is to detecting actual edges. Neural network is a wonderful tool for edge detection. As it is a non-linear network with built-in thresholding capability. Neural Network can be trained with back propagation technique using few training patterns but the most important and difficult part is to identify the correct and proper training set.

River terrace sand and gravel deposit reserve estimation using three-dimensional electrical resistivity tomography for bedrock surface detection

We describe the application of 3D electrical resistivity tomography (ERT) to the characterisation and reserve estimation of an economic fluvial sand and gravel deposit. Due to the smoothness constraints used to regularise the inversion, it can be difficult to accurately determine the geometry of sharp interfaces. We have therefore considered two approaches to interface detection that we have applied to the 3D ERT results in an attempt to provide an accurate and objective assessment of the bedrock surface elevation. The first is a gradient-based approach, in which the steepest gradient of the vertical resistivity profile is assumed to correspond to the elevation of the mineral/bedrock interface. The second method uses an intrusive sample point to identify the interface resistivity at a location within the model, from which an iso-resistivity surface is identified that is assumed to define the interface. Validation of these methods has been achieved through direct comparison with observed bedrock surface elevations that were measured using real-time-kinematic GPS subsequent to the 3D ERT survey when quarrying exposed the bedrock surface. The gradient-based edge detector severely underestimated the depth to bedrock in this case, whereas the interface resistivity method produced bedrock surface elevations that were in close agreement with the GPS-derived surface. The failure of the gradient-based method is attributed to insufficient model sensitivity in the region of the bedrock surface, whereas the success of the interface resistivity method is a consequence of the homogeneity of the mineral and bedrock, resulting in a consistent interface resistivity. These results highlight the need for some intrusive data for model validation and for edge detection approaches to be chosen on the basis of local geological conditions.

Unsupervised edge detection and noise detection from a single image

Edge detection is one of the oldest image processing areas that are still active. An important current area of study involves development of unsupervised edge detection algorithms. In this work a paradigm of unsupervised edge detection is proposed that is based on the computational edge detection approach introduced by Canny. It is a simple and computationally cheap technique that achieves non-trivial results. Additionally as a byproduct it generates information about the content and severity of noise in the image. The proposed technique uses a fast edge detector to generate the initial edge mask and subsequently optimizes that by studying the behavior of a proposed details estimator. The study of the same estimator also offers insight about the noise characteristics of the image.

A Novel Edge Detection Technique for Image Classification and Analysis

The main aim of this project is to propose a new method for image segmentation. Image is concerned with splitting an image up into segments (also called regions or areas) that each holds some property distinct from their neighbor. Simply, another word for the Object Detection is Segmentation . is divided into two types they are Supervised and Unsupervised Segmentation. consists of three types of methods which are divided on the basis of threshold, edge and region. Thresholding is a commonly used enhancement whose goal is to segment an image into object and background. Edge-based segmentations rely on edges found in an image by edge detecting operators. Region based segmentations basic idea is to divide an image into zones of maximum homogeneity, where homogeneity is an important property of regions. Edge detection has been a field of fundamental importance in digital image processing research. Edge can be defined as a pixels located at points where abrupt changes in gray level take place in this paper one novel approach for edge detection in gray scale images, which is based on diagonal pixels in 2*2 region of the image, is proposed. This method first uses a threshold value to segment the image and binary image. And then the proposed edge detector. In order to validate the results, seven different kinds of test images are considered to examine the versatility of the proposed edge detector. It has been observed that the proposed edge detector works effectively for different gray scale digital images. The results of this study are quite promising. In this project we proposed a new algorithm for edge Detection. The main advantage of this algorithm is with running mask on the original image we can detect the edges in the images by using the proposed scheme for edge detection.

Matched Filtering Algorithm for Pavement Cracking Detection

There have been rapid developments in automated surveying of cracking pavements in recent years. Laser-imaging technology has made the acquisition of shadow-free images feasible. However, because of the complexity of pavement surfaces, the diverse characteristics of cracks, the presence of foreign objects, and varying identification protocols, the results of automated cracking recognition have had limited use. A matched filtering algorithm is introduced for detection of pavement cracking. Unlike traditional edge detection approaches that adopt first-or second-order derivatives of image signals, the matched filtering algorithm detects cracks by matching predesigned filters with crack features by shape, orientation, or intensity. Experiments were conducted to compare the results of five traditional edge detectors (Roberts, Prewitt, Sobel, Laplacian of Gaussian, and Canny) and the matched filtering algorithm. The matched filtering algorithm was shown to be a robust approach for detecting cracks and had better performance in noise removal and detection of cracking. With matched filters aligned at various orientations, the matched filtering algorithm showed its distinctive advantages for extracting a single crack in its actual form and recording the crack's orientation to be used for more accurate classification in the next step of automated processing.

