3D Plant Modelling via Hyperspectral Imaging

Plant phenomics research requires different types of sensors be employed to measure the physical traits of plant surface and to estimate the plant biomass. Of particular interest is the hyperspectral imaging device which captures wavelength indexed band images that characterise material properties of objects under study. In this paper, we introduce a proof of concept research that builds 3D plant model directly from hyperspectral images captured in a controlled lab environment. We show that hyperspectral imaging has shown clear advantages in segmenting plant from its background and is promising in generating comprehensive 3D plant models.

[1]  Shahram Izadi,et al.  Modeling Kinect Sensor Noise for Improved 3D Reconstruction and Tracking , 2012, 2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization & Transmission.

[2]  Ayan Chakrabarti,et al.  Statistics of real-world hyperspectral images , 2011, CVPR 2011.

[3]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[4]  M. Tester,et al.  Phenomics--technologies to relieve the phenotyping bottleneck. , 2011, Trends in plant science.

[5]  Changchang Wu,et al.  Towards Linear-Time Incremental Structure from Motion , 2013, 2013 International Conference on 3D Vision.

[6]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[7]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[8]  Neelam Gupta,et al.  Hyperspectral imager development at Army Research Laboratory , 2008, SPIE Defense + Commercial Sensing.

[9]  Long Quan,et al.  Image-based plant modeling , 2006, ACM Trans. Graph..

[10]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Min H. Kim,et al.  3D imaging spectroscopy for measuring hyperspectral patterns on solid objects , 2012, ACM Trans. Graph..

[12]  Clive H. Bock,et al.  Plant Disease Severity Estimated Visually, by Digital Photography and Image Analysis, and by Hyperspectral Imaging , 2010 .

[13]  Luca Poletto,et al.  A System for 3D Modeling Frescoed Historical Buildings with Multispectral Texture Information , 2006, Machine Vision and Applications.

[14]  Takahiro Okabe,et al.  Camera spectral sensitivity estimation from a single image under unknown illumination by using fluorescence , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Herbert Edelsbrunner,et al.  Detailed reconstruction of 3D plant root shape , 2011, 2011 International Conference on Computer Vision.

[16]  Derek Nowrouzezahrai,et al.  Learning hatching for pen-and-ink illustration of surfaces , 2012, TOGS.

[17]  Diego Viejo,et al.  3D geological modelling using laser and hyperspectral data , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[18]  Steven M. Seitz,et al.  Multicore bundle adjustment , 2011, CVPR 2011.

[19]  Jun Zhou,et al.  Efficient Estimation of Reflectance Parameters From Imaging Spectroscopy , 2013, IEEE Transactions on Image Processing.

[20]  Jun Zhou,et al.  Salient object detection in hyperspectral imagery , 2013, 2013 IEEE International Conference on Image Processing.

[21]  Jun Zhou,et al.  MILIS: Multiple Instance Learning with Instance Selection , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.