SlideShare a Scribd company logo
1 of 6
Download to read offline
IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE)
e-ISSN: 2278-1684,p-ISSN: 2320-334X, Volume 12, Issue 2 Ver. VII (Mar - Apr. 2015), PP 10-15
www.iosrjournals.org
DOI: 10.9790/1684-12271015 www.iosrjournals.org 10 | Page
The Accuracy of Determining the Volumes Using Close Range
Photogrammetry
Hossam El-Din Fawzy
Lecturer, Civil Engineering Department Faculty of Engineering, Kafr El-Sheikh University, Kafr El-Sheikh,
EGYPT
Abstract: Time and accuracy required are the two important factors that decide on the appropriateness of
volume calculations for road project, mining enterprise, geological works and building applications. Geodetic
surveying methods have been insufficient for the volume calculation of the objects need to calculation of volume
in a short time or in a risk area. In this paper, digital close range photogrammetry is an alternative method to
volume calculation. The main objective of this paper is investigated the use of close range photogrammetry to
calculate the volume instead of traditional methods. This paper gives also the sequence of the field operations
and computational steps for this task. A numerical example is included to reinforce the theoretical aspects.
Keywords: Close Range Photogrammetry, Accuracy, Volumes, Surfer Software.
I. Introduction
The volume calculations are important requirement of the construction and mining industry. The
accurate volume estimation is important in many applications, for example road project, mining enterprise,
geological works and building applications. The traditional methods such as the trapezoidal method (rectangular
or triangular prisms), traditional cross sectioning (trapezoidal, Simpson, and average formula), and improved
methods (Simpson-based, cubic spline, and cubic Hermite formula) have been used in volume computing [1].
The main elements of these methods are to collect the points that appropriate distribution and density. These
methods needs more mathematical processes and take more time. The difficulties have been overcome by
developments in computer technologies. The corrections of volume is direct proportional with the presentations
of land surface in a best representation of land surface in best form is depend on the number of certain X,Y,Z
coordinate points. The total station instrument has been used to determine the certain coordinate for land
surface.
In this research, digital close range photogrammetry is an alternative method to volume calculation.
Digital close range photogrammetry is a method which has been used for three dimension surveying of objects
for many years. By the development of digital techniques, digital close range photogrammetry used in many
fields such as engineering surveying, topographic surveying, architectural surveying, archeological surveying,
etc disciplines has become a productive, faster and an economical method. Due to the developments in digital
photogrammetry and computer technology in past years, to constitute of three dimension models of objects
included in current research topics [2]. In this study, performance in volume calculation of digital close range
photogrammetry has been investigated.
The ground control points in photogrammetric method are used to calculate the position and orientation
of each camera in a stereo pair of photographs. In many cases the placement cannot be made because of
inaccessibility or safety restrictions. Even if access is not a problem it is often difficult to place the ground
control points in positions that allow for good visibility or distribution. Poor distribution of ground control
points can result in the calculated position and orientation of the camera to have a non unique solution or
variations of true position in the order of 10s of meters. The use of ground control points to calculate position
adds significantly to the time and skill required for image processing particularly in the documentation and
identification [3]. Only ground control points have to be signalized and measured in terrain by traditional
methods. However, it means just a few points in a comparison to an evaluation of all points or lines in a quarry
or a mine. In this paper, volume calculation performance has been investigated using two methods (traditional
and digital close range photogrammetry).
II. Traditional Methods For Determination The Volume
Current methods used for estimating volume assumes that the ground profile between the grid points is
linear (based on the trapezoidal rule), or nonlinear (based on Simpson’s 1/3 and 3/8 formulas). Generally
speaking, the nonlinear profile formulas provide better accuracy than the linear profile formulas. However, all
the methods mentioned above have a common drawback: The joints (grid points) of any two straight lines (the
The Accuracy of Determining the Volumes Using Close Range Photogrammetry
DOI: 10.9790/1684-12271015 www.iosrjournals.org 11 | Page
1
1
11109
8765
11109
4321






ZLYLXL
LZLYLXL
yyg
ZLYLXL
LZLYLXL
xxf
trapezoidal rule), quadratic polynomials (Simpson’s 1/3 formula), or cubic polynomials (Simpson’s 3/8 formula)
form sharp corners [3].
Simpson equations have been used in volume calculation with cross sections.
𝑉 =
𝐴1 + 𝐴2
2
∗ 𝑙1 +
𝐴2 + 𝐴3
2
∗ 𝑙2 + … . +
𝐴 𝑛−1 + 𝐴 𝑛
2
∗ 𝑙 𝑛
Where;
𝐴1 ….𝑛 = cross section areas,
𝑙1 … 𝑛 = distances between cross sections.
In surfer 10 software, grid surfaces of object have been generated from the X, Y, Z coordinates of object surface
using linear interpolation methods. In this software, the volume under the f(x,y) function can be determined with
double integral equations.
V = f x, y dx dy
Ymax
Ymin
Xmax
Xmin
In software, extended trapezoidal rules for volumes;
𝐴𝑖 =
∆𝑥
2
𝐻𝑖,1 + 2𝐻𝑖,2 + 2𝐻𝑖,3 + ⋯. +2𝐻𝑖,𝑛−1 + 𝐻𝑖,𝑛
𝑉 =
∆𝑦
2
𝐴1 + 2𝐴2 + 2𝐴3 + ⋯ . +2𝐴 𝑛−1 + 𝐴 𝑛
Extended Simpson Rule;
𝐴𝑖 =
∆𝑥
3
𝐻𝑖,1 + 4𝐻𝑖,2 + 2𝐻𝑖,3 + 4𝐻𝑖,4 + ⋯ . +2𝐻𝑖,𝑛−1 + 𝐻𝑖,𝑛
𝑉 =
∆𝑦
3
𝐴1 + 4𝐴2 + 2𝐴3 + 4𝐴4 + ⋯ . +2𝐴 𝑛−1 + 𝐴 𝑛
Extended Simpson 3/8 Rule;
𝐴𝑖 =
3∆𝑥
8
𝐻𝑖,1 + 3𝐻𝑖,2 + 3𝐻𝑖,3 + 2𝐻𝑖,4 + ⋯ . +2𝐻𝑖,𝑛−1 + 𝐻𝑖,𝑛
𝑉 =
3∆𝑦
8
𝐴1 + 3𝐴2 + 3𝐴3 + 2𝐴4 + ⋯. +2𝐴 𝑛−1 + 𝐴 𝑛
In traditional methods by means of set points on object surface and forming cross sections, excavation
volume of soil has been calculated. Using surfer 10 software, object surface obtained from geodetic points and
volume calculation could be accomplished. During this process the time spent and cost calculations have been
also made [3].
III. Close Range Photogrammetry For Determination The Volume
Digital close range photogrammetry is a technique for accurately measuring objects directly from
photographs or digital images captured with a camera at close range. Multiple overlapping images taken from
different perspectives, produces measurements that can be used to create accurate three dimension models of
objects. Knowing the position of camera is not necessary because the geometry of the object is established
directly from the images. Photogrammetry techniques allow you to convert images of an object into a three
dimension model. Using a digital camera with known characteristic (lens focal length, imager size and number
of pixels), you need a minimum of two pictures of an object. If you can indicate the same three object points in
the two images and you can indicate a known dimension, you can determine other three dimension points in the
images [4]. The photogrammetric three dimension coordinate determination is based on the co-linearity equation
which simply states that object point, camera projective centre and image point lie on a straight line. The
determination of the three dimension coordinates from a definite point is achieved through the intersection of
two or more straight lines or observed by electronic total station instrument.
Therefore, each point of interest should appear in at least two photographs then, coordinates are measured from
three dimension model which is constituted by photogrammetric software [5].
Abdel Aziz and Karara proposed a simple method for close range photogrammetric data reduction with non-
metric cameras; it establishes the direct linear transformation (DLT) between the two-dimensional coordinates,
and the corresponding object- space coordinates [6].
The Direct Linear Transformation (DLT) between a point (X, Y, Z) in object space and its corresponding image
space coordinates (x, y) can be established by the linear fractional equations:
(1)
The Accuracy of Determining the Volumes Using Close Range Photogrammetry
DOI: 10.9790/1684-12271015 www.iosrjournals.org 12 | Page

