scholarly journals A potential crack region method to detect crack using image processing of multiple thresholding

2022 ◽  
Author(s):  
Cheng Chen ◽  
Hyungjoon Seo ◽  
ChangHyun Jun ◽  
Yang Zhao
Keyword(s):  
Treatment Algorithm ◽  
Histogram Equalization ◽  
Pavement Distress ◽  
Region Method ◽  
Road Pavement ◽  
Traffic Lane ◽  
Local Threshold ◽  
Global Threshold ◽  
Segmentation Methods ◽  
Pre Treatment

AbstractIn this paper, a potential crack region method is proposed to detect road pavement cracks by using the adaptive threshold. To reduce the noises of the image, the pre-treatment algorithm was applied according to the following steps: grayscale processing, histogram equalization, filtering traffic lane. From the image segmentation methods, the algorithm combines the global threshold and the local threshold to segment the image. According to the grayscale distribution characteristics of the crack image, the sliding window is used to obtain the window deviation, and then, the deviation image is segmented based on the maximum inter-class deviation. Obtain a potential crack region and then perform a local threshold-based segmentation algorithm. Real images of pavement surface were used at the Su Tong Li road in Suzhou, China. It was found that the proposed approach could give a more explicit description of pavement cracks in images. The method was tested on 509 images of the German asphalt pavement distress (Gap) dataset: The test results were found to be promising (precision = 0.82, recall = 0.81, F1 score = 0.83).

scholarly journals COMPARATIVE EVALUATION OF PAVEMENT CRACK DETECTION USING KERNEL-BASED TECHNIQUES IN ASPHALT ROAD SURFACES

2016 ◽  
Vol XLI-B1 ◽  
pp. 689-694 ◽  
Author(s):  
A. Miraliakbari ◽  
S. Sok ◽  
Y. O. Ouma ◽  
M. Hahn
Keyword(s):  
Comparative Evaluation ◽  
Crack Detection ◽  
Pavement Condition ◽  
Pavement Distress ◽  
The Road ◽  
Road Pavement ◽  
Segmentation Methods ◽  
Increasing Demand ◽  
Rotational Invariant ◽  
Pavement Crack Detection

With the increasing demand for the digital survey and acquisition of road pavement conditions, there is also the parallel growing need for the development of automated techniques for the analysis and evaluation of the actual road conditions. This is due in part to the resulting large volumes of road pavement data captured through digital surveys, and also to the requirements for rapid data processing and evaluations. In this study, the Canon 5D Mark II RGB camera with a resolution of 21 megapixels is used for the road pavement condition mapping. Even though many imaging and mapping sensors are available, the development of automated pavement distress detection, recognition and extraction systems for pavement condition is still a challenge. In order to detect and extract pavement cracks, a comparative evaluation of kernel-based segmentation methods comprising line filtering (LF), local binary pattern (LBP) and high-pass filtering (HPF) is carried out. While the LF and LBP methods are based on the principle of rotation-invariance for pattern matching, the HPF applies the same principle for filtering, but with a rotational invariant matrix. With respect to the processing speeds, HPF is fastest due to the fact that it is based on a single kernel, as compared to LF and LBP which are based on several kernels. Experiments with 20 sample images which contain linear, block and alligator cracks are carried out. On an average a completeness of distress extraction with values of 81.2%, 76.2% and 81.1% have been found for LF, HPF and LBP respectively.

scholarly journals COMPARATIVE EVALUATION OF PAVEMENT CRACK DETECTION USING KERNEL-BASED TECHNIQUES IN ASPHALT ROAD SURFACES

2016 ◽  
Vol XLI-B1 ◽  
pp. 689-694
Author(s):  
A. Miraliakbari ◽  
S. Sok ◽  
Y. O. Ouma ◽  
M. Hahn
Keyword(s):  
Comparative Evaluation ◽  
Crack Detection ◽  
Pavement Condition ◽  
Pavement Distress ◽  
The Road ◽  
Road Pavement ◽  
Segmentation Methods ◽  
Increasing Demand ◽  
Rotational Invariant ◽  
Pavement Crack Detection

With the increasing demand for the digital survey and acquisition of road pavement conditions, there is also the parallel growing need for the development of automated techniques for the analysis and evaluation of the actual road conditions. This is due in part to the resulting large volumes of road pavement data captured through digital surveys, and also to the requirements for rapid data processing and evaluations. In this study, the Canon 5D Mark II RGB camera with a resolution of 21 megapixels is used for the road pavement condition mapping. Even though many imaging and mapping sensors are available, the development of automated pavement distress detection, recognition and extraction systems for pavement condition is still a challenge. In order to detect and extract pavement cracks, a comparative evaluation of kernel-based segmentation methods comprising line filtering (LF), local binary pattern (LBP) and high-pass filtering (HPF) is carried out. While the LF and LBP methods are based on the principle of rotation-invariance for pattern matching, the HPF applies the same principle for filtering, but with a rotational invariant matrix. With respect to the processing speeds, HPF is fastest due to the fact that it is based on a single kernel, as compared to LF and LBP which are based on several kernels. Experiments with 20 sample images which contain linear, block and alligator cracks are carried out. On an average a completeness of distress extraction with values of 81.2%, 76.2% and 81.1% have been found for LF, HPF and LBP respectively.

