2014 IEEE 5th Control and System Graduate Research Colloquium, Aug. 11 - 12, UiTM, Shah Alam, Malaysia
Data Acquisition and Representation of Leaves using Digital Close Range Photogrammetry for Species Identification Muhd Safarudin Chek Mat*1, Jezan Md Diah*2, Mokhtar Azizi Mohd Din#3 & Abd. Manan Samad*1 Pixelgrammetry & Al-Idrisi Research Group (Pi_ALiRG) Green Technology & Sustainable Development (GTSD), UiTM-RMI Communities of Research (CoRe) *1
Centre of Studies Surveying Science and Geomatics Faculty of Architecture, Planning and Surveying *2 Faculty of Civil Engineering Universiti Teknologi MARA Malaysia, Shah Alam, SELANGOR #3
Department of Civil Engineering Faculty of Civil Engineering Universiti Malaya, Kuala Lumpur, WILAYAH PERSEKUTUAN Email:
[email protected] identification is important in all this effort. Therefore a tool is needed which could identify plants using easily available information. All information about flora such as species information is easily accessible, but one of the system that make it less efficient is a digital image of the species. Such as an example the image of the leaf species is in normal photo format. There is less operative and dynamic to be monitored and for advance use to analyze. This research is aimed to use the close-range Photogrammetry approach which has potential for mapping a leaf in accurate dimension. Leaves are the important part of trees and forest, different levels have different textures and vein, characters, the high realistic leaf modelling and dynamic simulation has become the focus of virtual botany study [3]. Close-range Photogrammetry is an accurate, cost effective technique of collecting measurements of real world objects and conditions directly from photographs [4,5]. Close range Photogrammetry is done using photographs taken at close range, often less than 300 meters and is used for detailed three dimensional renderings and plotting of small-scale features and objects [6].
Abstract - The surveying technique of close-range Photogrammetry is based on an approach of representation of the image forming mechanism of photography and extract spatial information through computation on photos. This research used the fundamentals of close-range Photogrammetry and is applied to model leaves, measure the geometric model of leaf length and width and compare with the conventional measurement method. Ringed Automated Detection (RAD) coded target are applied in this research. RAD coded target offers automatic detection and matching of points across multiple photographs. The process has a better opportunity to execute with less error even in complex conditions. The process is much faster without worrying about the residual error. The research took two species of leaves, Jelutong leaf, dyera constulata species and Kulim leaf, scorodocarpus borneensis species. Three specimens are taken from each species to identify the shape size and range. These leaves are then taken in photographs and process the images using PhotoModeler software to extract the data. Index Terms -Close-range Photogrammetry, model leaves, measures the geometric model of leaves, ringed automated detection (RAD).
I. INTRODUCTION Plants are part of the global ecosystem that plays a vital role in human life and other lives on the earth. Environmental deterioration due to intensive development leads to several plant species are at the margins of extinction. So, there is an urgent need to conserve flora, the identification of varying plant varieties by botanists is a must, Tropical Forest Biodiversity Centre, which was established in Forest Research Institute Malaysia commonly known as FRIM to act as the plant diversity center in Malaysia now take the role for plant conservation and study [1]. Biological Diversity Clearing-House Mechanism (CHM) website was developed and conducted by FRIM is an effort to contribute information services for exchanging and integrating information on biodiversity database Malaysia to all global users [1,2]. Flora classification and recognition for plant
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II. AIM AND OBJECTIVES
The studies aim to use the potential of close-range Photogrammetry to mapping a leaf which able to classify and analyze species identification for advance use in biodiversity research. The objectives of this research include:
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To mapping accurate leaves model similar with real leaf dimension features.
x
To compare the geometry accuracy of real leaf with leaf model produce from close-range Photogrammetry technique.
2014 IEEE 5th Control and System Graduate Research Colloquium, Aug. 11 - 12, UiTM, Shah Alam, Malaysia
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To recognize leaves model based on a shape using close range Photogrammetry is effective for species identification.
Digital single-lens reflex (DSLR) Canon EOS 1100D has been chosen for this project. The device is chosen based on its specification ability and project budget. DSLR is a better choice because have more control setting usually the lens. Lens can be fixed, non-zoom, wide angle and have higher resolution it is a suite with PhotoModeler software requirement. Sensor photo detectors up to 12.2 megapixel a Canon EOS 1100D is a high performance camera and suitable for capturing the leaf image.
III. METHODOLOGY The workflow of this research (Fig. 1) involves in this research activity to obtain the final output of this research.
Project Planning Problem Understanding
Camera, Software & Leaves Selection
Camera
Camera Calibration
Fig. 2. Canon EOS 1100D.
