6533b828fe1ef96bd1288609
RESEARCH PRODUCT
Region-based segmentation on depth images from a 3D reference surface for tree species recognition.
Christophe StolzLew F.c. Lew Yan VoonAhlem OthmaniAlexandre PibouleNicolas Loméniesubject
[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingComputer science[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingFeature extractionPoint cloudComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-space segmentation[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing02 engineering and technology[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Minimum spanning tree-based segmentation[STAT.AP] Statistics [stat]/Applications [stat.AP][INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[ INFO.INFO-TI ] Computer Science [cs]/Image Processing0202 electrical engineering electronic engineering information engineeringSegmentationComputer vision[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing[STAT.AP]Statistics [stat]/Applications [stat.AP]Contextual image classificationbusiness.industry[ STAT.AP ] Statistics [stat]/Applications [stat.AP][INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]020207 software engineeringPattern recognitionImage segmentation15. Life on landdepth image segmentationRandom forestdepth images from 3D point cloudsIEEE[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV][INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]020201 artificial intelligence & image processingsingle tree species recognitionArtificial intelligenceRange segmentationbusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingForest inventorydescription
International audience; The aim of the work presented in this paper is to develop a method for the automatic identification of tree species using Terrestrial Light Detection and Ranging (T-LiDAR) data. The approach that we propose analyses depth images built from 3D point clouds corresponding to a 30 cm segment of the tree trunk in order to extract characteristic shape features used for classifying the different tree species using the Random Forest classifier. We will present the method used to transform the 3D point cloud to a depth image and the region based segmentation method used to segment the depth images before shape features are computed on the segmented images. Our approach has been evaluated using two datasets acquired in two different French forests with different terrain characteristics. The results obtained are very encouraging and promising.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2013-09-15 |