scholarly journals Asphalt Mixture Segregation Detection: Digital Image Processing Approach

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Mohamadtaqi Baqersad ◽  
Amirmasoud Hamedi ◽  
Mojtaba Mohammadafzali ◽  
Hesham Ali
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Visual Inspection ◽  
Asphalt Mixture ◽  
Core Density ◽  
Inspection Method ◽  
Processing Approach ◽  
Circular Track ◽  
Image Frequency

Segregation determination in the asphalt pavement is an issue causing many disputes between agencies and contractors. The visual inspection method has commonly been used to determine pavement texture and in-place core density test used for verification. Furthermore, laser-based devices, such as the Florida Texture Meter (FTM) and the Circular Track Meter (CTM), have recently been developed to evaluate the asphalt mixture texture. In this study, an innovative digital image processing approach is used to determine pavement segregation. In this procedure, the standard deviation of the grayscale image frequency histogram is used to determine segregated regions. Linear Discriminate Analysis (LDA) is then implemented on the obtained standard deviations from image processing to classify pavements into the segregated and nonsegregated areas. The visual inspection method is utilized to verify this method. The results have demonstrated that this new method is a robust tool to determine segregated areas in newly paved FC9.5 pavement types.

Gradation measurement of asphalt mixture by X-Ray CT images and digital image processing methods

Measurement ◽  
2019 ◽  
Vol 132 ◽  
pp. 377-386 ◽  
Author(s):  
Chao Xing ◽  
Huining Xu ◽  
Yiqiu Tan ◽  
Xueyan Liu ◽  
Changhong Zhou ◽  
...  
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Asphalt Mixture ◽  
Ct Images ◽  
Processing Methods ◽  
X Ray

Microstructural analysis of asphalt mixtures using digital image processing techniques

2014 ◽  
Vol 41 (1) ◽  
pp. 74-86 ◽  
Author(s):  
Ki Hoon Moon ◽  
Augusto Cannone Falchetto ◽  
Jin Hoon Jeong
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Microstructural Analysis ◽  
Asphalt Mixture ◽  
Material Model ◽  
Asphalt Mixtures ◽  
Recycled Asphalt ◽  
Air Voids ◽  
Processing Techniques

In this paper, the internal microstructure of asphalt mixture is analyzed through digital image processing (DIP) of two-dimensional asphalt mixture images. A set of 12 mixtures prepared with two binders, two air voids percentages, and different recycled asphalt pavement (RAP) contents is used. First, small asphalt mixture beams of the same size of bending beam rheometer specimens are prepared for the images acquisition. Then, based on mixture volumetric properties, a three-phase material model is obtained. Finally, 2- and 3-point correlation functions of the material phases are numerically evaluated. No significant differences were observed in the microstructure and spatial distributions of aggregates, asphalt mastic, and air voids for asphalt mixtures containing up to 40% of RAP. However, an increase in auto correlation length (ACL) was found for RAP mixtures in comparison with the conventional mixtures.

scholarly journals Mobile Screening Framework of Anterior Segment Photographed Images

2018 ◽  
Vol 7 (4.11) ◽  
pp. 85
Author(s):  
N Syahira M Zamani ◽  
Laily Azyan Ramlan ◽  
W Mimi Diyana W Zaki ◽  
Aini Hussain ◽  
Haliza Abdul Mutalib
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Anterior Segment ◽  
Qualitative Assessment ◽  
Computational Time ◽  
Future Studies ◽  
Processing Approach ◽  
Processing Platform ◽  
Mobile Screening

This work presents a qualitative measurement of anterior segment photographed images (ASPIs) to identify between normal eyes and eyes with pterygium and pinguecula through Otsu multi-thresholding approach without contrast enhancement. In addition, we also propose a mobile screening framework of ASPIs through smartphones. ASPIs were directly sent to the cloud storage once an ASPI was captured using a smartphone camera, and then each image was processed through a digital image processing approach in a processing platform. Three important steps, namely, pre-processing, image segmentation and qualitative assessment, are involved in the processing platform of the mobile screening framework. The ASPIs are pre-processed to minimise or eliminate any unwanted areas within the image. Then, these ASPIs are segmented through multi-thresholding Otsu approach with clustering number n = 3. Segmentation result shows that the accuracy of the proposed method is 87.5%, which is comparable with the previously established work that has applied three-step differencing (3SD) method. However, the proposed approach has better computational time which is six times faster than the 3SD method. These results demonstrate a remarkable effort to produce a simple but straightforward digital image processing approach to be implemented in cloud computing for future studies.                                                                                       

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