Edge Detection Operator Research Articles

Defect detection for tire laser shearography image using curvelet transform based edge detector

In this work, we approach the analysis and segmentation of tire laser shearography image by combining curvelet transform and Canny edge detection to detect defects in tire surface. We rely on the feature of curvelet that edge features can be represented with larger coefficients in sub-highest frequency band thus we modify curvelet coefficients to enhance image edges before further edge detection operations. Only the most important coefficients that contribute to rebuild edges are selected to reconstruct the image while most small coefficients are cut off. This would result in a reconstructed image more convenient for edge detection and the time complexity is reduced on the other hand. Furthermore, the eight-neighborhood bilinear interpolation non-maximum suppression method is introduced to improve the performance of Canny edge detection. Our detection results are evaluated on test laser shearography images using the proposed scheme and compare favorably to the state-of-the-art methods.

Extraction of vehicle number plates using edge detection and morphological operations

This paper presents a methodology for extraction of the vehicle number plates from the vehicle images using edge detection techniques. The main idea is to use different morphological operations in such a way so that the number plate of the vehicle can be identified precisely. The methodology makes the task of extraction of the number plate independent of color, size and location of number plate. This algorithm can quickly and correctly detect the number plate area from the vehicle image in any direction but there is sufficient light on fall on number plate.

The Image Positioning and Segmentation Techniques about Gauge Visual Detection System between High Signals and Contact Net Based on Target Feature

In the design process of gauge visual detection system between high signals and contact net, it is need to do target positioning and segmentation between high signals and contact line. The paper first analyzes the structural characteristics of high signals and contact line, and then uses the Sobel operator for edge detection, and uses first close and then open of image morphological operations for edge treatment, finally uses Hough transform for line and circle detection to extract the object's edge. This positioning and segmentation of target object would be achieved.

Robust automatic breast and pectoral muscle segmentation from scanned mammograms

Breast skin–air interface and pectoral muscle segmentation are usually first steps in all CAD applications on scanned as well as digital mammograms. Breast skin–air interface segmentation is much more difficult task when performed on scanned mammograms than on digital mammograms. In case of pectoral muscle segmentation, segmentation difficulty of analog and digital mammograms is usually similar. In this paper we present adaptive contrast enhancement method for breast skin–air interface detection which combines usage of adaptive histogram equalization method on small region of interest which contains actual edge and edge detection operators. Pectoral muscle detection method uses combination of contrast enhancement using adaptive histogram equalization and polynomial curvature estimation on selected region of interest. This method makes segmentation of very low contrast pectoral muscle areas possible because of estimation used to segment areas which have lower contrast difference than detection threshold.

Image Segmentation and Asymmetry Analysis of Breast Thermograms for Tumor Detection

Breast Cancer is the most commonly diagnosed form of cancer in women. Infrared Thermography is a promising technology for breast cancer detection. But analysis of Breast thermograms has often been subjective and has resulted in inconsistency in the diagnosis of breast diseases by thermography. The fast growing tumor has a higher metabolic rate and associated increase in local vascularisation. It will cause the occurrence of some asymmetric heat patterns. Clinical interpretation of a breast thermogram is primarily based on the asymmetry analysis of these heat patterns visually and subjectively. In this paper, a new approach for automatic segmentation of Region of Interest and asymmetry analysis of breast thermograms is implemented. Canny edge detection operator and gradient operator are used to first segment the region of interest. Further asymmetry analysis is performed according to seven extracted features. The abnormality of a breast thermogram is clearly indicated by the features. A GUI is further created in Matlab to make the approach effectual, feasible and for Real Time analysis. 31 thermograms of normal and 11 thermograms of abnormal volunteers were taken.

Image Steganography by Variable Embedding and Multiple Edge Detection using Canny Operator

This paper proposes a method for image steganography. According to Human Visual System, any variation in the edges shown in the images has a low probability of being perceived by the human eyes, thus data can be hidden in the pixel of the cover image. These edges can be detected by using sophisticated operators like Canny edge detection operator. The gradient of the pixel of the image is determined and the image edges are detected by taking a threshold value of the gradient image. Here we go for multiple edge detection so that more number of pixels around the edge area is detected using the above method. Thus resulting in embedding of more information bits in the LSB of the image pixels further again we go for variable embedding so the combination of multiple edge detection and variable embedding improves the data hiding capacity. The multiple edge detection is limited to three times so as to reduce the distortions. To further increase the data hiding capacity and at the same time maintain a respectable PSNR variable embedding method is employed. For extraction of data the stego image is compared with the cover image, and the variation in the pixel values are detected, thus the embedded data is efficiently extracted.

