DETECTION OF CRACKS IN PAVED ROAD SURFACE USING LASER SCAN IMAGE DATA

In the current study, we developed a methodology for detecting cracks in the surface of paved road using 3D digital surface model of road created by measuring with three-dimensional laser scanner which works on the basis of the light-section method automatically. For the detection of cracks from the imagery data of the model, the background subtraction method (Rolling Ball Background Subtraction Algorithm) was applied to the data for filtering out the background noise originating from the undulation and gradual slope and also for filtering the ruts that were caused by wearing, aging and excessive use of road and other reasons. We confirmed the influence from the difference in height (depth) caused by forgoing reasons included in a data can be reduced significantly at this stage. Various parameters of ball radius were applied for checking how the result of data obtained with this process vary according to the change of parameter and it becomes clear that there are not important differences by the change of parameters if they are in a certain range radius. And then, image segmentation was performed by multi-resolution segmentation based on the object-based image analysis technique. The parameters for the image segmentation, scale, pixel value (height/depth) and the compactness of objects were used. For the classification of cracks in the database, the height, length and other geometric property are used and we confirmed the method is useful for the detection of cracks in a paved road surface.

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DETECTION OF CRACKS IN PAVED ROAD SURFACE USING LASER SCAN IMAGE DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b1-559-2016 ◽

2016 ◽

Vol XLI-B1 ◽

pp. 559-562 ◽

Cited By ~ 3

Author(s):

J. Choi ◽

L. Zhu ◽

H. Kurosu

Keyword(s):

Image Segmentation ◽

Background Subtraction ◽

Three Dimensional ◽

Laser Scanner ◽

Image Data ◽

Road Surface ◽

Surface Model ◽

Rolling Ball ◽

The Difference ◽

Paved Road

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OS4-2 Three-dimensional measurement system for road surface using laser scanner and camera

The Proceedings of Conference of Kyushu Branch ◽

10.1299/jsmekyushu.2011.129 ◽

2011 ◽

Vol 2011 (0) ◽

pp. 129-130 ◽

Cited By ~ 1

Author(s):

Kikuhito KAWASUE ◽

Shouji KONAKANO

Keyword(s):

Measurement System ◽

Three Dimensional ◽

Laser Scanner ◽

Road Surface ◽

Dimensional Measurement ◽

Three Dimensional Measurement

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The effect of radiometer placement and view on inferred directional and hemispheric radiometric temperatures of a urban canopy

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-8-1891-2015 ◽

2015 ◽

Vol 8 (2) ◽

pp. 1891-1933 ◽

Cited By ~ 4

Author(s):

C. Adderley ◽

A. Christen ◽

J. A. Voogt

Keyword(s):