A Novel Approach for Edge Detection in Images Based on Cellular Learning Automata

Cellular Learning Automata (CLA) has been used in many fields of image processing such as noise elimination, smoothing, retrieval, fractionated and extraction of the content Characteristics of the images. The edge detection in images and methods if edge detection, have a great role in machine vision and cognizance systems. This method uses operands for analyzing images and digital image processing. Many studios here been conducted till now in edge detection algorithms of various conditions. In this study a new method for edge detection in images with the use of CLA is recommended. The proposed method of edge detection in images was tested with different sizes and the results were compared with Sobel edge detector classic method. The result show that the method based on CLA has a desirable performance in edge detection and compares the images with a more uniformity during a minimum period of time.

Object-Based Building Extraction from High Resolution Satellite Imagery

Abstract. Automatic building extraction from high resolution satellite imagery is considered as an important field of research in remote sensing and machine vision. Many algorithms for extraction of buildings from satellite images have been presented so far. These algorithms mainly have considered radiometric, geometric, edge detection and shadow criteria approaches to perform the building extraction. In this paper, we propose a novel object based approach for automatic and robust detection and extraction of building in high spatial resolution images. To achieve this goal, we use stable and variable features together. Stable features are derived from inherent characteristics of building phenomenon and variable features are extracted using SEparability and THresholds analysis tool. The proposed method has been applied on a QuickBird imagery of an urban area in Isfahan city and visual validation demonstrates that the proposed method provides promising results.

Investigations of Cellular Automata Game of Life Rules for Noise Filtering and Edge Detection

In digital image processing, edge detection of images is an important and difficult task. Also, if the images are corrupted by noise, it smears some details and thus resulting in inaccurate edge detection. Hence, a pre-processing step must be taken before the edge detection. In this paper a new approach for edge detection with noise filtering of digital images using Cellular Automata Game of Life is presented. This procedure can easily be generalized and used for any type of digital media. To illustrate the proposed method, some experiments have been performed on standard test images and compared with popular methods. The results reveal that the proposed method has relatively desirable performance.

An Multi-scale Edge Detection Approach

An novel edge detection approach is proposed. Firstly, the nonsubsampled contourlet transform is used to decompose the original image into low frequency approximation image and the high frequency subbands. Then, because of nonsubsampled contourlet transform shift-invariance, the original image corresponding gradient magnitude is redefined in every scale. Finally, different scales gradient is synthesized into the image dege. The experimental results of edge detection for several test images are provided to demonstrate the approach.

Unstructured Road Edge Detection and Initial Positioning Approach based on Monocular Vision

This paper proposes two complementary road edge detection methods for unstructured roads. The methods can be used for visual navigation and intelligent control of mobile robots. One method is color-based, which is applicable to the circumstance that the road and off-road areas have obvious color difference; the other is based on threshold segmentation and can be applied even the road boundary is not clear. On the other hand, shadow has enormous impact on threshold segmentation method, but color-based method is not sensitive to it. These make the two methods complementary. Furthermore, on the basis of edge detection, inverse perspective projection transformation is carried out to calculate running state of the robots. Through experiments on mobile robot platform, verifications that the two proposed methods complement each other in different road environments well are made. The experiments also show that the methods achieve high accuracy in initial positioning and meet requirement of real-time.

Quantitative Analysis of Feature Detection Using Adaptive Canny Edge Detector and Enhanced Ant Colony Optimization

Feature detection is a fundamental technique in broad fields of image processing, pattern recognition and computer vision. A digital image in general contains objects, edges, noises and background. Critical changes in properties of objects can be captured via detecting sharp variations in image brightness. The edges can be detected via numerous approaches on a basis of image intensity changes. Edge broken and false detection are typical problems using classical methods, which will result in information loss and feature deformity. The notion of optimization is thus introduced into edge detection. The Canny edge detector and Ant Colony Optimization (ACO) detector are among the most successful and effective approaches for edge detection. The Canny edge detector is designed to capture edges by searching local optima of the gradient of the intensity. It is susceptible to noises presenting on the raw images, so details of images could be slightly changed when Gaussian smoothing is applied. To improve accuracy, the adaptive edge tracing scheme is proposed. On the other hand, artificial intelligence has also been introduced. Being one of metaheuristic optimization approaches, the evolutionary computing oriented ACO becomes a promising approach for feature capturing without necessity of smoothing filters. Selection of maximum intensity difference as the path visibility function for ACO will contribute better to generate true edges and avoid false edges. Both the adaptive Canny edge detection and enhanced ACO are proposed in this article. Comparative studies are also conducted to evaluate the edge detection qualities. The outcomes are analyzed and evaluated from both qualitative and quantitative points of view, where merits and drawbacks of the two schemes have been indicated.

