scholarly journals Analysis of a robust edge detection system in different color spaces using color and depth images

2019 ◽  
Vol 43 (4) ◽  
pp. 632-646
Author(s):  
S.M.H. Mousavi ◽  
V. Lyashenko ◽  
V.B.S. Prasath
Keyword(s):  
Edge Detection ◽  
Mean Squared Error ◽  
Detection System ◽  
Detection Methods ◽  
Morphological Operations ◽  
Color Spaces ◽  
Detection Techniques ◽  
Depth Data ◽  
Depth Images ◽  
Different Color

Edge detection is very important technique to reveal significant areas in the digital image, which could aids the feature extraction techniques. In fact it is possible to remove un-necessary parts from image, using edge detection. A lot of edge detection techniques has been made already, but we propose a robust evolutionary based system to extract the vital parts of the image. System is based on a lot of pre and post-processing techniques such as filters and morphological operations, and applying modified Ant Colony Optimization edge detection method to the image. The main goal is to test the system on different color spaces, and calculate the system’s performance. Another novel aspect of the research is using depth images along with color ones, which depth data is acquired by Kinect V.2 in validation part, to understand edge detection concept better in depth data. System is going to be tested with 10 benchmark test images for color and 5 images for depth format, and validate using 7 Image Quality Assessment factors such as Peak Signal-to-Noise Ratio, Mean Squared Error, Structural Similarity and more (mostly related to edges) for prove, in different color spaces and compared with other famous edge detection methods in same condition. Also for evaluating the robustness of the system, some types of noises such as Gaussian, Salt and pepper, Poisson and Speckle are added to images, to shows proposed system power in any condition. The goal is reaching to best edges possible and to do this, more computation is needed, which increases run time computation just a bit more. But with today’s systems this time is decreased to minimum, which is worth it to make such a system. Acquired results are so promising and satisfactory in compare with other methods available in validation section of the paper.

A Study on Different Edge Detection Techniques in Digital Image Processing

2017 ◽  
pp. 100-122 ◽  
Author(s):  
Shouvik Chakraborty ◽  
Mousomi Roy ◽  
Sirshendu Hore
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Digital Image ◽  
Basic Feature ◽  
Detection Methods ◽  
Natural Image ◽  
Detection Techniques ◽  
Image Edges

Image segmentation is one of the fundamental problems in image processing. In digital image processing, there are many image segmentation techniques. One of the most important techniques is Edge detection techniques for natural image segmentation. Edge is a one of the basic feature of an image. Edge detection can be used as a fundamental tool for image segmentation. Edge detection methods transform original images into edge images benefits from the changes of grey tones in the image. The image edges include a good number of rich information that is very significant for obtaining the image characteristic by object recognition and analyzing the image. In a gray scale image, the edge is a local feature that, within a neighborhood, separates two regions, in each of which the gray level is more or less uniform with different values on the two sides of the edge. In this paper, the main objective is to study the theory of edge detection for image segmentation using various computing approaches.

A Review of Multiple Edge Detection in Road Lane Detection Using Improved Hough Transform

2015 ◽  
Vol 1125 ◽  
pp. 541-545 ◽  
Author(s):  
Muhamad Lazim Talib ◽  
Suzaimah Ramli
Keyword(s):  
Edge Detection ◽  
Hough Transform ◽  
Autonomous Navigation ◽  
Traffic Accidents ◽  
Detection System ◽  
Lane Detection ◽  
Safe Driving ◽  
Driving Experience ◽  
Detection Techniques ◽  
Selection Of

Lane detection system for the driver of the car is an important issue for the inquiry as a platform for safe driving experience. Implementation of this system is trying to investigate the possibility of traffic accidents, monitor the efficiency of the movement and position of the car contributes to the development of autonomous navigation technology. The purpose of this study is to get the best selection of banks in a better Hough transform technique to detect lane roads using edge detection techniques. For this study, Canny, Sobel and Prewitt edge detection is used as a trial. Selection of the best edge detection was using neural network techniques. Improved Hough Transform is used to extract features of a structured road. Point area near the straight line model adopted to accelerate the speed of calculation data and find the appropriate line. Prior knowledge is used in the process of finding a path to efficiently reduce the Hough space efficiently, thereby increasing the resistance by increasing the processing speed. Experiments provide good results in detecting straight and smooth fair curvature lane on highway even the hallways are painted shadows. Data from the lane highways have been taken in video format. Experiments have been done using an edge detection technique of choice in each scenario, and found that the best method of producing high accuracy of detection is to use intelligent edge detector. In this way, other people will be the best in certain cases scenarios lane highway.