Mean Squared Error ◽

Three Dimensional ◽

Urban Canopy ◽

Weather Conditions ◽

Urban Surface ◽

Surface Model ◽

View Factor ◽

Atmospheric Effects ◽

Positional Error ◽

The Difference

Abstract. Any radiometer at a fixed location has a biased view when observing a convoluted, three dimensional surface such as an urban canopy. The goal of this contribution is to determine the bias of various sensors views observing a simple urban residential neighbourhood (nadir, oblique, hemispherical) over a 24 h cycle under clear weather conditions. The error in measuring longwave radiance (L) and/or inferring surface temperatures (T0) is quantified for different times over a diurnal cycle. Panoramic time-sequential thermography (PTST) data was recorded by a thermal camera on a hydraulic mast above a residential canyon in Vancouver, BC. The dataset resolved sub-facet temperature variability of all representative urban facets in a 360° swath repetitively over a 24 h cycle. This dataset is used along with computer graphics and vision techniques to project measured fields of L for a given time and pixel onto texture sheets of a three-dimensional urban surface model at a resolution of centimetres. The resulting dataset attributes L of each pixel on the texture sheets to different urban facets and associates facet location, azimuth, slope, material, and sky view factor. The texture sheets of L are used to calculate the complete surface temperature (T0,C) and to simulate the instantaneous field of view (IFOV) of narrow and hemispheric radiometers observing the same urban surface (in absence of emissivity and atmospheric effects). The simulated directional (T0,d) and hemispheric (T0,h) radiometric temperatures inferred from various biased views are compared to T0,C. For a range of simulated off-nadir (&varphi;) and azimuth (Ω) angles, T0,d (&varphi;, Ω) and T0,C differ between −2.7 and +2.9 K over the course of the day. The effects of effective anisotropy are highest in the daytime, particularly around sunrise and sunset when different views can lead to differences in T0,d (&varphi;, Ω) that are as high as 3.5 K. For a sensor with a narrow IFOV in the nadir of the urban surface, T0,d (&varphi; = 0°) differs from T0,C by −2.2 K (day) and by +1.6 K (night). Simulations of the IFOV of hemispherical, downward-facing pyrgeometers at 270 positions show considerable variations in the measured L and inferred hemispherical radiometeric temperature T0,h as a function of both horizontal placement and height. The root mean squared error (RMSE) between different horizontal positions in retrieving outgoing longwave emittance L↑ decreased exponentially with height, and was 11.2, 6.3 and 2.0 W m−2 at 2, 3, and 5 times the mean building height zb. Generally, above 3.5 zb the horizontal positional error is less than the typical accuracy of common pyrgeometers. The average T0,h over 24 h determined from the hemispherical radiometer sufficiently above an urban surface is in close agreement with the average T0,C. However, over the course of the day, the difference between T0,h and T0,C shows an RMSE of 1.8 K (9.9 W m−2) because the relative contributions of facets within the projected IFOV of a pyrgeometer do not correspond to their fractions of the complete urban surface.

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Construction of Three-Dimensional Road Surface and Application on Interaction between Vehicle and Road

Shock and Vibration ◽

10.1155/2018/2535409 ◽

2018 ◽

Vol 2018 ◽

pp. 1-14 ◽

Cited By ~ 1

Author(s):

Lu Yongjie ◽

Huai Wenqing ◽

Zhang Junning

Keyword(s):

Quantitative Description ◽

Three Dimensional ◽

Point Contact ◽

Estimation Method ◽

Road Surface ◽

Surface Model ◽

Dynamical Process ◽

Contact Points ◽

Box Counting Dimension ◽

The Impact

The quantitative description is given to three-dimensional micro and macro self-similar characteristics of road surface from the perspective of fractal geometry using FBM stochastic midpoint displacement and diamond-square algorithm in conjunction with fractal characteristics and statistical characteristics of standard pavement determined by estimation method of box-counting dimension. The comparative analysis between reconstructed three-dimensional road surface spectrum and theoretical road surface spectrum and correlation coefficient demonstrate the high reconstruction accuracy of fractal reconstructed road spectrum. Furthermore, the bump zone is taken as an example to reconstruct a more arbitrary 3D road model through isomorphism of special road surface with stochastic road surface model. Measurement is taken to assume the tire footprint on road surface to be a rectangle, where the pressure distribution is expressed with mean stiffness, while the contact points in the contact area are replaced with a number of springs. Two-DOF vehicle is used as an example to analyze the difference between three-dimensional multipoint-and-plane contact and traditional point contact model. Three-dimensional road surface spectrum provides a more accurate description of the impact effect of tire on road surface, thereby laying a theoretical basis for studies on the dynamical process of interaction of vehicle-road surface and the road friendliness.