Information theoretic analysis of linear shift-invariant edge-detection operators

Generally, the designs of digital image processing algorithms and image gathering devices remain separate. Consequently, the performance of digital image processing algorithms is evaluated without taking into account the influences by the image gathering process. However, experiments show that the image gathering process has a profound impact on the performance of digital image processing and the quality of the resulting images. Huck et al. proposed one definitive theoretic analysis of visual communication channels, where the different parts, such as image gathering, processing, and display, are assessed in an integrated manner using Shannon's information theory. We perform an end-to-end information theory based system analysis to assess linear shift-invariant edge-detection algorithms. We evaluate the performance of the different algorithms as a function of the characteristics of the scene and the parameters, such as sampling, additive noise etc., that define the image gathering system. The edge-detection algorithm is regarded as having high performance only if the information rate from the scene to the edge image approaches its maximum possible. This goal can be achieved only by jointly optimizing all processes. Our information-theoretic assessment provides a new tool that allows us to compare different linear shift-invariant edge detectors in a common environment.

Neural Network based Vehicle Classification for Intelligent Traffic Control

Nowadays, number of vehicles has been increased and traditional systems of traffic controlling couldn’t be able to meet the needs that cause to emergence of Intelligent Traffic Controlling Systems. They improve controlling and urban management and increase confidence index in roads and highways. The goal of this article is vehicles classification base on neural networks. In this research, it has been used a immovable camera which is located in nearly close height of the road surface to detect and classify the vehicles. The algorithm that used is included two general phases; at first, we are obtaining mobile vehicles in the traffic situations by using some techniques included image processing and remove background of the images and performing edge detection and morphology operations. In the second phase, vehicles near the camera are selected and the specific features are processed and extracted. These features apply to the neural networks as a vector so the outputs determine type of vehicle. This presented model is able to classify the vehicles in three classes; heavy vehicles, light vehicles and motorcycles. Results demonstrate accuracy of the algorithm and its highly functional level.

A convolution approach to the circle Hough transform for arbitrary radius

The Hough transform is a well-established family of algorithms for locating and describing geometric figures in an image. However, the computational complexity of the algorithm used to calculate the transform is high when used to target complex objects. As a result, the use of the Hough transform to find objects more complex than lines is uncommon in real-time applications. We describe a convolution method for calculating the Hough transform for finding circles of arbitrary radius. The algorithm operates by performing a three-dimensional convolution of the input image with an appropriate Hough kernel. The use of the fast Fourier transform to calculate the convolution results in a Hough transform algorithm with reduced computational complexity and thus increased speed. Edge detection and other convolution-based image processing operations can be incorporated as part of the transform, which removes the need to perform them with a separate pre-processing or post-processing step. As the Discrete Fourier Transform implements circular convolution rather than linear convolution, consideration must be given to padding the input image before forming the Hough transform.

A Detection System of Tool Condition Based on Image Processing Technology

This paper focused on introducing a real-time detection system of tool condition based on image processing technology and determined final algorithm after analyzing several different operators of edge detection. Firstly, the system will complete an acquisition of tool image, and transfer it to the computer. Then the image will be processed by some digital processing algorithms. At last, we can get the tool wear according to the size change of a tool. The result of this study showed that the detection system and its image processing solution could give a desired result. The detection accuracy could reach macron level, and the degree of automation could be improved too. So the system can be used to better meet the needs of high precision and high-speed in modern production.

Fractional differential algorithms for rock fracture images

It is a new research topic that fractional differential theory is used into image processing. This paper presents a new type of algorithms to improve the fractional differential Tiansi operator, which can significantly enhance the edge detection result. The studied algorithms are based on the enhancement ability of fractional differential to image details, and they can be used to analyse the properties of fractional differential. The general procedure of the algorithms is as follows: firstly, Tiansi template is divided into eight sub-templates with different directions around the detecting pixel, and then the eight weight sum values for the eight sub-templates are obtained. Furthermore, those eight weights are classified into different groups. In this way, the three improved algorithms with different enhancing ranges are obtained. Finally, the experiments of edge detection show that the improved algorithms can obtain edge information more effectively and can show much more detailed information than traditional edge detection operators especially for the images of fine edges such as complicated rock fracture images.

XDoG: An eXtended difference-of-Gaussians compendium including advanced image stylization

Recent extensions to the standard difference-of-Gaussians (DoG) edge detection operator have rendered it less susceptible to noise and increased its aesthetic appeal. Despite these advances, the technical subtleties and stylistic potential of the DoG operator are often overlooked. This paper offers a detailed review of the DoG operator and its extensions, highlighting useful relationships to other image processing techniques. It also presents many new results spanning a variety of styles, including pencil-shading, pastel, hatching, and woodcut. Additionally, we demonstrate a range of subtle artistic effects, such as ghosting, speed-lines, negative edges, indication, and abstraction, all of which are obtained using an extended DoG formulation, or slight modifications thereof. In all cases, the visual quality achieved by the extended DoG operator is comparable to or better than those of systems dedicated to a single style.

Weed Image Edge Detection Based on Order Morphology Transformation

Weed recognition is one of the key parts of variable rate spray system, and is the important part of precision agriculture. This paper applied the order morphology to weed recognition, and constructs the edge detection operations that fit to weed image. The corresponding simulation is also done, the result show that the operation that this paper proposed is very suit for weed edge detection.

Adaptive order morphology edge detection

In this paper, we present an edge detection model by combining order morphology transformation and anti-noise morphology transformation. In the model, we construct three kinds of edge detection operators and apply them in edge detection of some gray images and some urban satellite images with different levels of salt and pepper noise, gaussian noise and mixed noise. The experiment results show that less than 30% noise the edge detection effect by our model is better than that by canny operator and the general order morphology transformation. The conclusion is that our model is feasible for edge detection of the gray image and the satellite remote sensing images.