Data Collection
TABLE 1. CHARACTERISTICS OF THE CANON EOS 1100D Leaves Information
Image Capturing
1st Stage 2nd Stage Data Processing
EOS 1100D
Body Type Max resolution Image ratio Effective pixels Sensor size Auto focus Manual focus
Compact SLR 4272x2848 3:2 12.2 megapixels 22.2 x 14.7 mm Yes Yes
x
Data Analysis and Finding
Software
Photomodeler Scanner Version 2013 has been chosen as a platform to assist the process of photographs taken. The ability of this software in applying in many branches, like accident reconstruction, biology, archaeology, architecture and preservation make this software as a right choice [7]. PhotoModeler software provides image-based modelling for accurate 3D model. As suggested by researcher that used this software before, it is the best software that uses in close-range Photogrammetry with good criteria such as user friendly instruction, simple step selection and give a high precision output at the end of the work.
Fig. 1. Flowchart of research methodology.
A. Project Planning Project planning is an initial stage of this research. This will covers problem understanding, camera, software and leaves selection and last, camera calibration. Project planning is the first step of organization plans in managing structure of research. It will lead this research to achieve aim and objectives. x
Features
x
Problem Understanding
Leaves selection
Leaves are essential object in this project was the main object to test the potential to model using close-range Photogrammetry. Two different leaf species are selected in this project. Jelutong leaf, scientific name is dyera constulata and another one is Kulim leaf, scientific name is scorodocarpus borneensis are selected in this project. These two species are selected based on the source of information and ready available at FRIM. Three samples taken from each species to identify the shape and range size.
Problem understanding is necessary in order to solve the problem in an efficient way. The lack operative of the leaf image is a main problem. A tool and method is needed to give a better service of the leaves data. Close-range Photogrammetry is well known technique that acquiring the information using photographs. This research will assess the potential of close-range Photogrammetry to model the leaves and provides a good data for botanical studies.
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2014 IEEE 5th Control and System Graduate Research Colloquium, Aug. 11 - 12, UiTM, Shah Alam, Malaysia
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target. There are 999 unique codes on each coded target offer for enhanced high accuracy project.
Camera calibration
Camera calibration is an important phase for non - metric cameras as being a necessary element for photogrammetric assessment [7]. The purpose of camera calibration is to determine the focal length, principal point, and lens distortion from the camera. This process is important to figure out the unstable element in the camera like interior orientation and lens distortion parameters. These parameters are required in the process of 3D modelling in PhotoModeler software. Hence, is important to carry out the process of calibration to figure out the value of the parameters of the camera.
Fig. 4. RAD coded target with unique codes.
The RAD coded targets are placed on each side the rectangle plate which white background paper covered to ensure the object taken is less noise and able to get high accuracy result. Seven coded target in each side which length 19.2cm. The known point is needed to identify the scale of the object later.
B. Data Collection Data Collection involves with leaves information and image capturing. x
Leaves information
This is the most important part in completing the research, without the data the project will disrupt. In this section all information to mobilize this research is collected from various sources such as supervisor, journal, article, newspaper, books, website and other relevant sources. The information of leaves is collected at FRIM. x Conventional Measurement of Leaves
Fig.5. RAD coded targets placed on a rectangle white plate.
Leaves Position
The conventional measurement of leaves is using ruler. The leaves length and width are measured from RAD coded target point to ensure the bias error of measurement is minimized.
Three pairing of each species is taken to check the consistency of the model feature shape, width and length. Four RAD coded targets are placed on the leaf surface to identify the length and width. The targets are more fixed and easy to measure with high accuracy value attempt. The graphical example as below:
Fig.3. Measuring leaf length and width using ruler.
Fig.6 (a). Jelutong leaves specimens.
x Capturing Leaves Image RAD Coded Target and Scale Plate In this project the Ringed Automatically Detected Target (RAD) is used in the scene of photographs captures. The targets are identified automatically by the program from the images. RAD coded target release by EOS System, which able to automatic detection and matching of point across multiple photographs. The high speed process of automatic referencing and orientation with less residual error now occur with this
Fig.6 (b). Kulim leaves specimens.
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2014 IEEE 5th Control and System Graduate Research Colloquium, Aug. 11 - 12, UiTM, Shah Alam, Malaysia
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IV. ANALYSIS AND FINDINGS
Position, Angle and Distance of Camera Capturing Leaves
A. Camera Calibration Parameter Results:
The position of the camera is taken upward the object. Four or five images are snap and must be in stereo photo. The position of camera station CH1 to CH2 angle is below 20° as well as camera station CH3 and Ch4 as shown in (Fig.7).The distance from the images taken is set to be the range of 3040cm due to the size of the object.
The underlying parameter inside the camera, including the focal length, height, format, format, width, principal point and lens distortion are shown in Table 2. TABLE 2. CAMERA CALIBRATION PARAMETERS
Fig.7. Camera position, angle and distance from object.