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ROAD EXTRACTION AND VECTORIZATION FROM AERIAL IMAGE DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b2-2020-1323-2020 ◽

2020 ◽

Vol XLIII-B2-2020 ◽

pp. 1323-1327

Author(s):

L. Zhu ◽

Y. Li ◽

H. Shimamura

Keyword(s):

Road Network ◽

Image Data ◽

Road Surface ◽

Aerial Image ◽

Surface Model ◽

Center Line ◽

Road Extraction ◽

The Road ◽

Road Map ◽

Road Network Extraction

Abstract. The objective of this study is the automatic extraction of the road network in a scene of the urban area from high resolution aerial image data. Our approach includes two stages aiming to solve two important issues respectively, i.e., an effective road extraction pipeline, and a precise vectorized road map. In the first stage, we proposed a so-called all element road model which describes a multiple-level structure of the basic road elements, i.e. intersection, central line, side lines, and road plane based on their spatial relations. An advanced road network extraction scheme was proposed to address the issues of tedious steps on segmentation, recognition and grouping, using the digital surface model (DSM) only. The key feature of the proposed approach was the cross validation of the road basic elements, which was applied all the way through the entire procedure of road extraction. In the second stage, the regularized road map was produced where center line and side lines subject to parallel and even layout rules. It gives more accurate and reliable map by utilizing both the height information of the DSM and the color information of the ortho image. Road surface was extracted from the image by region growing. Then, a regularized center line was modeled by linear regression using all the pixels of the road surface. The road width was estimated and two road side lines were modeled as the straight lines parallel with the center line. Finally, the road model was built up in terms of vectorized points and lines. The experimental results showed that the proposed approach performed satisfactorily in our test site.

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Comparison of clinical bracket point registration with 3D laser scanner and coordinate measuring machine

Dental Press Journal of Orthodontics ◽

10.1590/2176-9451.20.1.059-065.oar ◽

2015 ◽

Vol 20 (1) ◽

pp. 59-65 ◽

Cited By ~ 5

Author(s):

Mahtab Nouri ◽

Arash Farzan ◽

Ali Reza Akbarzadeh Baghban ◽

Reza Massudi

Keyword(s):

Intraclass Correlation ◽

Three Dimensional ◽

Laser Scanner ◽

Coordinate Measuring Machine ◽

Diagnostic Value ◽

Mean Difference ◽

Validity And Reliability ◽

Measuring Machine ◽

The Mean ◽

The Difference

OBJECTIVE: The aim of the present study was to assess the diagnostic value of a laser scanner developed to determine the coordinates of clinical bracket points and to compare with the results of a coordinate measuring machine (CMM). METHODS: This diagnostic experimental study was conducted on maxillary and mandibular orthodontic study casts of 18 adults with normal Class I occlusion. First, the coordinates of the bracket points were measured on all casts by a CMM. Then, the three-dimensional coordinates (X, Y, Z) of the bracket points were measured on the same casts by a 3D laser scanner designed at Shahid Beheshti University, Tehran, Iran. The validity and reliability of each system were assessed by means of intraclass correlation coefficient (ICC) and Dahlberg's formula. RESULTS: The difference between the mean dimension and the actual value for the CMM was 0.0066 mm. (95% CI: 69.98340, 69.99140). The mean difference for the laser scanner was 0.107 ± 0.133 mm (95% CI: -0.002, 0.24). In each method, differences were not significant. The ICC comparing the two methods was 0.998 for the X coordinate, and 0.996 for the Y coordinate; the mean difference for coordinates recorded in the entire arch and for each tooth was 0.616 mm. CONCLUSION: The accuracy of clinical bracket point coordinates measured by the laser scanner was equal to that of CMM. The mean difference in measurements was within the range of operator errors.

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Powerful processing to three-dimensional facial recognition using triple information

International Journal of Advances in Applied Sciences ◽

10.11591/ijaas.v9.i4.pp326-332 ◽

2020 ◽

Vol 9 (4) ◽

pp. 326

Author(s):

Mohammad Karimi Moridani ◽

Ahad Karimi Moridani ◽

Mahin Gholipour

Keyword(s):