A Comparative Study on Retrieved Images by Content Based Image Retrieval System based on Binary Tree, Color, Texture and Canny Edge Detection Approach

Content based image retrieval (CBIR) is a technique in which images are indexed by extracting their low level features or high level features or both. This paper presents performance & comparative study of the result obtained by the two different approaches of CBIR system. The resultant retrieved images by the CBIR system is based on two approaches. Both approaches of the system using two major processes one as feature extraction and other as search and retrieval. The first approach is based on extracting the features using binary tree structure, color & texture and the second approach is based on extracting shape features using Canny Edge Detection. In both the approaches the feature vector of the query image is compared with the feature vector of the each image present in the database. In the first approach the images are retrieved and arranged according to their matching score or rank value and in the second approach retrieved images are arranged according to their mean and correlation values.

An Improved Approach of Color Edge Detection Based on Robustness of Cell Neural Network

For the disadvantage of cell neural network (CNN) method which can not directly deal with color images, we present a new color image edge detection algorithm according to CNN model. Through robustness analysis for CNN template, a CNN theorem be carried out which can compute in the RGB color space. The experimental results show that our approach can effectively carry out edge extraction and locates accurately.

Approach of Image Edge Detection Based on Wavelet Scale Multiplication

Edge detection is one of the most important techniques in image processing. A new image edge detection approach is presented by wavelet scale multiplication, which has the advantage of magnifying the edge structures and suppressing the noise. The dyadic wavelet transforms at tow adjacent scales are multiplied as product function and image edge is detected by wavelet model maximum. It need not link edge points, realize simply and is usually applied to noisy, low contrast image.

Fabric Defects Detection Using Multi-Scale Wavelet and Locating

In this paper, multi-scale wavelet edge detection approach is investigated for real time inspection of diversified fabric texture. Multi-scale edge detectors smooth the signal at various scales and detect sharp variations points from their first or second order derivative. The extreme values of the first derivative correspond to the zero crossings of the second derivative and to the inflection points of the smoothed signal. Quadtree decomposition of segmented defects shows pinpoint location of specific web flaw. Further, preliminary graphical user interface (GUI) was designed so as to facilitate operation. After integrate GUI with procedure, parameters material can be acquired, which is vital to applying the inspection system on industrial PC.

Accurate Breast Contour Detection Algorithms in Digital Mammogram

Aided Diagnosis (CAD) systems have improved diagnosis of abnormalities in mammogram images. The principal feature within the breast region is the breast contour. Extraction of the breast region and delineation of the breast contour allows the search for abnormalities to be limited to the region of the breast without undue influence from the background of the mammogram. After performing an essential pre-processing step to suppress artifacts and accentuate the breast region, the exact breast region as the region of interest (ROI), has to be segmented. In this paper we present a fully automated segmentation and boundary detection method for mammographic images. In this research paper we have proposed a new homogeneity enhancement process namely Binary Homogeneity Enhancement Algorithm (BHEA) for digital mammogram. This is followed by a novel approach for edge detection (EDA) and finally obtaining the breast boundary by using our proposed Breast Border Boundary Enhancement Algorithm. This composite method have been implemented and applied to mini- MIAS, one of the most well-known mammographic database consisting of 322 medio-lateral oblique (MLO) view obtained via a digitization procedure. To demonstrate the capability of our segmentation algorithm it was extensively tested on mammograms using ground truth images and quantitative metrics to evaluate its performance characteristics. The experimental results indicate that the breast boundary regions were extracted accurately characterize the corresponding ground truth images. The algorithm is fully autonomous, and is able to preserve, skin and nipple (if in profile), a task very few existing mammogram segmentation algorithms can claim.

Application of Fractal Theory for Prediction of Shrinkage of Dried Kiwifruit Using Artificial Neural Network and Genetic Algorithm

In current research, fractal theory has been applied for estimation of shrinkage of osmotically dehydrated and air-dried kiwifruit using a combination of neural network and genetic algorithm. Kiwifruits were dehydrated at different conditions and digital images of final dried products were taken. Kiwifruit-background interface lines were detected using a threshold combined with an edge detection approach and their corresponding fractal dimensions were calculated based on a box counting method. A neural network was constructed using fractal dimension and moisture content as inputs to predict shrinkage of dried kiwifruit and a genetic algorithm was applied for optimization of the neural network's parameters. The results indicated good accuracy of optimal model (correlation coefficient of 0.95) and high potential application of fractal theory and described intelligent model for shrinkage estimation of dried kiwifruit.

A novel bacterial foraging technique for edge detection

A new approach for edge detection using a combination of bacterial foraging algorithm (BFA) and probabilistic derivative technique derived from Ant Colony Systems, is presented in this paper. The foraging behavior of some species of bacteria like Escherichia coli can be hypothetically modeled as an optimization process. A group of bacteria search for nutrients in a way that maximizes the energy obtained per unit time spent during the foraging. The proposed approach aims at driving the bacteria through the edge pixels. The direction of movement of the bacteria is found using a direction probability matrix, computed using derivatives along the possible directions. Rules defining the derivatives are devised to ensure that the variation of intensity due to noise is discarded. Quantitative analysis of the feasibility of the proposed approach and its comparison with other standard edge detection operators in terms of kappa and entropy are given. The effect of initial values of parameters of BFA on the edge detection is discussed.