scholarly journals Boosted Edge Detection Algorithm for Unstructured Environment in Document Using Optimized Text Region Detection

2019 ◽  
Vol 8 (S1) ◽  
pp. 50-53
Author(s):  
N. P. Revathy ◽  
S. Janarthanam ◽  
S. Sukumaran
Keyword(s):  
Edge Detection ◽  
Detection Efficiency ◽  
Optimal Solution ◽  
Document Retrieval ◽  
Detection Algorithm ◽  
Document Image ◽  
Detection Methods ◽  
Computational Time ◽  
Document Images ◽  
Unstructured Environment

Document images are more popular in today’s world and being made available over the internet for Information retrieval. The document images becomes a difficult task compared with digital texts and edge detection is an important task in the document image retrieval, edge detection indicates to the process of finding sharp discontinuation of characters in the document images. The single edge detection methods causing the weak gradient and edge missing problems adopts the method of combining global with local edge detection to extract edge. The global edge detection obtains the whole edges and uses to improve adaptive smooth filter algorithm based on canny operator. These combinations increase the detection efficiency and reduce the computational time. In addition, the proposed algorithm has been tested through real-time document retrieval system to detect the edges in unstructured environment and generate 2D maps. These maps contain the starting and destination points in addition to current positions of the objects. This proposed work enhancing the searching ability of the document to move towards the optimal solution and to verify the capability in terms of detection efficiency.

scholarly journals Low Complexity Lane Detection Methods for Light Photometry System

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1665
Author(s):  
Jakub Suder ◽  
Kacper Podbucki ◽  
Tomasz Marciniak ◽  
Adam Dąbrowski
Keyword(s):  
Edge Detection ◽  
Optimal Path ◽  
Color Space ◽  
Low Complexity ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Detection Methods ◽  
Raspberry Pi ◽  
Line Detection ◽  
Hsv Color Space

The aim of the paper was to analyze effective solutions for accurate lane detection on the roads. We focused on effective detection of airport runways and taxiways in order to drive a light-measurement trailer correctly. Three techniques for video-based line extracting were used for specific detection of environment conditions: (i) line detection using edge detection, Scharr mask and Hough transform, (ii) finding the optimal path using the hyperbola fitting line detection algorithm based on edge detection and (iii) detection of horizontal markings using image segmentation in the HSV color space. The developed solutions were tuned and tested with the use of embedded devices such as Raspberry Pi 4B or NVIDIA Jetson Nano.

A New Medical Image Edge Detection Algorithm Based on BC-ACO

2017 ◽  
Vol 31 (03) ◽  
pp. 1757002
Author(s):  
Jingyu Zhang ◽  
Jianfu Teng ◽  
Yu Bai
Keyword(s):  
Edge Detection ◽  
Medical Image ◽  
Ant Colony Algorithm ◽  
Bacterial Chemotaxis ◽  
Optimal Solution ◽  
Detection Algorithm ◽  
Ant Colony ◽  
Image Edge Detection ◽  
Image Edge ◽  
Improved Ant Colony Algorithm

Taking the improved ant colony algorithm based on bacterial chemotaxis as a means, this paper proposes one new swarm intelligence optimization algorithm to solve the medical image edge detection problem. The improved ant colony algorithm based on bacterial chemotaxis mainly aims at the shortcoming that the basic ant colony algorithm lacks initial pheromone, and combines bacterial chemotaxis algorithm with basic ant colony algorithm. Firstly, feasible better solution can be found through bacterial chemotaxis algorithm and fed back as initial pheromone. Then ant colony algorithm is implemented to search for the global optimal solution. The algorithm test indicates that the improved ant colony algorithm is more effective in the aspects of searching precision, reliability, optimization speed and stability compared with basic ant colony algorithm. Finally, the improved ant colony algorithm is applied into the edge detection of medical image. It can be seen from the computer simulation that compared with other operators and basic ant colony algorithm on the issue of solving medical image edge detection, the improved ant colony algorithm has superiority and the detected edge is clearer.

A ROBUST SYSTEM FOR THRESHOLDING AND SKEW DETECTION IN MIXED TEXT/GRAPHICS DOCUMENTS

2005 ◽  
Vol 05 (02) ◽  
pp. 247-265 ◽  
Author(s):  
ADNAN AMIN ◽  
SUE WU
Keyword(s):  
Automatic System ◽  
Real Life ◽  
Detection Algorithm ◽  
Document Image ◽  
Superior Performance ◽  
Document Images ◽  
Skew Detection ◽  
Robust System ◽  
Graphic Images ◽  
Scanned Images

This article presents an automatic system that takes in grayscale scanned images, which could be mixed text/graphic documents, and performs thresholding and skew detection on the document images. The system consists of two major components; multistage thresholding and skew detection. The proposed skew detection algorithm has no restriction on detectable angle range and does not rely on large blocks of text. It works well on textual document images, graphical images and mixed text and graphic images. The performance of the systems was evaluated using over 60 images that consist of real life documents like envelopes and artificial mixed text/graphic icons. The superior performance of thresholding is clear compared to other techniques from the evaluation. The skew detection algorithm is robust when compared with other methods when very few text lines are present in the document image.