Face Recognition ◽

Three Dimensional ◽

Laser Scanner ◽

Principal Component ◽

Support Vector ◽

The Face ◽

Principal Component Analysis Algorithm ◽

The Difference ◽

Security Surveillance ◽

The Trinity

Face Detection plays a crucial role in identifying individuals and criminals in Security, surveillance, and footwork control systems. Face Recognition in the human is superb, and pictures can be easily identified even after years of separation. These abilities also apply to changes in a facial expression such as age, glasses, beard, or little change in the face. This method is based on 150 three-dimensional images using the Bosphorus database of a high range laser scanner in a Bogaziçi University in Turkey. This paper presents powerful processing for face recognition based on a combination of the salient information and features of the face, such as eyes and nose, for the detection of three-dimensional figures identified through analysis of surface curvature. The Trinity of the nose and two eyes were selected for applying principal component analysis algorithm and support vector machine to revealing and classification the difference between face and non-face. The results with different facial expressions and extracted from different angles have indicated the efficiency of our powerful processing.

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THE SUITABILITY OF TERRESTRIAL LASER SCANNING FOR BUILDING SURVEY AND MAPPING APPLICATIONS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w9-663-2019 ◽

2019 ◽

Vol XLII-2/W9 ◽

pp. 663-670 ◽

Cited By ~ 2

Author(s):

N. A. S. Russhakim ◽

M. F. M. Ariff ◽

Z. Majid ◽

K. M. Idris ◽

N. Darwin ◽

...

Keyword(s):

Laser Scanning ◽

3D Model ◽

Three Dimensional ◽

Laser Scanner ◽

Terrestrial Laser Scanning ◽

Point Clouds ◽

Short Period ◽

Complex Building ◽

The Difference ◽

2D Data

Abstract. The popularity of Terrestrial Laser Scanner (TLS) has been introduced into a field of surveying and has increased dramatically especially in producing the 3D model of the building. The used of terrestrial laser scanning (TLS) is becoming rapidly popular because of its ability in several applications, especially the ability to observe complex documentation of complex building and observe millions of point cloud in three-dimensional in a short period. Users of building plan usually find it difficult to translate the traditional two-dimensional (2D) data on maps they see on a flat piece of paper to three-dimensional (3D). The TLS is able to record thousands of point clouds which contains very rich of geometry details and made the processing usually takes longer time. In addition, the demand of building survey work has made the surveyors need to obtain the data with full of accuracy and time saves. Therefore, the aim of this study is to study the limitation uses of TLS and its suitability for building survey and mapping. In this study, the efficiency of TLS Leica C10 for building survey was determined in term of its accuracy and comparing with Zeb-Revo Handheld Mobile Laser Scanning (MLS) and the distometer. The accuracy for scanned data from both, TLS and MLS were compared with the Distometer by using root mean square error (RMSE) formula. Then, the 3D model of the building for both data, TLS and MLS were produced to analyze the visualization for different type of scanners. The software used; Autodesk Recap, Autodesk Revit, Leica Cyclone Software, Autocad Software and Geo Slam Software. The RMSE for TLS technique is 0.001m meanwhile, RMSE for MLS technique is 0.007m. The difference between these two techniques is 0.006m. The 3D model of building for both models did not have too much different but the scanned data from TLS is much easier to process and generate the 3D model compared to scanned data from MLS. It is because the scanned data from TLS comes with an image, while none from MLS scanned data. There are limitations of TLS for building survey such as water and glass window but this study proved that acquiring data by TLS is better than using MLS.

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Airfoil Surface Forming and Conformity Test Using Laser Tracker

Advances in Materials Science and Engineering ◽

10.1155/2022/6930741 ◽

2022 ◽

Vol 2022 ◽

pp. 1-10

Author(s):

Habtamu Beri ◽

Perumalla Janaki Ramulu

Keyword(s):

