Analysis of Grass Area Aerial Image Set

Sutthiphong Spot Srigrarom, Yap Zhan Hong, Lee Meng Da, Cheong Kah Yuen

Abstract


In the recent years, the emergence of drone industry have expanded its utilities purpose in the commercial world. When coupled with computer vision, it can provide useful analysis of the aerial image set taken from the drone, and have since benefited across various industries such as agriculture and land surveillance. For a land surveillance mission, not only does it have the possibility of producing results with higher accuracy, it will also reduce the number of manpower and time required if similar tasks were carried out manually. Singapore is known as a City in a Garden due to the huge amount of greeneries spread across the country. Many of these greeneries consists of large empty grass patch which need maintenance work by hired contractors and visual checks by agency officers every few weeks. With a few thousands site large and small, the amount of manpower required is tremendous. In this paper we propose a solution that can resolve the manpower issue and offer visual analysis through computer vision methods.

Keywords


Grass area monitoring, Structure from motion, Photogrammetry

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References


C. Tomasi, T. Kanade “Detection and Tracking of Point Features” Technical Report CMU-CS-91-132 1991. Available: https://cecas.clemson.edu/~stb/

klt/tomasi- kanade-techreport-1991.pdf

J. Shi, C. Tomasi “Good features to track” IEEE Conference on Computer Vision and Pattern Recognition (CVPR94) 1994. Available: https://pdfs.

semanticscholar.org/2eb6/74b39b2e2505663c75f92d1ccffb4311cb4b.pdf

P.H.S Torr, A. Zisserman “MLESAC: A new robust estimator with application to estimating image geometry” Oxford University 1995. Available: https://www.robots.ox.ac.uk/~vgg/publications/2000/Torr00/torr00.pdf

R. Hartley, P. Sturm “Triangulation” Computer Vision and Image Understanding Volume 68, Issue 2 1997, Pg 146-157. Available: https://users.cecs.a

nu.edu.au/~hartley/Papers/triangulation/triangulation.pdf

R. Hartley, A. Zisserman “Linear triangular methods” Multiple View Geometry in Computer Vision Second Edition 2004, Pg 312-321. Available: http://cvrs.wh

u.edu.cn/downloads/ebooks/Multiple%20View%20Geometry%20in%20Computer%20Vision%20(Second%20Edition).pdf

D. Kendal, C.E. Hauser, G.E. Garrard, S. Jellinek, K.M. Giljohann, J.L. Moore “Quantifying Plant Colour and Colour Difference as Perceived by Humans Using Digital Images” PMC 2013. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3748102/

J.K. Gillan, J.W. Karl, M. Duniway, A. Elaksher “Modeling vegetation heights from high resolution stereo aerial photography: An application for broad-scale rangeland monitoring” Journal of Environmental Management 2014, Pg 226-235

F. Leberl, A. Irschara, T. Pock, P. Meixner, M. Gruber, S. Scholz, and A. Wiechert “Point Clouds: Lidar versus 3D Vision” Photogrammetric Engineering & Remote Sensing Volume 76 2010, Pg 1123-1134. Available: https://pure.tugraz.at/ws/p

ortalfiles/portal/1372794

S. Pravenaa, R. Menaka “A Methodical Review on Image Stitching and Video Stitching Techniques,” International Journal of Applied Engineering Research Volume 11, no. 5 2016, Pg 3442-3448

C. Baillard, H. Maitre, “3-D Reconstruction of Urban Scenes from Aerial Stereo Imagery: A Focusing Strategy,” Computer Vision and Image Understanding Volume 76 no. 3, 1999, Pg 244-258

S. Zlatanova, J. Paintsil and K. Tempfli, “3D Object Reconstruction From Aerial Stereo Images” International Institute for Aerospace Survey and Earth Science (ITC). Available: https://otik.uk.zcu.c

z/bitstream/11025/15944/1/zlatanova_98.pdf

S. Mizoe, Y. Yaguchi, K. Takahashi, K. Ota, R. Oka “Reconstructing 3D Land Surface From a Sequence of Aerial Images,” IAPR Conference on Machine Vision Applications, Nara Japan, 2011

M. Mohan, C.A. Silva, C. Klauberg, P. Jat, G. Catts, A. Cardil, A.T. Hudak, M. Dia “Individual Tree Detection from Unmanned Aerial Vehicle (UAV) Derived Canopy Height Model in an Open Canopy Mixed Conifer Forest” MDPI Forests 2017, 8, 340; doi:10.3390/f8090340

“Desert View Aerial Photography” 2013, [Online] Available: https://dvaerialphoto.com/oblique-vs-vertical-aerial-photography-infographic/

P. King, B. Anstey, A. Vardy “Comparison of feature detection techniques for AUV navigation along a trained route” IEEE 10.23919/OCEANS.2013.67410

, 2014

F. Ali, S.U. Khan, M.Z. Mahmudi, R. Ullah, “A Comparison of FAST, SURF, Eigen, Harris, and MSER Features” International Journal of Computer Engineering and Information Technology Volume 8, no. 6 2016, Pg 100-105

M.O. Fril, E. Jones, M. Glavin, C. Hughes, “Comparison of Feature Detection Methods for an Automotive Camera System” ISSC 2007. Available: https://www.researchgate.net/publication/251403979_Comparison_of_Feature_Detection_Methods_for_an_Automotive_Camera_System

M.A. Fischler, R. C. Bolles, “Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography” Communication of ACM Volume 24, no. 6 1981. Available: https://www.sri.com/sites/d

efault/files/publications/ransac-publication.pdf

“Lecture 7: RANSAC and Minimal Solvers” 2013. [Online] Available: http://www.maths.lth.se/mate

matiklth/personal/calle/datorseende13/notes/forelas7.pdf

R. Hartley, A. Zisserman “Epipolar Geometry and the Fundamental Matrix” Multiple View Geometry in Computer Vision, Cambridge, Cambridge University Press 2004, Pg 219-243

G.H. Georgiev, V.D. Radulov “A Practical Method for Decomposition of the Essential Matrix” Applied Mathematical Sciences, Volume 8, no. 176 2014. Available: http://www.m-hikari.com/ams/a

ms-2014/ams-173-176-2014/georgievAMS173-176-2014.pdf

H. Stachel “Descriptive Geometry Meets Computer Vision — The Geometry Of Multiple Images” 12TH International Conference On Geometry And Graphics 2006. Available: https://pdfs.semanticsc

holar.org/093b/e28210c3e41725265d0d4dcba39877d278a2.pdf


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