Parameters Focal Length
Precision/Standard Deviation 18.9859mm
Format Size (Height x Width)
15.1130mm x 22.6617mm
Principal Point
Xp Yp
Radial Distortion
K1 K2 K3
266e004 -1.249e-006 0.000e+000
Decentring Distortion
P1 P2
-8.226e-005 6.958e-005
11.3162mm 7.6629mm
C. Data Processing B. Leaves Model Representation:
The process of photo-based 3D scanning or photogrammetric scanning provides results similar to a 3D laser scanner. This 3D scanning algorithm produces a dense point cloud from stereo pair photographs of textured surfaces. The process scans the surface and measure the 3D location of that point. If the intervals are small, scanner gets a dense cloud point and accurate representation of the surface in digital 3D model [8].
The model of leaves resulting from PhotoModeler Scanner is dynamic and has unique features. x
The Display of All Samples Leaves Models Generated
Then, post processing involves to filtering, hole filling, smoothing and meshing/triangulation the dense point clouds to completing and beautify the surface with accurate measurements. Lastly the quality texture can be applied to the meshed point cloud to produce as-built texture.
Fig.9 (a). Jelutong leaves generated using PhotoModeler.
Capture Image
Scanned Point Cloud
Fig.9 (b). Kulim leaves generated using PhotoModeler. Quality Texture Result
Meshed Point Cloud
Based on the leaves model generated, showed a clear and dynamic representation. The leaves model can be enlarged,
Fig.8. Photo-based 3D scanning process.
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2014 IEEE 5th Control and System Graduate Research Colloquium, Aug. 11 - 12, UiTM, Shah Alam, Malaysia
TABLE 6. KULIM LEAVES MEAN MEASUREMENT COMPARISON
zooming into certain areas and rotated. The model is really fine to identify the particular structure of leaves veins and shape. This very useful for a botanist to recognize the species and see the leaf spot diseases. C. Comparison between Conventional and Close-Range Photogrammetry Measurement TABLE 3. JELUTONG LEAVES MEASUREMENT COMPARISON Sample
Parameter
Conventional
CRP Method
Difference (+/-)
(%)
Length (cm)
20.500
20.381
0.119
0.580
Width (cm)
6.500
6.493
0.007
0.108
Length (cm)
22.000
21.999
0.111
0.505
Width (cm)
6.900
6.849
0.051
0.739
Length (cm)
20.400
20.187
0.213
1.044
Width (cm)
7.600
7.444
0.156
2.053
1
2
3
Parameter
Conventional
CRP Method
Difference
(%)
Length (cm)
18.400
18.105
0.295
1.603
Width (cm)
8.600
8.532
0.068
0.791
Length (cm)
16.500
16.542
0.042
0.255
Width (cm)
8.900
8.838
0.062
0.697
Length (cm)
19.700
19.776
0.076
0.386
Width (cm)
10.200
10.104
0.096
0.941
1
2
3
Percentage (%)
Length (cm) Width (cm)
20.967
20.856
0.111
0.529
7
6.929
0.071
1.014
18.200
18.141
0.059
0.324
9.200
9.158
0.042
0.457
Sample
Parameter
1
Length Width
Percentage Comparison (%) 1.603 0.791
2
Length Width
0.255 0.697
Length
0.386
3
Width
0.941
Overall RMS (pixels) 0.145
0.112
0.241
The large overall RMS value makes percentage comparison also large. The difference between small overall RMS as Sample 2 (0.112 pixels) and large overall RMS Sample, 3 (0.241 pixels) affects the percentage comparison which small overall RMS makes percentage comparison small and large overall RMS make a percentage comparison also large.
COMPARISON Difference (+/-)
Length (cm) Width (cm)
AND OVERALL RMS VALUE
TABLE 5. JELUTONG LEAVES MEAN MEASUREMENT CRP Method
Percentage (%)
TABLE 7. JELUTONG LEAVES THE PERCENTAGE COMPARISON
D. Mean Comparison between Conventional and Close-Range Photogrammetry Measurement
Conventional
Difference (+/-)
A RMS residual is the distance in pixels between where the points were marked by user or automatically on a photo and where the projection of the 3D point, associated with that marker point, falls on the photo. The relationship between the leaves percentage comparison and overall RMS are recorded in Table 7 and Table 8.
Table 3 and 4 showed a comparison measurement between both species. The difference is about 0.2cm to 0.007cm which belong to the small difference. The accuracy of the measurement is affected on many reasons, as camera calibration, the environment during taking a picture, the position of the camera and while processing the data. Although the accuracy depends on the process involved, the measurement still under acceptable values.
Parameter
CRP Method
E. Relationship Overall Root Mean Square (RMS) Factor with Measurement Comparison
Percentage
(+/-)
Conventional
Table 5 and 6 noted the mean measurement for both species. The mean is from a three samples from each species. The difference is between 0.1cm to 0.04cm. This result refines the comparison measurement is small and resolve the digital close range Photogrammetry technique is applicable for providing reliable leaves data.