 yxpxrprkrkrkxx 2
22
1
6
3
4
2
2
1 2)2()(
)2(22)( 2
21
6
3
4
2
2
1

 yrpyxprkrkrkyy
Where:
L1, L2 L3…L11 are the transformation parameters
X, Y and Z are the object space coordinates
(2)
Where:
x-
= x – xo y-
= y – yo r2
= (x – xo)2
+ (y – yo)2
(3)
x, y are image coordinates
p1 and p2 are two asymmetric parameters for de-centering distortion
k1 , k2 and k3 are three symmetric parameters for radial distortion
r is the radial distance from the principal point
Equation 1 results from the equation of the central perspective in a trivial manner; Δx, Δy are systematic
deformations of the image, i.e. deviations from the central perspective. Equation 1 can be solved directly for the
11 transformation parameters (L1, L2 L3 …L11) if there are at least six points in the image whose object-space
coordinates are known. Equation 1 is rewritten to serve in a least squares formulation relating known control
points to image coordinate measurements:
vx = L1 X + L2 Y + L3 Z + L4 – xX L9 – xY L10 – xZ L11– Δx (4)
vy = L5 X + L6 Y + L7 Z + L8– yX L9 – yY L10 – y Z L11– Δy
IV. Test Field
This test was carried out inside Kafr El-Sheikh University. A test field area has 361.5 m length, 234 m
width and 3.25 m height (Figure 1). A test field consist of many stone heap has been used for volume
calculation. Both traditional and photogrammetric methods have been applied for the volume calculation.
Volume calculation results with traditional and photogrammetric methods are given in the next parts.
Figure 1: A test field area inside Kafr El-Sheikh University
Figure 2: The control points of test area based on
traditional methods
Figure 3: The contour map of test area based on
traditional methods
The Accuracy of Determining the Volumes Using Close Range Photogrammetry
DOI: 10.9790/1684-12271015 www.iosrjournals.org 13 | Page
Volume Computing By Traditional Methods
Cross sections have been obtained with the points placed on the test field area. The 425 points
coordinates were measured using SOKKIA SET330RK electronic total station instrument. The horizontal
distribution of the points is seen in Figure 2. The accuracy of the SOKKIA SET330RK total station is 5 mm ± 2
mm/km and angle accuracy 3" [7] & [8]. Figure 3 is shown the contour map of test area based on the elevation
of 425 points. The same points have been transferred to Surfer 10 software and from Trapezoidal Rule; volume
of the test field has been calculated as 221475.14356249 m3
. According to Simpson's Rule, volume of the test
field has been calculated as 221424.52105452 m3
. From Simpson's 3/8 Rule, the volume of test field has been
calculated as 221484.05385912 m3. The three dimension map of test area based on traditional methods is shown
in figures 4 and 5.
Volume Computing By Photogrammetic Methods
To carry out photogrammetric evaluation, 42 control points (20cm*20cm) in white and black have been
used to make it easy to see. The coordinates of the control points have been measured with SOKKIA
SET330RK electronic total station instrument. The horizontal distribution of the points is seen in Figure 6.
Figure 4: Three dimension map of test area based
on traditional methods
Figure 5: Three dimension map of test area based
on traditional methods
Figure 6: The control points of test area based on
photogrammetric method
Figure 7: The contour map of test area based on
photogrammetric methods
The Accuracy of Determining the Volumes Using Close Range Photogrammetry
DOI: 10.9790/1684-12271015 www.iosrjournals.org 14 | Page
Taking photographs has been done with the Nikon D 3100 digital camera (Figure 10) in 14.2 mega
pixel resolution. Camera calibration procedures have been completed for mathematical calculations.
Photographs have been transferred to Photomodeler software, photogrammetric evaluation has been
accomplished 574 coordinate points have been obtained. PhotoModeler is a software application that performs
image based modeling and close range photogrammetry producing three dimension models and measurements
from photography. PhotoModeler, first publicly released in 1993, was the first commercial all digital close range
photogrammetry and image based modeling system
Figure 10: The Nikon D 3100 digital camera
PhotoModeler creates accurate three dimension models (consisting of Points, Lines, Curves, Edges,
Cylinders, Surfaces and Shapes), and accurate three dimension measurements from photographs taken with most
standard cameras (either digital or film). Three dimension models can be created and exported with
photographic textures extracted from the original photographs [9].
The 574 control points obtained have been transferred to Surfer software and Figure 7 shown the
contour map of test area based on photogrammetric methods. The volume of test field has been calculated. From
Trapezoidal Rule; volume of the test field has been calculated as 215310.5960983 m3
. According to Simpson's
Rule, volume of the test field has been calculated as 215300.43158866 m3
. From Simpson's 3/8 Rule, the
volume of test field has been calculated as 215304.11889089 m3
. The three dimension model of test area based
on photogrammetric methods is seen in Figures 8 & 9. Tables 1 summarize the obtained volume in previous
cases. Table 1 summarizes the obtained volumes result by the traditional and photogrammetric methods and
percentage of accuracy for photogrammetric method.
Table 1: The volume calculation according to traditional and photogrammetric methods.
Methods
Traditional Method Photogrammetric Method
Difference
(m3)
Accuracy
(%)
No. of Control
Points
Volume
(m3)
No. of Control
Points
No. of Generated
Points
Volume
(m3)
Trapezoidal
Rule
425
221475.1436
42 574
215310.5961 6164.5475 97.2166
Simpson's Rule 221424.5211 215300.4316 6124.0895 97.23423
Simpson's 3/8
Rule
221484.0539 215304.1189 6179.935 97.20976
Figure 8: three dimension map of test area based
on photogrammetric methods
Figure 9: three dimension map of test area based on
photogrammetric methods
The Accuracy of Determining the Volumes Using Close Range Photogrammetry
DOI: 10.9790/1684-12271015 www.iosrjournals.org 15 | Page
V. Conclusions
The photogrammetric method has been used and the obtained accuracy is discussed and presented.
When experimental results examined photogrammetric method could approach 97.21% ratio to the traditional
method value. Based on the experimental results, the following conclusions can be drawn:
- The photogrammetric method is efficient to determination the volumes;
- The photogrammetric method has proved to be fast and useful to estimate the volume with compared by
traditional method;
- The accuracy of photogrammetric method to determine the volume can be given good results 97.21% in
comparison with traditional method.
- The 10% of ground control points using to determine the volume was saved by photogrammetric method
with compared by traditional method which determine the same accuracy.
References
[1]. Marek Bajtala, Peter Brunčák, Jozef Kubinec, Miroslav Lipták, Štefan Sokol, “THE PROBLEM OF DETERMINING THE
VOLUMES USING MODERN SURVEYING TECHNIQUES” journal of inter disciplinary research, 2012.
[2]. Hossam El-Din Fawzy “The Accuracy of Mobile Phone Camera Instead of High Resolution Camera In Digital Close Range
Photogrammetry” International Journal of Civil Engineering & Technology (IJCIET), Volume 6, Issue 1, 2015, pp. 76-85, ISSN
Print: 0976 – 6308, ISSN Online: 0976 – 6316.
[3]. Yakar M., Yilmaz H.M., “ USING IN VOLUME COMPUTING OF DIGITAL CLOSE RANGE PHOTOGRAMMETRY” The
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B3b. Beijing
2008.
[4]. Yakar M., Yilmaz H.M. and Mutluoglu O., “Close range photogrammetry and robotic total station in volume calculation”
International Journal of the Physical Sciences Vol. 5 (2), pp. 086-096, February, 2010.
[5]. Kraus K (1992). “Photogrammetry, Advanced Methods and Applications” Foutrh Ed., Vol. II, Dümmler, Bonn.
[6]. Abdel-Aziz, Y. I. and Karara, H. M., 1971 “Direct Linear Transformation from Comparator Coordinates into Object Space
Coordinates in Close Range Photogrammetry” Proceedings of the ASP/UI Symposium on Close-Range Photogrammetry, Urbana,
Illinois, pp. 1-18.
[7]. Hossam El-Din Fawzy “Evaluate The Accuracy Of Reflector-Less Total Station” International Journal of Civil Engineering &
Technology (IJCIET), Volume 6, Issue 3, 2015, pp. 23-30, ISSN Print: 0976 – 6308, ISSN Online: 0976 – 6316.
[8]. https://www.scribd.com/doc/205256016/Manual-Sokkia-Series-30rk-Set230rk-Rk3-Set330rk-Rk3-Set530rk-Rk3-Set630rk-En,
2015.
[9]. http://en.wikipedia.org/wiki/PhotoModeler, 2015.