Application of Bidimensional Empirical Mode Decomposition to Medical Liquid Opacity Detection

2011 ◽  
Vol 128-129 ◽  
pp. 530-533
Author(s):  
Jian Wan ◽  
Yuan Peng Diao ◽  
Dong Mei Yan ◽  
Qiang Guo ◽  
Zhen Shen Qu
Keyword(s):  
Edge Detection ◽  
Empirical Mode Decomposition ◽  
External Environment ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Edge Detection Algorithm ◽  
Mode Decomposition ◽  
Simulation Results ◽  
Bidimensional Empirical Mode Decomposition

A Robert operator edge detection algorithm based on Bidimensional Empirical Mode Decomposition (BEMD) to detect medical liquid opacity is proposed. This method can effectively resolve the problem that traditional Robert operator edge detection can be easily effected by noise, and it also has certain effects on restraining external environment influence. The simulation results show that, compare with traditional medical liquid opacity detection methods, the proposed method could achieve higher detection accuracy, and has a certain theory and application value.

scholarly journals A Novel Edge Detection Algorithm for Mobile Robot Path Planning

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Rami Al-Jarrah ◽  
Mohammad Al-Jarrah ◽  
Hubert Roth
Keyword(s):  
Indoor Environment ◽  
Detection Efficiency ◽  
Robot Navigation ◽  
Optimal Path ◽  
Detection Algorithm ◽  
Computational Time ◽  
Robot Path Planning ◽  
Searching Ability ◽  
Fuzzy Image ◽  
Robot Path

A novel detection algorithm for vision systems has been proposed based on combined fuzzy image processing and bacterial algorithm. This combination aims to increase the detection efficiency and reduce the computational time. In addition, the proposed algorithm has been tested through real-time robot navigation system, where it has been applied to detect the robot and obstacles in unstructured environment and generate 2D maps. These maps contain the starting and destination points in addition to current positions of the robot and obstacles. Moreover, the genetic algorithm (GA) has been modified and applied to produce time-based trajectory for the optimal path. It is based on proposing and enhancing the searching ability of the robot to move towards the optimal path solution. Many scenarios have been adopted in indoor environment to verify the capability of the new algorithm in terms of detection efficiency and computational time.

scholarly journals A Mechanical Parts Image Segmentation Method Against Illumination for Industry

2020 ◽  
Author(s):  
Xin Xie ◽  
Tiancheng Wan ◽  
Bin Wang ◽  
Lei Xu ◽  
Xinlei Li
Keyword(s):  
Edge Detection ◽  
Learning Strategy ◽  
Detection Efficiency ◽  
Classical Method ◽  
Specular Reflection ◽  
Semantic Segmentation ◽  
Detection Methods ◽  
Mechanical Parts ◽  
Image Edge ◽  
Segmentation Task

Abstract Most of the current image edge detection methods rely on manually features to extract the edge, there are often false and missed detections when the image has adverse interference. The surface of mechanical parts is smooth, when taking photos in the industrial field, it is easy to have specular reflection and shadow at the same time, which will affect the edge detection results. In order to achieve excellent edge detection performance, we propose a semantic segmentation model based on encoder-decoder structure. It adopts joint learning strategy, using two decoders to process image decomposition task and segmentation task respectively, and sharing their parameters to eliminate the influence of illumination, so as to improve segmentation performance. In the training phase, the asymmetric convolution and BN fusion are combined to improve the detection efficiency. In addition, we built a gear part dataset for experimentation. The result shows that in the task of edge detection of mechanical parts affected by illumination, our method has better performance than classical method.

scholarly journals Gravitation-Based Edge Detection for Hyperspectral Images

2017 ◽  
Author(s):  
Genyun Sun ◽  
Aizhu Zhang ◽  
Jinchang Ren ◽  
Jingsheng Ma ◽  
Peng Wang ◽  
...  
Keyword(s):  
Edge Detection ◽  
Feature Space ◽  
Detection Algorithm ◽  
Hyperspectral Images ◽  
Detection Methods ◽  
Gravitational Potential Energy ◽  
Neighboring Pixel ◽  
Key Issues ◽  
Edge Detection Method ◽  
Edge Features

