Edge Detection Operator Research Articles

Image Segmentation using Morphological Operations

Image Segmentation plays vital role in Computer Vision and Digital Image Processing. It is the process of separating the digital image into distinct region(s) possessing homogeneous properties. The main objective of image segmentation is to extract various features of the image that are used for analyzing, interpretation and understanding of images. Image segmentation is applied in various applications like medical imaging, shape detection, content-based image retrieval, robot vision, etc. Several techniques have been developed for image segmentation such as pixel-based segmentation, edge based segmentation and region based segmentation. In this paper, segmentation technique is defined using the edge detection and morphological operations. Edge detection is done using Fuzzy Canny method for better output. After detecting the edges of image, segmentation is done using morphological operation. This gives better results. General Terms Dilation, Morphology, Erosion, Flood Fill.

Image segmentation with pulse-coupled neural network and Canny operators

In this paper, a Canny operator-based method using PCNN (Pulse-Coupled Neural Network) is proposed for color image segmentation. The coarse location information of the salient object and the background is first estimated based on the distribution of the detected key-points. An image is then over-segmented into super-pixels and their histograms are computed. The saliency of a super-pixel is obtained according to the maximum of variance ration and Shannon entropy. Color image segmentation is implemented using PCNN based on Canny operator edge detection method. The proposed method is compared with state-of-the-art methods on the widely used dataset, and the experiments show that it overall obtains more accurate results.

3-Line 버퍼를 사용한 실시간 Sobel 윤곽선 추출 블록 FPGA 구현

In this Paper, an efficient method of FPGA based design and implementation of Sobel Edge detector block using 3-Line buffers is presented. The FPGA provides the proper and sufficient hardware for image processing algorithms with flexibility to support Sobel edge detection algorithm. A pipe-lined method is used to implement the edge detector. The proposed Sobel edge detection operator is an model using of Finite State Machine(FSM) which executes a matrix mask operation to determine the level of edge intensity through different of pixels on an image. This approach is useful to improve the system performance by taking advantage of efficient look up tables, flip-flop resources on target device. The proposed Sobel detector using 3-line buffers is synthesized with Xilinx ISE 14.2 and implemented on Virtex II xc2vp-30-7-FF896 FPGA device. Using matlab, we show better PSNR performance of proposed design in terms of 3-Line buffers utilization.

Monocular Camera Calibration Method for Robot System

The basic principles of camera calibration are first analyzed, and the method of camera calibrate based on 2D plane circular array is presented. The first process is the use of the canny edge detection operator, and get the edge coordinates of ellipse. Then the ellipse is fitted to obtain the center point of the ellipse, and the centre point coordinates of ellipse is used to regard the feature points to implement camera caliblation. Finally, Zhang Zhengyou's method is used to obtain internal and external parameters of camera. This calibration method can be used to calbration of robot system.

Image Tamper Detection Based on Edge Image and Chaotic Arnold Map

Digital images are widely used and can be easily altered through the internet medium. Therefore, this article proposes a novel method of fragile watermarking to detect the image tampers. The proposed method is implemented by an edge image and chaotic Arnold map. The idea of edge detection is to make the image as a binary version and also significantly decrease the sum of information in an image and conserving the structural properties. The edge image is obtained from the watermark image using the Canny edge detection operator. The security and sensitivity of the proposed method is enhanced by employing the Arnold map before embedding the edge image. The accuracy of tamper detection depends upon the variations made in the image and it highlights a location where the tamper has been done. The quality of the watermarked image is evaluated using the metrics Peak Signal to Noise Ratio (PSNR) and Normalized Correlation (NC). The experiments were carried out to assess the performance of the proposed method for several forms of fiddling with different images. The result shows that the proposed method efficiently localizes the tampered regions. Keywords: Fragile Watermarking, Tamper Detection, Chaotic Map, Edge Image, PSNR, NC

The Assembly Error Detection of Automotive Airbag Based on Machine Vision

Based on the principle of machine vision technology, we designed a methodto detect the outline dimensions of automotive airbag quickly and accurately. We Used CCD camera obtain the airbag image, through the image processing method ofsmooth filtering andgray-scale transformationto complete pre-processing, finally applied Canny edge detection operator to extract the boundary of the airbag contour features,and then took the template matching methodto detect assemble error of the airbag image whether meet the requirement.The results show that the detection method have a higher precision, and the time is very short, it can improve the sampled positioningerror detection for the all checks image recognition detection, suitable for application in real-time online detection of airbag assembly line.

License Plate Recognition System Design

The license plate recognition system is an important part of modern traffic management. application which is very extensive. In this paper, a method to achieve three main modules split from the image pre-processing, license plate location and character. Image pre-processing module of this article is to image gray and step by Roberts operator edge detection. License plate positioning and segmentation using mathematical morphology is used to determine the license plate location method, and then use the license plate color information of color segmentation method to complete the license plate parts division. This article is to research its main part and use MATLAB to do the image processing simulation.