More Related Content

What's hot

A novel elementary spatial expanding scheme form on SISR method with modifyin...
A novel elementary spatial expanding scheme form on SISR method with modifyin...A novel elementary spatial expanding scheme form on SISR method with modifyin...
A novel elementary spatial expanding scheme form on SISR method with modifyin...TELKOMNIKA JOURNAL
 
4 hydrology geostatistics-part_2
4 hydrology geostatistics-part_2 4 hydrology geostatistics-part_2
4 hydrology geostatistics-part_2 Riccardo Rigon
 
MAP MAKING FROM TABLES
MAP MAKING FROM TABLESMAP MAKING FROM TABLES
MAP MAKING FROM TABLESijcga
 
Dynamic texture based traffic vehicle monitoring system
Dynamic texture based traffic vehicle monitoring systemDynamic texture based traffic vehicle monitoring system
Dynamic texture based traffic vehicle monitoring systemeSAT Journals
 
Digital image processing
Digital image processingDigital image processing
Digital image processinglakhveer singh
 
Effect of sub classes on the accuracy of the classified image
Effect of sub classes on the accuracy of the classified imageEffect of sub classes on the accuracy of the classified image
Effect of sub classes on the accuracy of the classified imageiaemedu
 
Single Image Fog Removal Based on Fusion Strategy
Single Image Fog Removal Based on Fusion Strategy Single Image Fog Removal Based on Fusion Strategy
Single Image Fog Removal Based on Fusion Strategy csandit
 
Object Elimination and Reconstruction Using an Effective Inpainting Method
Object Elimination and Reconstruction Using an Effective Inpainting MethodObject Elimination and Reconstruction Using an Effective Inpainting Method
Object Elimination and Reconstruction Using an Effective Inpainting MethodIOSR Journals
 
PROPOSED ALGORITHM OF ELIMINATING REDUNDANT DATA OF LASER SCANNING FOR CREATI...
PROPOSED ALGORITHM OF ELIMINATING REDUNDANT DATA OF LASER SCANNING FOR CREATI...PROPOSED ALGORITHM OF ELIMINATING REDUNDANT DATA OF LASER SCANNING FOR CREATI...
PROPOSED ALGORITHM OF ELIMINATING REDUNDANT DATA OF LASER SCANNING FOR CREATI...IAEME Publication
 
ACME2016-extendedAbstract
ACME2016-extendedAbstractACME2016-extendedAbstract
ACME2016-extendedAbstractZhaowei Liu
 
Application of Image Retrieval Techniques to Understand Evolving Weather
Application of Image Retrieval Techniques to Understand Evolving WeatherApplication of Image Retrieval Techniques to Understand Evolving Weather
Application of Image Retrieval Techniques to Understand Evolving Weatherijsrd.com
 
A NOVEL APPROACH TO SMOOTHING ON 3D STRUCTURED ADAPTIVE MESH OF THE KINECT-BA...
A NOVEL APPROACH TO SMOOTHING ON 3D STRUCTURED ADAPTIVE MESH OF THE KINECT-BA...A NOVEL APPROACH TO SMOOTHING ON 3D STRUCTURED ADAPTIVE MESH OF THE KINECT-BA...
A NOVEL APPROACH TO SMOOTHING ON 3D STRUCTURED ADAPTIVE MESH OF THE KINECT-BA...csandit
 

What's hot (16)

A novel elementary spatial expanding scheme form on SISR method with modifyin...
A novel elementary spatial expanding scheme form on SISR method with modifyin...A novel elementary spatial expanding scheme form on SISR method with modifyin...
A novel elementary spatial expanding scheme form on SISR method with modifyin...
 
4 hydrology geostatistics-part_2
4 hydrology geostatistics-part_2 4 hydrology geostatistics-part_2
4 hydrology geostatistics-part_2
 
Ak03302260233
Ak03302260233Ak03302260233
Ak03302260233
 
MAP MAKING FROM TABLES
MAP MAKING FROM TABLESMAP MAKING FROM TABLES
MAP MAKING FROM TABLES
 
Distortion Correction Scheme for Multiresolution Camera Images
Distortion Correction Scheme for Multiresolution Camera ImagesDistortion Correction Scheme for Multiresolution Camera Images
Distortion Correction Scheme for Multiresolution Camera Images
 
Ih2616101615
Ih2616101615Ih2616101615
Ih2616101615
 
Dynamic texture based traffic vehicle monitoring system
Dynamic texture based traffic vehicle monitoring systemDynamic texture based traffic vehicle monitoring system
Dynamic texture based traffic vehicle monitoring system
 
Collinearity Equations
Collinearity EquationsCollinearity Equations
Collinearity Equations
 
Digital image processing
Digital image processingDigital image processing
Digital image processing
 
Effect of sub classes on the accuracy of the classified image
Effect of sub classes on the accuracy of the classified imageEffect of sub classes on the accuracy of the classified image
Effect of sub classes on the accuracy of the classified image
 
Single Image Fog Removal Based on Fusion Strategy
Single Image Fog Removal Based on Fusion Strategy Single Image Fog Removal Based on Fusion Strategy
Single Image Fog Removal Based on Fusion Strategy
 
Object Elimination and Reconstruction Using an Effective Inpainting Method
Object Elimination and Reconstruction Using an Effective Inpainting MethodObject Elimination and Reconstruction Using an Effective Inpainting Method
Object Elimination and Reconstruction Using an Effective Inpainting Method
 
PROPOSED ALGORITHM OF ELIMINATING REDUNDANT DATA OF LASER SCANNING FOR CREATI...
PROPOSED ALGORITHM OF ELIMINATING REDUNDANT DATA OF LASER SCANNING FOR CREATI...PROPOSED ALGORITHM OF ELIMINATING REDUNDANT DATA OF LASER SCANNING FOR CREATI...
PROPOSED ALGORITHM OF ELIMINATING REDUNDANT DATA OF LASER SCANNING FOR CREATI...
 