General method for edge detection based on the shear transform

A method for detecting edges is proposed in this study. First, it combines a traditional edge detection operator with the shear transform utilising the fact that the shear filters provide a more favourable treatment of directions, the shear transform makes the directional edges easy to detect by traditional operators; then the edge information in different directions are fused to complement each other, and a correspondence threshold, which is acquired according to receiver operating characteristic curves, is used to refine the edges; finally, the optimal result is obtained. Moreover, the authors can consider the proposed method as a general method since it could be applicable to other edge detection operators. The experimental results have indicated that our method has advantage over the traditional ones of the effectiveness of edge detection and the ability of noise rejection.

A Hybrid Method for Edge Continuity Based on Pixel Neighbors Pattern Analysis (PNPA) for Remote Sensing Satellite Images

Edge enhancement is derived from lack of accurate result from edge detection techniques. The image which is captured from long distances carries a lot of noise and blur which causes edge discontinuity. Although some novel algorithms which are based on cellular neural network, fuzzy enhancement and binary morphology have shown accuracy in order to obtain refined edge but still the problem of edge discontinuity arises. Eliminating discontinuity of edge a hybrid technique is proposed based on pixel neighbors pattern analysis PNPA. In the technique Canny operator for initial edge detection, PNPA operation for edge enhancement are performed for remote sensing satellite image successively. The visual and subjective evaluation shows that the proposed PNPA operation can effectively eliminate the influence of edge discontinuity which occurred due to noise and blurr in original captured image, as comparing to existing edge segmenting processes.

A Design Flow for Robust License Plate Localization and Recognition in Complex Scenes

In this paper, we present a new design flow for robust license plate localization and recognition. The algorithm consists of three stages: 1) license plate localization; 2) character segmentation; and 3) feature extraction and character recognition. The algorithm uses Mexican hat operator for edge detection and Euler number of a binary image for identifying the license plate region. A pre-processing step using median filter and contrast enhancement is employed to improve the character segmentation performance in case of low resolution and blur images. A unique feature vector comprised of region properties, projection data and reflection symmetry coefficient has been proposed. Back propagation artificial neural network classifier has been used to train and test the neural network based on the extracted feature. A thorough testing of algorithm is performed on a database with varying test cases in terms of illumination and different plate conditions. Practical considerations like existence of another text block in an image, presence of dirt or shadow on or near license plate region, license plate with rows of characters and sensitivity to license plate dimensions have been addressed. The results are encouraging with success rate of 98.10% for license plate localization and 97.05% for character recognition.

A New Open Curve Detection Algorithm for Extracting the Laser Lines on the Road

This paper proposes a new open curve detection algorithm for recognizing the laser rays on the road to verify the roughness of the road surface by parsing the curvature of the laser rays. The laser line in the image is first enhanced by the bright line enhance filtering to remove the clutters generated by the background. The enhanced line has the characteristics that its gray value is greater than that of background, so the established energy functional in open curve detection process has a gray value constraint to drive the evolution curve to the brightest place in image. The line on the road surface is an open curve throughout the whole image, so we fixed the two endpoints of the evolution curve to maintain it is an open curve in curve evolution. Moreover, we design an edge inhibit operator to restrain the influence of objects edges to ensure the accuracy of the test laser line. We compared our algorithm with the edge detection operators, and the comparison results showed that our method is more accurate and more robustness to the background.

Application of Multi-Scale Morphology Gradient in the Foreign Fibers Recognition

The extraction of edge feature is a key technology in the foreign fibers recognition. The traditional algorithm of edge detection operator is more sensitive to noise, which is unable to extract complete edge information. Based on the morphological edge detection, we can get the ideal foreign fibers edge characteristics under the condition of noise for the multi-scale structure elements, combining with inflation and corrosion operator to structure morphology gradient operator, the adjustment of morphology structure elements dimensions. Experiment results show that it has a certain feasibility and effectiveness compared to classic edge detection algorithm in the respect of restraining the noise and improving the testing precision.

A Design of Face Detection and Facial Expression Recognition Techniques Based on Boosting Schema

During the development of the facial expression classification procedure, we evaluate three machine learning methods. We combine ABAs with CARTs, which selects weak classifiers and integrates them into a strong classifier automatically. We have presented a highly automatic facial expression recognition system in which a face detection procedure is first able to detect and locate human faces in image sequences acquired in real environments. We need not label or choose characteristic blocks in advance. In the face detection procedure, some geometrical properties are applied to eliminate the skin color regions that do not belong to human faces. In the facial feature extraction procedure, we only perform both the binarization and edge detection operations on the proper ranges of eyes, mouth, and eyebrows to obtain the 16 landmarks of a human face to further produce 16 characteristic distances which represent a kind of expressions. We realize a facial expression classification procedure by employing an ABA to recognize six kinds of expressions. The performance of the system is very satisfactory; whose recognition rate achieves more than 90%.