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 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 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.

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 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

2018 ◽  
pp. 1686-1708 ◽  
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.

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.

Comparative Analysis of Edge Detection Methods and its Implementation using UTLP Kit

2016 ◽  
Vol 3 (2) ◽  
pp. 26
Author(s):  
HEMALATHA R. ◽  
SANTHIYAKUMARI N. ◽  
MADHESWARAN M. ◽  
SURESH S. ◽  
◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Edge Detection ◽  
Detection Methods

scholarly journals Indirectly Supervised Anomaly Detection of Clinically Meaningful Health Events from Smart Home Data

2021 ◽  
Vol 12 (2) ◽  
pp. 1-18
Author(s):  
Jessamyn Dahmen ◽  
Diane J. Cook
Keyword(s):  
Anomaly Detection ◽  
Time Series Data ◽  
Bayesian Optimization ◽  
Detection Methods ◽  
Series Data ◽  
Detection Techniques ◽  
Health Events ◽  
Warm Start ◽  
Health Related ◽  
Unsupervised Algorithms

Anomaly detection techniques can extract a wealth of information about unusual events. Unfortunately, these methods yield an abundance of findings that are not of interest, obscuring relevant anomalies. In this work, we improve upon traditional anomaly detection methods by introducing Isudra, an Indirectly Supervised Detector of Relevant Anomalies from time series data. Isudra employs Bayesian optimization to select time scales, features, base detector algorithms, and algorithm hyperparameters that increase true positive and decrease false positive detection. This optimization is driven by a small amount of example anomalies, driving an indirectly supervised approach to anomaly detection. Additionally, we enhance the approach by introducing a warm-start method that reduces optimization time between similar problems. We validate the feasibility of Isudra to detect clinically relevant behavior anomalies from over 2M sensor readings collected in five smart homes, reflecting 26 health events. Results indicate that indirectly supervised anomaly detection outperforms both supervised and unsupervised algorithms at detecting instances of health-related anomalies such as falls, nocturia, depression, and weakness.

scholarly journals Evaluating effects of focal length and viewing angle in a comparison of recent face landmark and alignment methods

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Xiang Li ◽  
Jianzheng Liu ◽  
Jessica Baron ◽  
Khoa Luu ◽  
Eric Patterson
Keyword(s):  
Focal Length ◽  
Ground Truth ◽  
Detection Methods ◽  
Viewing Angle ◽  
Deep Convolutional Neural Networks ◽  
Landmark Detection ◽  
High Performing ◽  
Detection Techniques ◽  
And Performance ◽  
Lens Focal Length

AbstractRecent attention to facial alignment and landmark detection methods, particularly with application of deep convolutional neural networks, have yielded notable improvements. Neither these neural-network nor more traditional methods, though, have been tested directly regarding performance differences due to camera-lens focal length nor camera viewing angle of subjects systematically across the viewing hemisphere. This work uses photo-realistic, synthesized facial images with varying parameters and corresponding ground-truth landmarks to enable comparison of alignment and landmark detection techniques relative to general performance, performance across focal length, and performance across viewing angle. Recently published high-performing methods along with traditional techniques are compared in regards to these aspects.

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