ACME2016-extendedAbstract
ACME2016-extendedAbstractACME2016-extendedAbstract
ACME2016-extendedAbstract
 
Application of Image Retrieval Techniques to Understand Evolving Weather
Application of Image Retrieval Techniques to Understand Evolving WeatherApplication of Image Retrieval Techniques to Understand Evolving Weather
Application of Image Retrieval Techniques to Understand Evolving Weather
 
A NOVEL APPROACH TO SMOOTHING ON 3D STRUCTURED ADAPTIVE MESH OF THE KINECT-BA...
A NOVEL APPROACH TO SMOOTHING ON 3D STRUCTURED ADAPTIVE MESH OF THE KINECT-BA...A NOVEL APPROACH TO SMOOTHING ON 3D STRUCTURED ADAPTIVE MESH OF THE KINECT-BA...
A NOVEL APPROACH TO SMOOTHING ON 3D STRUCTURED ADAPTIVE MESH OF THE KINECT-BA...
 

Viewers also liked

Annual report draft-v2
Annual report draft-v2Annual report draft-v2
Annual report draft-v2Anil Krishna
 
9555569222 Supertech Basera Affordable Housing Sector 79 Gurgaon, Haryana Aff...
9555569222 Supertech Basera Affordable Housing Sector 79 Gurgaon, Haryana Aff...9555569222 Supertech Basera Affordable Housing Sector 79 Gurgaon, Haryana Aff...
9555569222 Supertech Basera Affordable Housing Sector 79 Gurgaon, Haryana Aff...affordablehousinggurgaon
 
Production of α-amylase using new strain of Bacillus polymyxa isolated from s...
Production of α-amylase using new strain of Bacillus polymyxa isolated from s...Production of α-amylase using new strain of Bacillus polymyxa isolated from s...
Production of α-amylase using new strain of Bacillus polymyxa isolated from s...IOSR Journals
 
Are NYS’s privately owned lLEC’s to big to fail
Are NYS’s privately owned lLEC’s to big to failAre NYS’s privately owned lLEC’s to big to fail
Are NYS’s privately owned lLEC’s to big to failRich Frank
 
Madcom osp design, engineering & construction capabilities
Madcom osp design, engineering & construction capabilitiesMadcom osp design, engineering & construction capabilities
Madcom osp design, engineering & construction capabilitiesRich Frank
 
Madcom analyzes bermuda ftth design
Madcom analyzes bermuda ftth designMadcom analyzes bermuda ftth design
Madcom analyzes bermuda ftth designRich Frank
 
Young - Task, Nullification Crisis
Young - Task, Nullification Crisis Young - Task, Nullification Crisis
Young - Task, Nullification Crisis Neal Young
 

Viewers also liked (18)

M010219295
M010219295M010219295
M010219295
 
G1804014348
G1804014348G1804014348
G1804014348
 
J017114852
J017114852J017114852
J017114852
 
E010143037
E010143037E010143037
E010143037
 
A017620123
A017620123A017620123
A017620123
 
N0175496100
N0175496100N0175496100
N0175496100
 
Annual report draft-v2
Annual report draft-v2Annual report draft-v2
Annual report draft-v2
 
M0174491101
M0174491101M0174491101
M0174491101
 
E017632934
E017632934E017632934
E017632934
 
I018126170
I018126170I018126170
I018126170
 
A010610110
A010610110A010610110
A010610110
 
K018146372
K018146372K018146372
K018146372
 
9555569222 Supertech Basera Affordable Housing Sector 79 Gurgaon, Haryana Aff...
9555569222 Supertech Basera Affordable Housing Sector 79 Gurgaon, Haryana Aff...9555569222 Supertech Basera Affordable Housing Sector 79 Gurgaon, Haryana Aff...
9555569222 Supertech Basera Affordable Housing Sector 79 Gurgaon, Haryana Aff...
 
Production of α-amylase using new strain of Bacillus polymyxa isolated from s...
Production of α-amylase using new strain of Bacillus polymyxa isolated from s...Production of α-amylase using new strain of Bacillus polymyxa isolated from s...
Production of α-amylase using new strain of Bacillus polymyxa isolated from s...
 
Are NYS’s privately owned lLEC’s to big to fail
Are NYS’s privately owned lLEC’s to big to failAre NYS’s privately owned lLEC’s to big to fail
Are NYS’s privately owned lLEC’s to big to fail
 
Madcom osp design, engineering & construction capabilities
Madcom osp design, engineering & construction capabilitiesMadcom osp design, engineering & construction capabilities
Madcom osp design, engineering & construction capabilities
 
Madcom analyzes bermuda ftth design
Madcom analyzes bermuda ftth designMadcom analyzes bermuda ftth design
Madcom analyzes bermuda ftth design
 
Young - Task, Nullification Crisis
Young - Task, Nullification Crisis Young - Task, Nullification Crisis
Young - Task, Nullification Crisis
 

Similar to C012271015

Stereo Vision Distance Estimation Employing Canny Edge Detector with Interpol...
Stereo Vision Distance Estimation Employing Canny Edge Detector with Interpol...Stereo Vision Distance Estimation Employing Canny Edge Detector with Interpol...
Stereo Vision Distance Estimation Employing Canny Edge Detector with Interpol...ZaidHussein6
 
Enhanced Algorithm for Obstacle Detection and Avoidance Using a Hybrid of Pla...
Enhanced Algorithm for Obstacle Detection and Avoidance Using a Hybrid of Pla...Enhanced Algorithm for Obstacle Detection and Avoidance Using a Hybrid of Pla...
Enhanced Algorithm for Obstacle Detection and Avoidance Using a Hybrid of Pla...IOSR Journals
 
Dense Visual Odometry Using Genetic Algorithm
Dense Visual Odometry Using Genetic AlgorithmDense Visual Odometry Using Genetic Algorithm
Dense Visual Odometry Using Genetic AlgorithmSlimane Djema
 
Geospatial Data Acquisition Using Unmanned Aerial Systems
Geospatial Data Acquisition Using Unmanned Aerial SystemsGeospatial Data Acquisition Using Unmanned Aerial Systems
Geospatial Data Acquisition Using Unmanned Aerial SystemsIEREK Press
 
Wujanz_Error_Projection_2011
Wujanz_Error_Projection_2011Wujanz_Error_Projection_2011
Wujanz_Error_Projection_2011Jacob Collstrup
 