English

The textual pieces in scene images might often provide vital semantic data for visual content understanding, indexing and analysis; as a result, text extraction had become a significant research area in image processing and computer vision. In this paper, we propose a new hybrid multilevel algorithm to extract text in various scene images. The algorithm converts the Red – Green –Blue (RGB) image into grayscale for color reduction. Next, it applies edge detection and mathematical morphological operations to extract edges in the image preprocessing phase. The resultant binary image passes through three subsequent levels in a multi layer behavior. Connected components labeling and text candidates' selection take place in each level through different criteria analysis. We used the structural features of connected components as basis criteria for selecting candidate texts, those features include: area, width, length and condense intensity mean of connected components. Afterwards, Horizontal projection profile analysis is used to further refine the candidate text areas and to eliminate non-text regions. The proposed algorithm is evaluated on a set of fifty images chosen from a well known text locating test dataset: KAIST. Extensive experiments show high robustness under different environments such as indoor, outdoor, shadow, night and light, and for different text properties such as various font size, style and complexities of backgrounds and textures. The algorithm effectively extracts textual contents from scenes images with high average of Precision, Recall, and F-Score which are 90.1, 99, and 94.3%, respectively.    Key words: Multilevel text extraction, hybrid text extraction, edge detection, connected components, text candidates, morphological operations, horizontal projection profile. &nbsp

Research of License Plate Recognition System Based on Labview

The vehicle license plate recognition system is the intelligent traffic management system based on the image and the character recognition technology, which is an important part of the intelligent transportation system. This paper introduces a method of vehicle license plate location based on edge detection and morphological operations, virtual instrument is combined with machine vision of the license plate recognition method [1]. Finally the license plate number of the vehicle is get. Experiment results show that such method can simplify the algorithm and has some correct location rate.

Simulation and Analysis for Activities in Image Recognition Using MATLAB®

This paper considers a fact that solutions to problems in the field of digital image processing require lots of experimental work involving software simulation and testing with large sets of sample images. A short overview of the fundamental steps of digital image processing is presented. The layout and the operation of an experimental software system that has been developed and implemented using MATLAB ® is introduced. A user friendly GUI is developed and two alternative methods for image acquisition are implemented. Few algorithms based on mask operators for image edge detection are studied, programmed, simulated, and evaluated. The paper also includes an analysis and a software implementation for an image matching technique.

A Novel Approach of Edge Detection Based on Gray Correlation Degree and Kirsch Operator

Image edge detection plays an important role in the process of image processing. This article classified the effect of various edge detection algorithms of digital images, mainly through the analysis of characteristics of the commonly used several kinds of classical edge detection operator in the time domain. For the actual image, the edge of the gray scale is sometimes not very clear, and the image also contains noise. A new algorithm of edge detection based on gray correlation degree and Kirsch operator is discussed in the paper. Kirsch operator is the reference sequence. Firstly, calculate the gray correlation degree of each pixel in the image and its neighbors formed eight action sequences and reference sequences, then determine the detection of edges based on gray correlation degree. The results show that this method can more accurately detect the useful information of the edge, and has a certain noise immunity. It is an effective and feasible new edge detection method.

Detection of Edges in Color Images

Edge detection considers the important technical details of digital image processing. Many edge detection operators already perform edge detection in digital color imaging. In this study, the edge of many real color images that represent the type of digital image was detected using a new operator in the least square approximation method, which is a type of numerical method. The Linear Fitting algorithm is computationally more expensive compared to the Canny, LoG, Sobel, Prewitt, HIS, Fuzzy, Parametric, Synthetic and Vector methods, and Robert’ operators. The results showed that the new method can detect an edge in a digital color image with high efficiency compared to standard methods used for edge detection. In addition, the suggested operator is very useful for detecting the edge in a digital color image.

On High-precision Subpixel-based Drilling Technique for Suture Needles with Thread

In order to improve the automation of end-hole drilling process in the production of suture needles with thread, a high-precision subpixel-based drilling method is proposed. According to the edge detection principle in mathematical morphology, combined with the characteristics of the magnified images of the ends of suture needles to be drilled, the morphological edge detection operators with variable structural elements are constructed to achieve noise suppression and fully extract the detailed information of edges of images of needle end holes to be drilled. Then, the subdivision method of spatial moments is adopted to realize the subpixel positioning of pixel-level edges. Finally, least squares fitting method is used to achieve the high-precision positioning of center of needle end hole to be drilled. The experimental results of the 0.5 mm needle samples show that the drilling method proposed in this study has a concentricity error no more than ± 0.2 μm and an average drilling time of 0.65S. Moreover, the method also boasts good real-time performance and stability and meets the automated production needs of drilling process of suture needles with thread.

Edge Detection Using Fuzzy Double Gradient Morphology

Detecting the edges of objects within images is a critical task for quality image processing. This paper proposes an edge detection operator based on the combination of fuzzy gradient morphology and Sobel operator. When we use traditional detection operators to detect the edge of an object in an image, we get lots of noisy Points. In this paper, we demonstrate a technique in which we preprocess an image with Sobel operator and then apply gradient morphology. This method effectively removes the noise and gives good detail image edge detection. We evaluate the method quantitatively and compare it to classical morphological method. Our fuzzy based edge segmentation method performs better than the classical edge detectors. Since the proposed methods are based on fuzzy morphological operations, these are efficient and enhanced.