Three Dimensional ◽

Surface Model ◽

Laser Tracker ◽

Maximum Magnitude ◽

Three Dimensional Model ◽

Airfoil Surface ◽

Scanning Method ◽

Modeling Software ◽

The Difference ◽

Naca0018 Airfoil

In this study, NACA0018 airfoil surface conformity test was conducted using API tracker3 in combination with SpatialAnalyzer (SA) and modeling software SolidWorks. Plaster of Paris is used as a plug making material and a woven-type fiberglass is used as mold and airfoil surface making material. For airfoil surface analysis, three-dimensional model of the airfoil surface was developed in SolidWorks software and imported in IGES file format to SpatialAnalyzer (SA) software. Then, measurements were taken from manufactured airfoil surface using laser tracker through surface scanning method. Surface conformity test was conducted through fitting of measured points to surface model imported from SolidWorks to SpatialAnalyzer (SA) software. The optimized fit summary result shows that the average fit difference is 0.0 having standard deviation from 0.22224 from the average and zero with RMS of 0.2210. The maximum magnitude of the difference including x and y together is 0.5336 and the minimum −0.5077. Thus, with a given range of surface quality specification, laser tracker is an easy and reliable measurement and inspection tool to be considered.

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Akuisisi Foreground dan Background Berbasis Fitur DTC pada Matting Citra secara Otomatis

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2020732195 ◽

2020 ◽

Vol 7 (3) ◽

pp. 547

Author(s):

Meidya Koeshardianto ◽

Eko Mulyanto Yuniarno ◽

Mochamad Hariadi

Keyword(s):

Computer Vision ◽

Image Segmentation ◽

Edge Detection ◽

Mean Absolute Error ◽

Image Data ◽

Absolute Error ◽

Image Matting ◽

Alpha Matte ◽

The Difference ◽

Research Experiment

Teknik pemisahan foreground dari background pada citra statis merupakan penelitian yang sangat diperlukan dalam computer vision. Teknik yang sering digunakan adalah image segmentation, namun hasil ekstraksinya masih kurang akurat. Image matting menjadi salah satu solusi untuk memperbaiki hasil dari image segmentation. Pada metode supervised, image matting membutuhkan scribbles atau trimap sebagai constraint yang berfungsi untuk melabeli daerah tersebut adalah foreground atau background. Pada makalah ini dibangun metode unsupervised dengan mengakuisisi foreground dan background sebagai constraint secara otomatis. Akuisisi background ditentukan dari varian nilai fitur DCT (Discrete Cosinus Transform) yang dikelompokkan menggunakan algoritme k-means. Untuk mengakuisisi foreground ditentukan dari subset hasil klaster fitur DCT dengan fitur edge detection. Hasil dari proses akuisisi foreground dan background tersebut dijadikan sebagai constraint. Perbedaan hasil dari penelitian diukur menggunakan MAE (Mean Absolute Error) dibandingkan dengan metode supervised matting maupun dengan metode unsupervised matting lainnya. Skor MAE dari hasil eksperimen menunjukkan bahwa nilai alpha matte yang dihasilkan mempunyai perbedaan 0,0336 serta selisih waktu proses 0,4 detik dibandingkan metode supervised matting. Seluruh data citra berasal dari citra yang telah digunakan para peneliti sebelumnyaAbstractThe technique of separating the foreground and the background from a still image is widely used in computer vision. Current research in this technique is image segmentation. However, the result of its extraction is considered inaccurate. Furthermore, image matting is one solution to improve the effect of image segmentation. Mostly, the matting process used scribbles or trimap as a constraint, which is done manually as called a supervised method. The contribution offered in this paper lies in the acquisition of foreground and background that will be used to build constraints automatically. Background acquisition is determined from the variant value of the DCT feature that is clustered using the k-means algorithm. Foreground acquisition is determined by a subset resulting from clustering DCT values with edge detection features. The results of the two stages will be used as an automatic constraint method. The success of the proposed method, the constraint will be used in the supervised matting method. The difference in results from In the research experiment was measured using MAE (Mean Absolute Error) compared with the supervised matting method and with other unsupervised matting methods. The MAE score from the experimental results shows that the alpha matte value produced has a difference of 0.336, and the difference in processing time is 0.4 seconds compared to the supervised matting method. All image data comes from images that have been used by previous researchers.

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