Detection and Classification of Root and Butt-Rot (RBR) in Stumps of Norway Spruce Using RGB Images and Machine Learning
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Bruce Talbot | Stefano Puliti | Ola Ringdahl | Ahmad Ostovar | Rasmus Astrup | S. Puliti | B. Talbot | R. Astrup | Ola Ringdahl | A. Ostovar
[1] Thomas Hellström,et al. Human Detection Based on Infrared Images in Forestry Environments , 2016, ICIAR.
[2] Marek Pierzchała,et al. Applications of Remote and Proximal Sensing for Improved Precision in Forest Operations , 2017 .
[3] J. Stenlid,et al. Controlling and predicting the spread of heterobasidion annosum from infected stumps and trees of picea abies , 1987 .
[4] Siegfried Fink,et al. Detection of incipient decay in tree stems with sonic tomography after wounding and fungal inoculation , 2008, Wood Science and Technology.
[5] Claudio A. Perez,et al. A neurofuzzy color image segmentation method for wood surface defect detection , 2005 .
[6] Kaiming He,et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[7] Heng-Da Cheng,et al. Color image segmentation based on homogram thresholding and region merging , 2002, Pattern Recognit..
[8] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[9] Magnus Thor,et al. Heterobasidion annosum root rot in Picea abies: Modelling economic outcomes of stump treatment in Scandinavian coniferous forests , 2006 .
[10] Juha Hyyppä,et al. Outlook for the Next Generation’s Precision Forestry in Finland , 2014 .
[11] Josef Kittler,et al. A Comparative Study of Hough Transform Methods for Circle Finding , 1989, Alvey Vision Conference.
[12] Pietro Perona,et al. Microsoft COCO: Common Objects in Context , 2014, ECCV.
[13] Thomas Hellström,et al. Adaptive Image Thresholding of Yellow Peppers for a Harvesting Robot , 2018, Robotics.
[14] V. Lygis,et al. Planting Betula pendula on pine sites infested by Heterobasidion annosum: disease transfer, silvicultural evaluation, and community of wood- inhabiting fungi , 2004 .
[15] Sathishkumar Samiappan,et al. Post-Logging Estimation of Loblolly Pine (Pinus taeda) Stump Size, Area and Population Using Imagery from a Small Unmanned Aerial System , 2017 .
[16] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[17] Trevor Darrell,et al. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[18] Patrick K. Simpson,et al. Fuzzy min-max neural networks - Part 2: Clustering , 1993, IEEE Trans. Fuzzy Syst..
[19] Charles C. Brunner,et al. Image segmentation algorithms applied to wood defect detection , 2003 .
[20] J. Stenlid,et al. Conifer root and butt rot caused by Heterobasidion annosum (Fr.) Bref. s.l. , 2005, Molecular plant pathology.
[21] Christopher Hunt,et al. Notes on the OpenSURF Library , 2009 .
[22] Pa Estevez,et al. Genetic input selection to a neural classifier for defect classification of radiata pine boards , 2003 .
[23] Terje Gobakken,et al. Accurate single-tree positions from a harvester: a test of two global satellite-based positioning systems , 2017 .
[24] Magnus Karlberg,et al. Simulated continuous mounding improvements through ideal machine vision and control , 2016 .
[25] Ali Farhadi,et al. YOLOv3: An Incremental Improvement , 2018, ArXiv.
[26] Yuan Zhong. Image segmentation for defect detection on veneer surfaces , 1994 .
[27] Kari T. Korhonen,et al. Occurrence of heterobasidion annosum in pure and mixed spruce stands in Southern Finland , 1990 .
[28] Heimo Ihalainen,et al. Measurement of annual ring width of log ends in forest machinery , 2008, Electronic Imaging.
[29] Heinrich Spiecker,et al. Detection and Classification of Norway Spruce Compression Wood in Reflected Light by Means of Hyperspectral Image Analysis , 2009 .
[30] Sten Gellerstedt. Operation of the Single-Grip Harvester: Motor-Sensory and Cognitive Work , 2002 .
[31] Thomas Seifert,et al. Simulating the extent of decay caused by Heterobasidion annosum s. l. in stems of Norway spruce , 2007 .
[32] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[33] S. Puliti,et al. Tree-Stump Detection, Segmentation, Classification, and Measurement Using Unmanned Aerial Vehicle (UAV) Imagery , 2018 .
[34] Keiichi Yamada,et al. A shape-independent method for pedestrian detection with far-infrared images , 2004, IEEE Transactions on Vehicular Technology.
[35] Thomas Hellström,et al. Detection of Trees Based on Quality Guided Image Segmentation , 2014 .
[36] Denis Laurendeau,et al. Extraction of texture features with a multiresolution neural network , 1992, Defense, Security, and Sensing.
[37] Gabriela Csurka,et al. Visual categorization with bags of keypoints , 2002, eccv 2004.
[38] N. Otsu. A threshold selection method from gray level histograms , 1979 .
[39] Pablo A. Estévez,et al. Automated visual inspection system for wood defect classification using computational intelligence techniques , 2009, Int. J. Syst. Sci..
[40] Selman Jabo. Machine vision for wood defect detection and classification , 2011 .