scholarly journals MSB based Cellular Automata for Edge Detection

2019 ◽  
Vol 8 (9) ◽  
pp. 1354-1358
Keyword(s):  
Cellular Automata ◽  
Edge Detection ◽  
Processing Time ◽  
Detection Methods ◽  
Detection Techniques ◽  
Detection Process ◽  
Cellular Computing ◽  
Finite State ◽  
Machine Learning Applications ◽  
Cluttered Background

A vitalcrucial pre-processing phase in image processing, computer vision and machine learning applications is Edge Detection which detects boundaries of foreground and background objects in an image. Discrimination between significant edges and not so important spurious edges highly affects the accuracy of edge detection process. This paper introduces an approach for extraction of significant edges present in images based on cellular automata. Cellular automata is a finite state machine where every cell has a state. Existing edge detection methods are complex to implement so they have large processing time. These methods tend to produce non-satisfactory results for noisy images which have cluttered background. Some methods are so trivial that they miss part of true edges and some methods are so complex that they tend to give spurious edges which are not required. The advantage of using cellular computing approach is to enhance edge detection process by reducing complexity and processing time. Parallel processing makes this method fast and computationally imple. MATLAB results of proposed method performed on images from Mendeley Dataset are compared with results obtained from existing edge detection techniques by evaluation of MSE and PSNR values Results indicate promising performance of the proposed algorithm. Visually compared, the proposed method produces better results to identify edges more clearly and is intelligent enough to discard spurious edges even for cluttered and complex images

A New Precision Non-Contact Laser-Based Hybrid Measurement Methodology

2011 ◽  
Author(s):  
Devdas Shetty ◽  
Suhash Ghosh ◽  
Claudio Campana ◽  
Mustafa Atalay
Keyword(s):  
Edge Detection ◽  
Turbine Blades ◽  
Hybrid Approach ◽  
Great Accuracy ◽  
Detection Methods ◽  
Detection Techniques ◽  
Aerospace Applications ◽  
Accuracy And Precision ◽  
Measurement Methodology ◽  
Nozzle Geometries

Precise and accurate manufacturing became an obligation in aerospace industry in last decades. Uniformity of turbine blades, nozzle geometries, gaps, diameter changes and misalignment issues in turbine assemblies have to be inspected carefully in terms of quality and exactitude. Like broadly used aluminum and titanium based materials, ceramics and special coated composites are also used in aerospace applications. A wide selection of measurement methods used is based on intensity sensing and range imaging. With the recent development in advanced laser techniques, new methods that involve non contact measurement methodologies are being investigated by many industries. In addition to their accuracy and precision, speed of measurement and compactness of such systems are also of high significance. In this paper, a hybrid approach consisting of laser based triangulation, photogrammetry and edge detection techniques has been investigated to measure inner surfaces of parts that have limited access, especially where human presence is impossible. The system is capable of detecting and measuring misalignments, gaps, inclinations as well as surface variations such as cracks and dents. The system employs the accuracy and speed of measurement of triangulation systems and combines these with the mobility and cost effectiveness of photogrammetry and edge detection techniques. In addition to gap and alignment offset inspections, the methodology and the instrument enables angle measurements, detailed surface texture examinations and other inspections needed to be done inside assemblies with narrow openings, with its compact body. Additionally, a comprehensive experimental study has been conducted to show that two different edge detection methods, namely, the “Simple Edge Tool” and “Straight Edge (Rake) Tool” can be used with great accuracy and precision for such measurement purposes. With this system, any surface, whether they have a reflectance or not, can be scrutinized.