Matching algorithm performance analysis for autocalibration method of stereo ...
Matching algorithm performance analysis for autocalibration method of stereo ...Matching algorithm performance analysis for autocalibration method of stereo ...
Matching algorithm performance analysis for autocalibration method of stereo ...TELKOMNIKA JOURNAL
 
Stereo matching based on absolute differences for multiple objects detection
Stereo matching based on absolute differences for multiple objects detectionStereo matching based on absolute differences for multiple objects detection
Stereo matching based on absolute differences for multiple objects detectionTELKOMNIKA JOURNAL
 
A NOVEL APPROACH TO SMOOTHING ON 3D STRUCTURED ADAPTIVE MESH OF THE KINECT-BA...
A NOVEL APPROACH TO SMOOTHING ON 3D STRUCTURED ADAPTIVE MESH OF THE KINECT-BA...A NOVEL APPROACH TO SMOOTHING ON 3D STRUCTURED ADAPTIVE MESH OF THE KINECT-BA...
A NOVEL APPROACH TO SMOOTHING ON 3D STRUCTURED ADAPTIVE MESH OF THE KINECT-BA...cscpconf
 
Stereo Correspondence Algorithms for Robotic Applications Under Ideal And Non...
Stereo Correspondence Algorithms for Robotic Applications Under Ideal And Non...Stereo Correspondence Algorithms for Robotic Applications Under Ideal And Non...
Stereo Correspondence Algorithms for Robotic Applications Under Ideal And Non...CSCJournals
 
Feature extraction based retrieval of
Feature extraction based retrieval ofFeature extraction based retrieval of
Feature extraction based retrieval ofijcsity
 
Towards better performance: phase congruency based face recognition
Towards better performance: phase congruency based face recognitionTowards better performance: phase congruency based face recognition
Towards better performance: phase congruency based face recognitionTELKOMNIKA JOURNAL
 
A STUDY AND ANALYSIS OF DIFFERENT EDGE DETECTION TECHNIQUES
A STUDY AND ANALYSIS OF DIFFERENT EDGE DETECTION TECHNIQUESA STUDY AND ANALYSIS OF DIFFERENT EDGE DETECTION TECHNIQUES
A STUDY AND ANALYSIS OF DIFFERENT EDGE DETECTION TECHNIQUEScscpconf
 
Time of arrival based localization in wireless sensor networks a non linear ...
Time of arrival based localization in wireless sensor networks  a non linear ...Time of arrival based localization in wireless sensor networks  a non linear ...
Time of arrival based localization in wireless sensor networks a non linear ...sipij
 
A novel predicate for active region merging in automatic image segmentation
A novel predicate for active region merging in automatic image segmentationA novel predicate for active region merging in automatic image segmentation
A novel predicate for active region merging in automatic image segmentationeSAT Publishing House
 
A novel predicate for active region merging in automatic image segmentation
A novel predicate for active region merging in automatic image segmentationA novel predicate for active region merging in automatic image segmentation
A novel predicate for active region merging in automatic image segmentationeSAT Journals
 
Surface generation from point cloud.pdf
Surface generation from point cloud.pdfSurface generation from point cloud.pdf
Surface generation from point cloud.pdfssuserd3982b1
 
EXTENDED HYBRID REGION GROWING SEGMENTATION OF POINT CLOUDS WITH DIFFERENT RE...
EXTENDED HYBRID REGION GROWING SEGMENTATION OF POINT CLOUDS WITH DIFFERENT RE...EXTENDED HYBRID REGION GROWING SEGMENTATION OF POINT CLOUDS WITH DIFFERENT RE...
EXTENDED HYBRID REGION GROWING SEGMENTATION OF POINT CLOUDS WITH DIFFERENT RE...cscpconf
 

Similar to C012271015 (20)

Stereo Vision Distance Estimation Employing Canny Edge Detector with Interpol...
Stereo Vision Distance Estimation Employing Canny Edge Detector with Interpol...Stereo Vision Distance Estimation Employing Canny Edge Detector with Interpol...
Stereo Vision Distance Estimation Employing Canny Edge Detector with Interpol...
 
Enhanced Algorithm for Obstacle Detection and Avoidance Using a Hybrid of Pla...
Enhanced Algorithm for Obstacle Detection and Avoidance Using a Hybrid of Pla...Enhanced Algorithm for Obstacle Detection and Avoidance Using a Hybrid of Pla...
Enhanced Algorithm for Obstacle Detection and Avoidance Using a Hybrid of Pla...
 
JBSC_online
JBSC_onlineJBSC_online
JBSC_online
 
Dense Visual Odometry Using Genetic Algorithm
Dense Visual Odometry Using Genetic AlgorithmDense Visual Odometry Using Genetic Algorithm
Dense Visual Odometry Using Genetic Algorithm
 
Geospatial Data Acquisition Using Unmanned Aerial Systems
Geospatial Data Acquisition Using Unmanned Aerial SystemsGeospatial Data Acquisition Using Unmanned Aerial Systems
Geospatial Data Acquisition Using Unmanned Aerial Systems
 
Wujanz_Error_Projection_2011
Wujanz_Error_Projection_2011Wujanz_Error_Projection_2011
Wujanz_Error_Projection_2011
 
Matching algorithm performance analysis for autocalibration method of stereo ...
Matching algorithm performance analysis for autocalibration method of stereo ...Matching algorithm performance analysis for autocalibration method of stereo ...
Matching algorithm performance analysis for autocalibration method of stereo ...
 
Stereo matching based on absolute differences for multiple objects detection
Stereo matching based on absolute differences for multiple objects detectionStereo matching based on absolute differences for multiple objects detection
Stereo matching based on absolute differences for multiple objects detection
 
A NOVEL APPROACH TO SMOOTHING ON 3D STRUCTURED ADAPTIVE MESH OF THE KINECT-BA...
A NOVEL APPROACH TO SMOOTHING ON 3D STRUCTURED ADAPTIVE MESH OF THE KINECT-BA...A NOVEL APPROACH TO SMOOTHING ON 3D STRUCTURED ADAPTIVE MESH OF THE KINECT-BA...
A NOVEL APPROACH TO SMOOTHING ON 3D STRUCTURED ADAPTIVE MESH OF THE KINECT-BA...
 
Stereo Correspondence Algorithms for Robotic Applications Under Ideal And Non...
Stereo Correspondence Algorithms for Robotic Applications Under Ideal And Non...Stereo Correspondence Algorithms for Robotic Applications Under Ideal And Non...
Stereo Correspondence Algorithms for Robotic Applications Under Ideal And Non...
 
998-isvc16
998-isvc16998-isvc16
998-isvc16
 
Feature extraction based retrieval of
Feature extraction based retrieval ofFeature extraction based retrieval of
Feature extraction based retrieval of
 
Towards better performance: phase congruency based face recognition
Towards better performance: phase congruency based face recognitionTowards better performance: phase congruency based face recognition
Towards better performance: phase congruency based face recognition
 
A STUDY AND ANALYSIS OF DIFFERENT EDGE DETECTION TECHNIQUES
A STUDY AND ANALYSIS OF DIFFERENT EDGE DETECTION TECHNIQUESA STUDY AND ANALYSIS OF DIFFERENT EDGE DETECTION TECHNIQUES
A STUDY AND ANALYSIS OF DIFFERENT EDGE DETECTION TECHNIQUES
 
Time of arrival based localization in wireless sensor networks a non linear ...
Time of arrival based localization in wireless sensor networks  a non linear ...Time of arrival based localization in wireless sensor networks  a non linear ...
Time of arrival based localization in wireless sensor networks a non linear ...
 