Edge detection is one of the key issues in the field of computer vision and remote sensing image analysis. Although many different edge-detection methods have been proposed for gray-scale, color, and multispectral images, they still face difficulties when extracting edge features from hyperspectral images (HSIs) that contain a large number of bands with very narrow gap in the spectral domain. Inspired by the clustering characteristic of the gravitation, a novel edge-detection algorithm for HSIs is presented in this paper. In the proposed method, we first construct a joint feature space by combining the spatial and spectral features. Each pixel of HSI is assumed to be a celestial object in the joint feature space, which exerts gravitational force to each of its neighboring pixel. Accordingly, each object travels in the joint feature space until it reaches a stable equilibrium. At the equilibrium, the image is smoothed and the edges are enhanced, where the edge pixels can be easily distinguished by calculating the gravitational potential energy. The proposed edge-detection method is tested on several benchmark HSIs and the obtained results were compared with those of three state-of-the-art approaches. The experimental results confirm the efficacy of the proposed method

scholarly journals Implementing canny edge detection algorithm for noisy image

2020 ◽  
Vol 9 (4) ◽  
pp. 1404-1410
Author(s):  
Ehsan Akbari Sekehravani ◽  
Eduard Babulak ◽  
Mehdi Masoodi
Keyword(s):  
Edge Detection ◽  
Median Filter ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Canny Edge Detection ◽  
Edge Detection Algorithm ◽  
Canny Edge ◽  
Significant Stage ◽  
Salt And Pepper ◽  
Canny Edge Detection Algorithm

Edge detection is a significant stage in different image processing operations like pattern recognition, feature extraction, and computer vision. Although the Canny edge detection algorithm exhibits high precision is computationally more complex contrasted to other edge detection methods. Due to the traditional Canny algorithm uses the Gaussian filter, which gives the edge detail represents blurry also its effect in filtering salt-and-pepper noise is not good. In order to resolve this problem, we utilized the median filter to maintain the details of the image and eliminate the noise. This paper presents implementing and enhance the accuracy of Canny edge detection for noisy images. Results present that this proposed method can definitely overcome noise disorders, preserve the edge useful data, and likewise enhance the edge detection precision.

Image Edge Detection Based on Neutrosophic Set Approach Combined with Chan–Vese Algorithm

2019 ◽  
Vol 33 (03) ◽  
pp. 1954008 ◽  
Author(s):  
Eser Sert ◽  
Ahmet Alkan
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Signal To Noise Ratio ◽  
Optimization Methods ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Neutrosophic Set ◽  
Neutrosophic Logic ◽  
Neutrosophic Statistics ◽  
Cv Model

Since edge detection is a field of study used by various disciplines, it is of vital importance to calculate it accuretly. In addition, an edge detection algorithm may be involved in many image processing phases. A recent and contemporary approach, neutrosophy is based on neutrosophic logic, neutrosophic probability, neutrosophic set and neutrosophic statistics. This method yields better results compared to various other optimization methods. Neutrosophic Set (NS) is based on the origin, nature and scope of neutralities. In NS, problems are separated into true, false and indeterminacy subsets. It helps solve indeterminate situations effectively. It has recently been used in the field of image processing as indeterminate situations are also encountered in this field. Chan–Vese (CV) model is one of the successful region-based segmentation methods. The present study proposes a new NS-based edge detection method using CV algorithm. The proposed method combines the philosophical view of NS with successful segmentation characteristics of CV model. Obtained edge detection results are compared with different edge detection methods. The performances of each method are analyzed by using Figure of Merit (FOM) and Peak Signal-To-Noise Ratio (PSNR). The results suggest that the proposed method displays a better performance assessment compared to the used well-known methods.

Research on Crack Detection Algorithm of Mining Car Baffle

2020 ◽  
Vol 13 ◽  
Author(s):  
Weiwei Li ◽  
Fanlei Yan
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Crack Detection ◽  
Low Cost ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Crack Identification ◽  
Actual Length ◽  
Computer Vision Technology ◽  
Sobel Edge Detection

Introduction: Image processing technology is widely used for crack detection. This technology is to build a data acquisition system and use computer vision technology for image analysis. Because of its simplicity in the processing, many of the image processing detection methods were proposed. It is relatively easy to deploy and has low cost. Method: The heterogeneity of the external light usually changes the authenticity of each target in the image, which will seriously cause the experiment to fail. At this time, the image needs to be processed by the gamma transform.Based on the analysis of the characteristics of the image of the mine car baffle, this paper improves the Gamma transform, and uses the improved Gamma transform to enhance the image. Result: We can conclude that the algorithm in this paper can accurately detect crack areas with an actual width greater than 1.2 mm, and the error between the detected crack length and the actual length is between (-2, 2) mm. In practice, this error is completely acceptable. Discussion: To compare the performance of a new crack detection method with existing methods, are used. The two most well-known traditional methods, Canny and Sobel edge detection, are selected. Although the Sobel edge detection provides some crack information. The texture of the surface of the mine cart baffle detected has caused great interference to the crack identification. Conclusion: If the cracks appearing on the mine car baffle are not found in time, they often cause accidents. Therefore, effective crack detection must be performed. If manual inspection is adopted for crack detection, it will be labor-intensive and easy to miss inspection. In order to reduce the labor of crack detection of mine cars and improve the accuracy of detection, this paper, based on the detection platform built, performs preprocessing, image enhancement, and convolution operations on the collected crack images of the mine car baffle.

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