Percentage
TABLE 4. KULIM LEAVES MEASUREMENT COMPARISON Sample
Parameter
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2014 IEEE 5th Control and System Graduate Research Colloquium, Aug. 11 - 12, UiTM, Shah Alam, Malaysia
TABLE 8. KULIM LEAVES THE PERCENTAGE COMPARISON AND
ACKNOWLEDGEMENTS
OVERALL RMS VALUE Sample
Parameter
Percentage (%)
1
Length Width
0.580 0.108
0.224
2
Length Width
0.505 0.739
0.150
Length
1.044
3
Width
2.053
Pixelgrammetry and Al-Idrisi Research Group (Pi_ALiRG); Green Technology & Sustainable Development (GTSD), UiTM-RMI Communities of Research (CoRe); UiTM Research and Management Institute (RMI-UiTM); Ministry of Higher Education (MOHE), FRGS RESEARCH GRANT [600-RMI / FRGS 5/3 (110/2012)] and ERGS RESEARCH GRANT [600-RMI / ERGS 5/3 (49/2013)]; Centre of Studies Surveying Science and Geomatics, Faculty of Architecture, Planning and Surveying, UiTM Shah Alam and Forest Research Institute Malaysia (FRIM) are greatly acknowledged
Overall RMS (pixels)
0.359
The difference between small overall RMS as Sample 2 (0.150 pixels) and large overall RMS Sample 3 (0.359 pixels) affects the percentage comparison which small overall RMS makes percentage comparison small and large overall RMS make a percentage comparison also large.
REFERENCES [1] Tropical Forest Biodiversity Centre (TFBC), Forest Biodiversity Division, Forest Research Institute Malaysia (FRIM).(2013). Conserving the Floristic Heritage in Malaysia. Retrieved from http://www.tfbc.frim.gov.my/welcome.html on 27 Mac 2013. [2] Convention on Biological Biodiversity.(2013). Clearing House Mechanism Mission. Retrieved fromhttps://www.cbd.int/chm on 27 Mac 2013.
VI. CONCLUSION In the conclusion, it has been shown that photogrammetric technique has the capability for representation and acquiring data from leaves. In this research study, camera calibration is the essential. It functions to determine information about the camera that improves accuracy in subsequent projects. The next approach is selected of the leaf specimen. Two species are chosen in this study, which are Jelutong leaf, scientific name is dyera constulata and another one is Kulim leaf, scientific name is scorodocarpus borneensis. Three samples of each species are selected to identify the consistency of shape and size. The leaves specimen and information are taken from FRIM. The leaves then are captured about four to five images per leaf. The stereo images are taken with angle below 20° and distance is about 30cm to 40cm from the object. These are basic requirement technique of capturing data for better result in photo-based 3D scanning. The digital images are processed using PhotoModeler Scanner software. The result showed the use of Photogrammetry in representing a leaf in digital model is more dynamic and useful. The precision obtained in the leaves models also small compared with conventional measurement. Digital close range Photogrammetry is not merely to supply the data related to the coordinate position even its capability to supply a reliable data in the forestry sector. The digital model of leaves can be utilised as a stored database for future extensions and share among institution for study and identification. Furthermore, on screen leaf spot disease also can be recognized. An expeditious development in computer engineering gives a lot of advantages to provide a substantial value of representation leaves model data.
[3] Huai,Y., Li, J., Wang, L., and Yang, Gang. (2009). Plant Leaf Modelling and Rendering Based-on GPU. Information Science and Engineering (ICISE), 2009 1th International Conference. Nanjing, China. pp 1372 – 1375. [4] Anuar Ahmad. (2010). Digital Photogrammetry: An experience of processing aerial photograph of UTM acquired using digital camera. Thesis article, Faculty of Geoinformation Science and Engineering, University Technology Malaysia, Skudai, Johor. [5] Pedro Arias, J.A., Vallejo, J., and Lorenzo, H. (2009). Close range digital photogrammetry and software application development for planar patterns computation. Vol.76, Issue 160, pp 7-15. [6] Wolf, P.R and Dewith, B.A. (2004). Element of Photogrammetry with Application in GIS. 3rd Edition, McGraw Hill, USA. [7] Mohd. Faizury Abol Hassan, Ismail Ma'arof and Abd. Manan Samad. (2014). Assessment of Camera Calibration Towards Accuracy Requirement. Colloquium on Signal Processing & its Applications (CSPA), 2014 10th IEEE International Colloquium. Kuala Lumpur, Malaysia. pp 123 – 128. [8] Walford, A. (2013). PhotoModeler homepage website. Retrieved from .http://www.photomodeler.com/index.html on 21 May 2013.
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