Non-Contact Measurement of Yarn Tension in Spinning Process

In this paper, the limitations of the direct contact measurement of yarn tension in domestic and foreign are analyzed by use of yarn balloon theory and digital image processing technology. A new method is established for measuring the dynamic tension of yarn ------non-contact measurement. The mathematical model is established according to the theory that yarn tension is relent to yarn balloon through the research on the dynamics of yarn balloon. In human-computer interaction, image enhancement is realized by using the gray scale linear transformation curve, image segmentation is achieved by using Canny edge detection operator and the edge of the image is extracted by using mathematical morphology and borders tracking algorithm accurately from yarn balloon which is taken by CCD immediately, finally the pattern characteristic parameters of the yarn balloon are extracted and the yarn tension is measured.

Dental image analysis with different edge detection operators

This paper analyzes the quality of the six most significant operators to detect the edges through the parameters of the Peak Signal to Noise Ratio (PSNR), signal to noise ratio (SNR), Mean Square Error (MSE) and Entropy over dental images. The analysis results are presented graphically and in tables. Model analysis was performed through the conversion of primary images in the pictures with different edge detection, and analysis of images obtained through the above parameters and varying degrees of bits per pixel (bpp). The analysis shows that operators Pyramid and Roberts give much better results at lower levels of bpp, whilst, according to Entropy of image, best results are being given by Pyramid operator. The study is consisted of two parts: first analyzing the parameters PSNR, SNR and MSE through bpp and afterwards analyzing Entropy images and level of detail.

The Improved Algorithm of Edge Detection Based on Mathematics Morphology

In morphology, the structural elements of different sizes and shapes have different abilities in maintaining image detail and anti-noise, an adaptive algorithm of edge detection based on multi-structure and multi-scale element is presented in this paper. First, the existing morphological operators for edge detection are improved. Then, the weight coefficients of different scales and different shapes are determined respectively by using the method of morphological difference and calculating the information entropy of the edge-detected. Last, fuse together the edges to achieve the image edge. For the image with noise, the algorithm can filter out noise effectively, and the edge is continuous, smooth, clear outline. The experiment results show that the algorithm has good effect in edge detection, strong anti-noise capacity, and the objective evaluation and visual effect are good, too.

Image Edge Detection Based on Solitary Wave Algorithm

Edge detection is the most basic arithmetic of the local information of image change detection, image edge detection can help people better describe or identify image, is abasic technique in image processing. Edge detection is mainly to find out the important goal of the salient points on the boundary of the image,because the traditional edge detection operators have the same direction, so easily missed in the detection process.In this paper, the solitary wave algorithm for image edge detection, this algorithm can provide both the direction and size of the image edge, a certain extent, can also solve the missing edges, it is possible to extract more accurate image information.

Adaptive Real-Time Ship Detection and Tracking Using Morphological Operations

In this paper, we propose an algorithm that can efficiently detect and monitor multiple ships in real-time. The proposed algorithm uses morphological operations and edge information for detecting and tracking ships. We used smoothing filter with a <TEX>$3{\times}3$</TEX> Gaussian window and luminance component instead of RGB components in the captured image. Additionally, we applied Sobel operator for edge detection and a threshold for binary images. Finally, object labeling with connectivity and morphological operation with open and erosion were used for ship detection. Compared with conventional methods, the proposed method is meant to be used mainly in coastal surveillance systems and monitoring systems of harbors. A system based on this method was tested for both stationary and non-stationary backgrounds, and the results of the detection and tracking rates were more than 97% on average. Thousands of image frames and 20 different video sequences in both online and offline modes were tested, and an overall detection rate of 97.6% was achieved.

Approach of Car License Plate Location based on Improved Sobel Operator with the Combination of Mathematical Morphology

Plate license locating is the key process in vehicle license plate recognition for automated transport system. It will affect the accuracy of recognition directly. License plate area contains rich edge and texture information. In this paper, improved Sobel operator is used to detecting the edge of the car image. This paper presents a new approach of extracting car license plate from car images based on morphology. Based on the information of width and height of car license plate, and space between characters of the car license plate, we design a new structural elements for morphological operations used to the edge image of car license plate, which can obtain several candidate areas. Finally, the criterions basing on the ratio of plate area, ratio of height to width and projections are used in comprehensive analysis,and the real license plate area can be located accurately. The improved Sobel operator method is helpful to reveal distinct characteristics for some edge features, efficiently with high speed processing. Compared with the classical edge detection operators edge detection algorithm, the improved algorithm is more advantage, the quality and speed of the picture edge extraction significantly improved. Experimental results show that this method is simple, accurate.It can satisfy the real time need of license location demand, and can make the preparation for the following character identification.