scholarly journals Detection and Classification of Cancer from Microscopic Biopsy Images Using Clinically Significant and Biologically Interpretable Features

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Rajesh Kumar ◽  
Rajeev Srivastava ◽  
Subodh Srivastava
Keyword(s):  
Texture Features ◽  
Histogram Equalization ◽  
Gray Level ◽  
Segmentation Methods ◽  
Segmented Images ◽  
Clinically Significant ◽  
Classification Of Images ◽  
Nearest Neighborhood ◽  
Neighborhood Method

A framework for automated detection and classification of cancer from microscopic biopsy images using clinically significant and biologically interpretable features is proposed and examined. The various stages involved in the proposed methodology include enhancement of microscopic images, segmentation of background cells, features extraction, and finally the classification. An appropriate and efficient method is employed in each of the design steps of the proposed framework after making a comparative analysis of commonly used method in each category. For highlighting the details of the tissue and structures, the contrast limited adaptive histogram equalization approach is used. For the segmentation of background cells, k-means segmentation algorithm is used because it performs better in comparison to other commonly used segmentation methods. In feature extraction phase, it is proposed to extract various biologically interpretable and clinically significant shapes as well as morphology based features from the segmented images. These include gray level texture features, color based features, color gray level texture features, Law’s Texture Energy based features, Tamura’s features, and wavelet features. Finally, the K-nearest neighborhood method is used for classification of images into normal and cancerous categories because it is performing better in comparison to other commonly used methods for this application. The performance of the proposed framework is evaluated using well-known parameters for four fundamental tissues (connective, epithelial, muscular, and nervous) of randomly selected 1000 microscopic biopsy images.

Nausea and vomiting

2019 ◽  
pp. 531-538
Keyword(s):  
Treatment Algorithm ◽  
Nausea And Vomiting ◽  
Evidence Based ◽  
Nursing Management ◽  
Pharmacological Management ◽  
Chemotherapy Drugs ◽  
Wide Range ◽  
Detailed Assessment ◽  
Anticipatory Nausea And Vomiting ◽  
Pre Treatment

Nausea and vomiting are common and distressing symptoms of cancer and its treatments. Treatment-related nausea and vomiting are covered in depth, including pre-treatment assessment, the emetogenic level of chemotherapy drugs, and pharmacological management of chemotherapy-induced nausea and vomiting. An evidence-based treatment algorithm is described, covering the wide range of possible anti-emetics. Non-pharmacological options are also described. There is also a section on anticipatory nausea and vomiting. Nausea and vomiting in advanced cancer are covered separately. The multifactorial nature of this is discussed, with a focus on different anti-emetic regimes, as well as nursing management, including detailed assessment and ongoing nutritional and psychological support.

scholarly journals Pavement Distress Detection Methods: A Review

2018 ◽  
Vol 3 (4) ◽  
pp. 58 ◽  
Author(s):  
Antonella Ragnoli ◽  
Maria De Blasiis ◽  
Alessandro Di Benedetto
Keyword(s):  
Data Collection ◽  
Operating Cost ◽  
Pavement Management ◽  
Point Of View ◽  
Detection Methods ◽  
Technical Improvement ◽  
Pavement Distress ◽  
The Road ◽  
Road Pavement ◽  
Pavement Distress Detection

The road pavement conditions affect safety and comfort, traffic and travel times, vehicles operating cost, and emission levels. In order to optimize the road pavement management and guarantee satisfactory mobility conditions for all road users, the Pavement Management System (PMS) is an effective tool for the road manager. An effective PMS requires the availability of pavement distress data, the possibility of data maintenance and updating, in order to evaluate the best maintenance program. In the last decade, many researches have been focused on pavement distress detection, using a huge variety of technological solutions for both data collection and information extraction and qualification. This paper presents a literature review of data collection systems and processing approach aimed at the pavement condition evaluation. Both commercial solutions and research approaches have been included. The main goal is to draw a framework of the actual existing solutions, considering them from a different point of view in order to identify the most suitable for further research and technical improvement, while also considering the automated and semi-automated emerging technologies. An important attempt is to evaluate the aptness of the data collection and extraction to the type of distress, considering the distress detection, classification, and quantification phases of the procedure.