A novel predicate for active region merging in automatic image segmentation
A novel predicate for active region merging in automatic image segmentationA novel predicate for active region merging in automatic image segmentation
A novel predicate for active region merging in automatic image segmentation
 
A novel predicate for active region merging in automatic image segmentation
A novel predicate for active region merging in automatic image segmentationA novel predicate for active region merging in automatic image segmentation
A novel predicate for active region merging in automatic image segmentation
 
Surface generation from point cloud.pdf
Surface generation from point cloud.pdfSurface generation from point cloud.pdf
Surface generation from point cloud.pdf
 
EXTENDED HYBRID REGION GROWING SEGMENTATION OF POINT CLOUDS WITH DIFFERENT RE...
EXTENDED HYBRID REGION GROWING SEGMENTATION OF POINT CLOUDS WITH DIFFERENT RE...EXTENDED HYBRID REGION GROWING SEGMENTATION OF POINT CLOUDS WITH DIFFERENT RE...
EXTENDED HYBRID REGION GROWING SEGMENTATION OF POINT CLOUDS WITH DIFFERENT RE...
 
40120130406008
4012013040600840120130406008
40120130406008
 

More from IOSR Journals (20)

A011140104
A011140104A011140104
A011140104
 
M0111397100
M0111397100M0111397100
M0111397100
 
L011138596
L011138596L011138596
L011138596
 
K011138084
K011138084K011138084
K011138084
 
J011137479
J011137479J011137479
J011137479
 
I011136673
I011136673I011136673
I011136673
 
G011134454
G011134454G011134454
G011134454
 
H011135565
H011135565H011135565
H011135565
 
F011134043
F011134043F011134043
F011134043
 
E011133639
E011133639E011133639
E011133639
 
D011132635
D011132635D011132635
D011132635
 
C011131925
C011131925C011131925
C011131925
 
B011130918
B011130918B011130918
B011130918
 
A011130108
A011130108A011130108
A011130108
 
I011125160
I011125160I011125160
I011125160
 
H011124050
H011124050H011124050
H011124050
 
G011123539
G011123539G011123539
G011123539
 
F011123134
F011123134F011123134
F011123134
 
E011122530
E011122530E011122530
E011122530
 
D011121524
D011121524D011121524
D011121524
 

Recently uploaded

WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure serviceWhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure servicePooja Nehwal
 
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | DelhiFULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhisoniya singh
 
IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsIAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsEnterprise Knowledge
 
Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)Allon Mureinik
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonetsnaman860154
 
CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):comworks
 
Pigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food ManufacturingPigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food ManufacturingPigging Solutions
 
AI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsAI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsMemoori
 
Understanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitectureUnderstanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitecturePixlogix Infotech
 
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry InnovationBeyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry InnovationSafe Software
 
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024BookNet Canada
 
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreternaman860154
 
Azure Monitor & Application Insight to monitor Infrastructure & Application
Azure Monitor & Application Insight to monitor Infrastructure & ApplicationAzure Monitor & Application Insight to monitor Infrastructure & Application
Azure Monitor & Application Insight to monitor Infrastructure & ApplicationAndikSusilo4
 
Install Stable Diffusion in windows machine
Install Stable Diffusion in windows machineInstall Stable Diffusion in windows machine
Install Stable Diffusion in windows machinePadma Pradeep
 
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking MenDelhi Call girls
 
Benefits Of Flutter Compared To Other Frameworks
Benefits Of Flutter Compared To Other FrameworksBenefits Of Flutter Compared To Other Frameworks
Benefits Of Flutter Compared To Other FrameworksSoftradix Technologies
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxMalak Abu Hammad
 
Key Features Of Token Development (1).pptx
Key  Features Of Token  Development (1).pptxKey  Features Of Token  Development (1).pptx
Key Features Of Token Development (1).pptxLBM Solutions
 
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...HostedbyConfluent
 

Recently uploaded (20)

WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure serviceWhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
 
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | DelhiFULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
 
IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsIAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI Solutions
 
Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
 
CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):
 
Pigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food ManufacturingPigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food Manufacturing
 
AI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsAI as an Interface for Commercial Buildings
AI as an Interface for Commercial Buildings
 
Understanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitectureUnderstanding the Laravel MVC Architecture
Understanding the Laravel MVC Architecture
 
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptxE-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
 
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry InnovationBeyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
 
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
 
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreter
 
Azure Monitor & Application Insight to monitor Infrastructure & Application
Azure Monitor & Application Insight to monitor Infrastructure & ApplicationAzure Monitor & Application Insight to monitor Infrastructure & Application
Azure Monitor & Application Insight to monitor Infrastructure & Application
 
Install Stable Diffusion in windows machine
Install Stable Diffusion in windows machineInstall Stable Diffusion in windows machine
Install Stable Diffusion in windows machine
 
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
 
Benefits Of Flutter Compared To Other Frameworks
Benefits Of Flutter Compared To Other FrameworksBenefits Of Flutter Compared To Other Frameworks
Benefits Of Flutter Compared To Other Frameworks
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptx
 
Key Features Of Token Development (1).pptx
Key  Features Of Token  Development (1).pptxKey  Features Of Token  Development (1).pptx
Key Features Of Token Development (1).pptx
 