Study on the Image Segmentation Algorithm with Depth Data of Kinect

2014 ◽  
Vol 998-999 ◽  
pp. 929-933
Author(s):  
Lu Yi Li ◽  
Jun Yong Ye
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Surface Area ◽  
Segmentation Algorithm ◽  
Detection Methods ◽  
Depth Image ◽  
Normal Vector ◽  
Support Surface ◽  
Canny Operator ◽  
Depth Data

In the segmentation algorithms of the depth image, because the object and its support surface are continuous in the depth data ,the traditional method of edge detection methods can’t detect the edge between the object and its support surface. To solve this problem, the segmentation algorithm of the depth image is studied in this paper. Firstly, we use canny operator to detect the edge the of depth image of the scene. Then the depth image of the scene is transformed into points of a 3-D space coordinate and normal vector is calculated for each point. The method of calculation the direction of the normal vector is used to determine the point of which belongs to the support surface area, which determine the support surface area of the scene. Finally, we detect image edge of the image that the support surface area is extracted, and fuse the result of canny operator edge detection and edge of the image that the support surface area is extracted. Experiments show that the segmentation algorithm works well, which the problem of detection the edge between the support surface area and the object and can also achieve a good depth image segmentation.

scholarly journals Performance Study of Soft Local Binary Pattern over Local Binary Pattern under Noisy Images

2016 ◽  
Vol 6 (3) ◽  
pp. 1161 ◽  
Author(s):  
Sabina Yasmin ◽  
Md. Masud Rana
Keyword(s):  
Face Recognition ◽  
Edge Detection ◽  
Local Binary Pattern ◽  
Recognition Rate ◽  
Quality Measurement ◽  
Computation Time ◽  
Detection Methods ◽  
Performance Study ◽  
Detection Techniques ◽  
Noisy Condition

In this paper, the performance of soft local binary pattern (SLBP) method has been investigated with edge detection techniques for face recognition in case of noisy condition. Various edge detection techniques such as Canny, Robert and Log methods have been used with SLBP for recognizing faces. The results obtained using SLBP with various edge detection for noisy condition based on image quality measurement shows better recognition rate compared to the results obtained using local binary pattern (LBP). Simplified edge detection methods simplify the images as a result SLBP with edge detection requires less computation time compared with edge detection of LBP.

scholarly journals Feature Extraction for Face Recognition using Edge Detection and Thresholding

2020 ◽  
Vol 9 (7) ◽  
pp. 59-63
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Edge Detection ◽  
Detection System ◽  
Image Features ◽  
Face Identification ◽  
Morphological Operations ◽  
Illumination Changes ◽  
The Face ◽  
Edge Features

Face recognition is first and foremost step in video surveillance applications which include human behavioral analysis, event detection, border security and ATM banking. Most of the time, it is very difficult to get good facial features from the particular image frame and it often requires sophisticated algorithm for face identification and recognition. Robust face detection system is still a more challenging job because of complex environments including illumination changes, background clutter and occlusions. This article presents a novel feature extraction algorithm for face recognition using edge detection and thresholding. Initially, the incoming image is preprocessed to smoothen the image features and it is converted in to grayscale image to reduce the computational complexity of post processing steps. In feature extraction step, the image is completely iterated throughout the spatial coordinates and the edges are detected using thresholding technique. The optimum threshold for global thresholding is identified by calculating the maximum between-class variance in the given image. The extracted edge features are invariant under scale and illumination changes and thus it ensures the robust binary mask for face identification. Finally, the foreground features are obtained using morphological operations and the face is highlighted in subsequent incoming image frames. The proposed method can be deployed in public places such as malls, ATM centers and airports for security applications. Experimental results clearly indicate that the proposed approach works well under complex situations.

scholarly journals Intrusion Detection System

2020 ◽  
pp. 13-16
Author(s):  
Shivani Agrawal ◽  
Priyanka Walke ◽  
Shivam Pandit ◽  
Sangram Nevse ◽  
Sunil Deokule
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Detection Methods ◽  
Digital Information ◽  
Traditional Use ◽  
Software Application ◽  
Detection Techniques ◽  
Complete Study ◽  
Use Of Internet

Intrusion Detection System (IDS) is well-defined as a Device or software application which monitors the system activities and finds if there is any malevolent activity that has occured. Unresolved growth and traditional use of internet raises anxieties about how to connect and protect the digital information securely. In today's world hackers use different types of attacks for getting the valued information. Many of the intrusion detection techniques, methods and procedures help to perceive those several attacks. The main objective of this paper is to provide a complete study about the intrusion detection, sorts of intrusion detection methods, types of attacks, diverse tools and practices, research needs, challenges and finally change the IDS Tool for Research Purpose That tool are capable of perceiving and prevent the attack from the intruder.

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