DOCUMENT IMAGE BINARISATION USING A SUPERVISED NEURAL NETWORK

2008 ◽  
Vol 18 (05) ◽  
pp. 405-418 ◽  
Author(s):  
ADNAN KHASHMAN ◽  
BORAN SEKEROGLU
Keyword(s):  
Neural Network ◽  
Digital Libraries ◽  
Cost Effective ◽  
Threshold Value ◽  
Document Image ◽  
Local Threshold ◽  
Global Threshold ◽  
Local Thresholding ◽  
Global Thresholding ◽  
Degraded Documents

Advances in digital technologies have allowed us to generate more images than ever. Images of scanned documents are examples of these images that form a vital part in digital libraries and archives. Scanned degraded documents contain background noise and varying contrast and illumination, therefore, document image binarisation must be performed in order to separate foreground from background layers. Image binarisation is performed using either local adaptive thresholding or global thresholding; with local thresholding being generally considered as more successful. This paper presents a novel method to global thresholding, where a neural network is trained using local threshold values of an image in order to determine an optimum global threshold value which is used to binarise the whole image. The proposed method is compared with five local thresholding methods, and the experimental results indicate that our method is computationally cost-effective and capable of binarising scanned degraded documents with superior results.

Improved Hough Transform Applying in Mobile Robot Vision Navigation

2012 ◽  
Vol 232 ◽  
pp. 408-413
Author(s):  
Yin Ping Jiang ◽  
Xian Xian Zhang ◽  
Xiao Peng Fu
Keyword(s):  
Mobile Robot ◽  
Hough Transform ◽  
Robot Vision ◽  
Geometric Figure ◽  
Vision Navigation ◽  
Line Extraction ◽  
Local Threshold ◽  
Global Threshold ◽  
Image Quality Reduction ◽  
Mobile Robot Vision

This paper mainly discusses that in mobile robot vision navigation system, by using the improved Hough transform, we can improve the accuracy of line extraction and therefore avoid the image quality reduction caused by noise points. Considering the limitations of the standard Hough transform, we come up with a method with which we will accumulates the H (ρ, θ) through distributing the increment value, set a global threshold to shun the pointless measurements, eliminate the false lines by comparing θ difference between tow arbitrary lines, find the peaks by using rectangle window, and set a local threshold to eliminate false peaks. In this way, we can gain a method superior to the standard Hough transform which works better in extracting lines in application. The experiments show that this method can not only extract line features of geometric figure effectively in brief background, but also eliminate the iterative lines efficiently.

scholarly journals DESAIN TEBAL PERKERASAN TERHADAP VOLUME LALU LINTAS PADA KONDISI TANAH RAWA JALAN LABUHA–TOMORI BACAN KABUPATEN HALMAHERA SELATAN

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
Chairul Anwar ◽  
M Taufik Yudha Saputra
Keyword(s):  
Flexible Pavement ◽  
Traffic Volume ◽  
Pavement Design ◽  
Distribution Factor ◽  
The Road ◽  
Road Pavement ◽  
Traffic Lane ◽  
Road Type ◽  
Median Width ◽  
Pavement Thickness

This research was carried out on Jalan Oesman Shah on Labuha-Tomori Road. In determiningflexible pavement thickness, based on the results of analysis and calculation of PavementThickness Design Against Traffic Volume on Swamp Conditions on the Labuha-Tomori RoadSection using the Road Pavement Design Manual Number 02 / M / BM / 2013. Based on theexisting plan, this Labuha-Tomori road section in South Halmahera Regency, North MalukuProvince is an arterial road with 2-lane 2-way road type using the median (2/2 UD), plan width of10 meters, width of existing traffic lane 4, 5 meters, median width of 1 meter, and plannedshoulder width 2.40 m. Based on the results of the analysis of growth rates obtained traffic growthrates of 33.066% over the life of the plan, determining the distribution factor of the lane and thecapacity of the lane of lane 1 and the vehicle in the design lane taken 100%. The equivalent loadfactor can be determined using the value of VDF (Vehicle Damage Factor) according to thesurvey results in the field. The traffic volume plan to determine the CESA4 value = 18,835,021.85= 18.84 million is used for the selection of pavement types while the CESA5 value =33,903,039.33 = 33.90 million is used to determine the type of flexible pavement based on thedesign chart provided in The Road Pavement Design Manual Number 02 / M / BM / 2013, ishighly emphasized in terms of the improvement of subgrade, by looking at the condition of theCBR of the subgrade and CESA5 which will be received by pavement. So if the pavement CBR is5.20% and CESA5 is 33.90 Million, the flexible pavement design is of 2 kinds in the design ofpavement thickness: AC - WC = 4 cm, AC - BC = 15.5 cm, CTB = 15 cm, LPA Class A = 15 cm,Choice of 10 cm and AC - WC = 4 cm, AC - BC = 6 cm, AC BASE = 18 cm, LPA = 30 cm, Choiceof Stock = 10 cm, and Subgrade = 5.20%

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