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
 

C012271015

  • 1. IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE) e-ISSN: 2278-1684,p-ISSN: 2320-334X, Volume 12, Issue 2 Ver. VII (Mar - Apr. 2015), PP 10-15 www.iosrjournals.org DOI: 10.9790/1684-12271015 www.iosrjournals.org 10 | Page The Accuracy of Determining the Volumes Using Close Range Photogrammetry Hossam El-Din Fawzy Lecturer, Civil Engineering Department Faculty of Engineering, Kafr El-Sheikh University, Kafr El-Sheikh, EGYPT Abstract: Time and accuracy required are the two important factors that decide on the appropriateness of volume calculations for road project, mining enterprise, geological works and building applications. Geodetic surveying methods have been insufficient for the volume calculation of the objects need to calculation of volume in a short time or in a risk area. In this paper, digital close range photogrammetry is an alternative method to volume calculation. The main objective of this paper is investigated the use of close range photogrammetry to calculate the volume instead of traditional methods. This paper gives also the sequence of the field operations and computational steps for this task. A numerical example is included to reinforce the theoretical aspects. Keywords: Close Range Photogrammetry, Accuracy, Volumes, Surfer Software. I. Introduction The volume calculations are important requirement of the construction and mining industry. The accurate volume estimation is important in many applications, for example road project, mining enterprise, geological works and building applications. The traditional methods such as the trapezoidal method (rectangular or triangular prisms), traditional cross sectioning (trapezoidal, Simpson, and average formula), and improved methods (Simpson-based, cubic spline, and cubic Hermite formula) have been used in volume computing [1]. The main elements of these methods are to collect the points that appropriate distribution and density. These methods needs more mathematical processes and take more time. The difficulties have been overcome by developments in computer technologies. The corrections of volume is direct proportional with the presentations of land surface in a best representation of land surface in best form is depend on the number of certain X,Y,Z coordinate points. The total station instrument has been used to determine the certain coordinate for land surface. In this research, digital close range photogrammetry is an alternative method to volume calculation. Digital close range photogrammetry is a method which has been used for three dimension surveying of objects for many years. By the development of digital techniques, digital close range photogrammetry used in many fields such as engineering surveying, topographic surveying, architectural surveying, archeological surveying, etc disciplines has become a productive, faster and an economical method. Due to the developments in digital photogrammetry and computer technology in past years, to constitute of three dimension models of objects included in current research topics [2]. In this study, performance in volume calculation of digital close range photogrammetry has been investigated. The ground control points in photogrammetric method are used to calculate the position and orientation of each camera in a stereo pair of photographs. In many cases the placement cannot be made because of inaccessibility or safety restrictions. Even if access is not a problem it is often difficult to place the ground control points in positions that allow for good visibility or distribution. Poor distribution of ground control points can result in the calculated position and orientation of the camera to have a non unique solution or variations of true position in the order of 10s of meters. The use of ground control points to calculate position adds significantly to the time and skill required for image processing particularly in the documentation and identification [3]. Only ground control points have to be signalized and measured in terrain by traditional methods. However, it means just a few points in a comparison to an evaluation of all points or lines in a quarry or a mine. In this paper, volume calculation performance has been investigated using two methods (traditional and digital close range photogrammetry). II. Traditional Methods For Determination The Volume Current methods used for estimating volume assumes that the ground profile between the grid points is linear (based on the trapezoidal rule), or nonlinear (based on Simpson’s 1/3 and 3/8 formulas). Generally speaking, the nonlinear profile formulas provide better accuracy than the linear profile formulas. However, all the methods mentioned above have a common drawback: The joints (grid points) of any two straight lines (the
  • 2. The Accuracy of Determining the Volumes Using Close Range Photogrammetry DOI: 10.9790/1684-12271015 www.iosrjournals.org 11 | Page 1 1 11109 8765 11109 4321       ZLYLXL LZLYLXL yyg ZLYLXL LZLYLXL xxf trapezoidal rule), quadratic polynomials (Simpson’s 1/3 formula), or cubic polynomials (Simpson’s 3/8 formula) form sharp corners [3]. Simpson equations have been used in volume calculation with cross sections. 𝑉 = 𝐴1 + 𝐴2 2 ∗ 𝑙1 + 𝐴2 + 𝐴3 2 ∗ 𝑙2 + … . + 𝐴 𝑛−1 + 𝐴 𝑛 2 ∗ 𝑙 𝑛 Where; 𝐴1 ….𝑛 = cross section areas, 𝑙1 … 𝑛 = distances between cross sections. In surfer 10 software, grid surfaces of object have been generated from the X, Y, Z coordinates of object surface using linear interpolation methods. In this software, the volume under the f(x,y) function can be determined with double integral equations. V = f x, y dx dy Ymax Ymin Xmax Xmin In software, extended trapezoidal rules for volumes; 𝐴𝑖 = ∆𝑥 2 𝐻𝑖,1 + 2𝐻𝑖,2 + 2𝐻𝑖,3 + ⋯. +2𝐻𝑖,𝑛−1 + 𝐻𝑖,𝑛 𝑉 = ∆𝑦 2 𝐴1 + 2𝐴2 + 2𝐴3 + ⋯ . +2𝐴 𝑛−1 + 𝐴 𝑛 Extended Simpson Rule; 𝐴𝑖 = ∆𝑥 3 𝐻𝑖,1 + 4𝐻𝑖,2 + 2𝐻𝑖,3 + 4𝐻𝑖,4 + ⋯ . +2𝐻𝑖,𝑛−1 + 𝐻𝑖,𝑛 𝑉 = ∆𝑦 3 𝐴1 + 4𝐴2 + 2𝐴3 + 4𝐴4 + ⋯ . +2𝐴 𝑛−1 + 𝐴 𝑛 Extended Simpson 3/8 Rule; 𝐴𝑖 = 3∆𝑥 8 𝐻𝑖,1 + 3𝐻𝑖,2 + 3𝐻𝑖,3 + 2𝐻𝑖,4 + ⋯ . +2𝐻𝑖,𝑛−1 + 𝐻𝑖,𝑛 𝑉 = 3∆𝑦 8 𝐴1 + 3𝐴2 + 3𝐴3 + 2𝐴4 + ⋯. +2𝐴 𝑛−1 + 𝐴 𝑛 In traditional methods by means of set points on object surface and forming cross sections, excavation volume of soil has been calculated. Using surfer 10 software, object surface obtained from geodetic points and volume calculation could be accomplished. During this process the time spent and cost calculations have been also made [3]. III. Close Range Photogrammetry For Determination The Volume Digital close range photogrammetry is a technique for accurately measuring objects directly from photographs or digital images captured with a camera at close range. Multiple overlapping images taken from different perspectives, produces measurements that can be used to create accurate three dimension models of objects. Knowing the position of camera is not necessary because the geometry of the object is established directly from the images. Photogrammetry techniques allow you to convert images of an object into a three dimension model. Using a digital camera with known characteristic (lens focal length, imager size and number of pixels), you need a minimum of two pictures of an object. If you can indicate the same three object points in the two images and you can indicate a known dimension, you can determine other three dimension points in the images [4]. The photogrammetric three dimension coordinate determination is based on the co-linearity equation which simply states that object point, camera projective centre and image point lie on a straight line. The determination of the three dimension coordinates from a definite point is achieved through the intersection of two or more straight lines or observed by electronic total station instrument. Therefore, each point of interest should appear in at least two photographs then, coordinates are measured from three dimension model which is constituted by photogrammetric software [5]. Abdel Aziz and Karara proposed a simple method for close range photogrammetric data reduction with non- metric cameras; it establishes the direct linear transformation (DLT) between the two-dimensional coordinates, and the corresponding object- space coordinates [6]. The Direct Linear Transformation (DLT) between a point (X, Y, Z) in object space and its corresponding image space coordinates (x, y) can be established by the linear fractional equations: (1)
  • 3. The Accuracy of Determining the Volumes Using Close Range Photogrammetry DOI: 10.9790/1684-12271015 www.iosrjournals.org 12 | Page   yxpxrprkrkrkxx 2 22 1 6 3 4 2 2 1 2)2()( )2(22)( 2 21 6 3 4 2 2 1   yrpyxprkrkrkyy Where: L1, L2 L3…L11 are the transformation parameters X, Y and Z are the object space coordinates (2) Where: x- = x – xo y- = y – yo r2 = (x – xo)2 + (y – yo)2 (3) x, y are image coordinates p1 and p2 are two asymmetric parameters for de-centering distortion k1 , k2 and k3 are three symmetric parameters for radial distortion r is the radial distance from the principal point Equation 1 results from the equation of the central perspective in a trivial manner; Δx, Δy are systematic deformations of the image, i.e. deviations from the central perspective. Equation 1 can be solved directly for the 11 transformation parameters (L1, L2 L3 …L11) if there are at least six points in the image whose object-space coordinates are known. Equation 1 is rewritten to serve in a least squares formulation relating known control points to image coordinate measurements: vx = L1 X + L2 Y + L3 Z + L4 – xX L9 – xY L10 – xZ L11– Δx (4) vy = L5 X + L6 Y + L7 Z + L8– yX L9 – yY L10 – y Z L11– Δy IV. Test Field This test was carried out inside Kafr El-Sheikh University. A test field area has 361.5 m length, 234 m width and 3.25 m height (Figure 1). A test field consist of many stone heap has been used for volume calculation. Both traditional and photogrammetric methods have been applied for the volume calculation. Volume calculation results with traditional and photogrammetric methods are given in the next parts. Figure 1: A test field area inside Kafr El-Sheikh University Figure 2: The control points of test area based on traditional methods Figure 3: The contour map of test area based on traditional methods
  • 4. The Accuracy of Determining the Volumes Using Close Range Photogrammetry DOI: 10.9790/1684-12271015 www.iosrjournals.org 13 | Page Volume Computing By Traditional Methods Cross sections have been obtained with the points placed on the test field area. The 425 points coordinates were measured using SOKKIA SET330RK electronic total station instrument. The horizontal distribution of the points is seen in Figure 2. The accuracy of the SOKKIA SET330RK total station is 5 mm ± 2 mm/km and angle accuracy 3" [7] & [8]. Figure 3 is shown the contour map of test area based on the elevation of 425 points. The same points have been transferred to Surfer 10 software and from Trapezoidal Rule; volume of the test field has been calculated as 221475.14356249 m3 . According to Simpson's Rule, volume of the test field has been calculated as 221424.52105452 m3 . From Simpson's 3/8 Rule, the volume of test field has been calculated as 221484.05385912 m3. The three dimension map of test area based on traditional methods is shown in figures 4 and 5. Volume Computing By Photogrammetic Methods To carry out photogrammetric evaluation, 42 control points (20cm*20cm) in white and black have been used to make it easy to see. The coordinates of the control points have been measured with SOKKIA SET330RK electronic total station instrument. The horizontal distribution of the points is seen in Figure 6. Figure 4: Three dimension map of test area based on traditional methods Figure 5: Three dimension map of test area based on traditional methods Figure 6: The control points of test area based on photogrammetric method Figure 7: The contour map of test area based on photogrammetric methods
  • 5. The Accuracy of Determining the Volumes Using Close Range Photogrammetry DOI: 10.9790/1684-12271015 www.iosrjournals.org 14 | Page Taking photographs has been done with the Nikon D 3100 digital camera (Figure 10) in 14.2 mega pixel resolution. Camera calibration procedures have been completed for mathematical calculations. Photographs have been transferred to Photomodeler software, photogrammetric evaluation has been accomplished 574 coordinate points have been obtained. PhotoModeler is a software application that performs image based modeling and close range photogrammetry producing three dimension models and measurements from photography. PhotoModeler, first publicly released in 1993, was the first commercial all digital close range photogrammetry and image based modeling system Figure 10: The Nikon D 3100 digital camera PhotoModeler creates accurate three dimension models (consisting of Points, Lines, Curves, Edges, Cylinders, Surfaces and Shapes), and accurate three dimension measurements from photographs taken with most standard cameras (either digital or film). Three dimension models can be created and exported with photographic textures extracted from the original photographs [9]. The 574 control points obtained have been transferred to Surfer software and Figure 7 shown the contour map of test area based on photogrammetric methods. The volume of test field has been calculated. From Trapezoidal Rule; volume of the test field has been calculated as 215310.5960983 m3 . According to Simpson's Rule, volume of the test field has been calculated as 215300.43158866 m3 . From Simpson's 3/8 Rule, the volume of test field has been calculated as 215304.11889089 m3 . The three dimension model of test area based on photogrammetric methods is seen in Figures 8 & 9. Tables 1 summarize the obtained volume in previous cases. Table 1 summarizes the obtained volumes result by the traditional and photogrammetric methods and percentage of accuracy for photogrammetric method. Table 1: The volume calculation according to traditional and photogrammetric methods. Methods Traditional Method Photogrammetric Method Difference (m3) Accuracy (%) No. of Control Points Volume (m3) No. of Control Points No. of Generated Points Volume (m3) Trapezoidal Rule 425 221475.1436 42 574 215310.5961 6164.5475 97.2166 Simpson's Rule 221424.5211 215300.4316 6124.0895 97.23423 Simpson's 3/8 Rule 221484.0539 215304.1189 6179.935 97.20976 Figure 8: three dimension map of test area based on photogrammetric methods Figure 9: three dimension map of test area based on photogrammetric methods
  • 6. The Accuracy of Determining the Volumes Using Close Range Photogrammetry DOI: 10.9790/1684-12271015 www.iosrjournals.org 15 | Page V. Conclusions The photogrammetric method has been used and the obtained accuracy is discussed and presented. When experimental results examined photogrammetric method could approach 97.21% ratio to the traditional method value. Based on the experimental results, the following conclusions can be drawn: - The photogrammetric method is efficient to determination the volumes; - The photogrammetric method has proved to be fast and useful to estimate the volume with compared by traditional method; - The accuracy of photogrammetric method to determine the volume can be given good results 97.21% in comparison with traditional method. - The 10% of ground control points using to determine the volume was saved by photogrammetric method with compared by traditional method which determine the same accuracy. References [1]. Marek Bajtala, Peter Brunčák, Jozef Kubinec, Miroslav Lipták, Štefan Sokol, “THE PROBLEM OF DETERMINING THE VOLUMES USING MODERN SURVEYING TECHNIQUES” journal of inter disciplinary research, 2012. [2]. Hossam El-Din Fawzy “The Accuracy of Mobile Phone Camera Instead of High Resolution Camera In Digital Close Range Photogrammetry” International Journal of Civil Engineering & Technology (IJCIET), Volume 6, Issue 1, 2015, pp. 76-85, ISSN Print: 0976 – 6308, ISSN Online: 0976 – 6316. [3]. Yakar M., Yilmaz H.M., “ USING IN VOLUME COMPUTING OF DIGITAL CLOSE RANGE PHOTOGRAMMETRY” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B3b. Beijing 2008. [4]. Yakar M., Yilmaz H.M. and Mutluoglu O., “Close range photogrammetry and robotic total station in volume calculation” International Journal of the Physical Sciences Vol. 5 (2), pp. 086-096, February, 2010. [5]. Kraus K (1992). “Photogrammetry, Advanced Methods and Applications” Foutrh Ed., Vol. II, Dümmler, Bonn. [6]. Abdel-Aziz, Y. I. and Karara, H. M., 1971 “Direct Linear Transformation from Comparator Coordinates into Object Space Coordinates in Close Range Photogrammetry” Proceedings of the ASP/UI Symposium on Close-Range Photogrammetry, Urbana, Illinois, pp. 1-18. [7]. Hossam El-Din Fawzy “Evaluate The Accuracy Of Reflector-Less Total Station” International Journal of Civil Engineering & Technology (IJCIET), Volume 6, Issue 3, 2015, pp. 23-30, ISSN Print: 0976 – 6308, ISSN Online: 0976 – 6316. [8]. https://www.scribd.com/doc/205256016/Manual-Sokkia-Series-30rk-Set230rk-Rk3-Set330rk-Rk3-Set530rk-Rk3-Set630rk-En, 2015. [9]. http://en.wikipedia.org/wiki/PhotoModeler, 2015.