Automatic calculation of tree diameter from stereoscopic image pairs using digital image processing.

Automatic operations play an important role in societies by saving time and improving efficiency. In this paper, we apply the digital image processing method to the field of lumbering to automatically calculate tree diameters in order to reduce culler work and enable a third party to verify tree diameters. To calculate the cross-sectional diameter of a tree, the image was first segmented by the marker-controlled watershed transform algorithm based on the hue saturation intensity (HSI) color model. Then, the tree diameter was obtained by measuring the area of every isolated region in the segmented image. Finally, the true diameter was calculated by multiplying the diameter computed in the image and the scale, which was derived from the baseline and disparity of correspondence points from stereoscopic image pairs captured by rectified configuration cameras.

[1]  P Réfrégier,et al.  Optimal snake-based segmentation of a random luminance target on a spatially disjoint background. , 1996, Optics letters.

[2]  P Refregier,et al.  Improvement in robustness of the statistically independent region snake-based segmentation method of target-shape tracking. , 1998, Optics letters.

[3]  Bahram Javidi,et al.  Depth-independent segmentation of macroscopic three-dimensional objects encoded in single perspectives of digital holograms. , 2007, Optics letters.

[4]  Richard E. Carson,et al.  The watershed algorithm: a method to segment noisy PET transmission images , 1998, 1998 IEEE Nuclear Science Symposium Conference Record. 1998 IEEE Nuclear Science Symposium and Medical Imaging Conference (Cat. No.98CH36255).

[5]  Shangbo Zhou,et al.  A Fast SIFT Feature Matching Algorithm for Image Registration , 2011, 2011 International Conference on Multimedia and Signal Processing.

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

[7]  G. Kishore Kumar,et al.  Automatic object searching system based on Real Time SIFT Algorithm , 2010, 2010 INTERNATIONAL CONFERENCE ON COMMUNICATION CONTROL AND COMPUTING TECHNOLOGIES.

[8]  Stephen L. Bacharach,et al.  The watershed algorithm: a method to segment noisy PET transmission images , 1998 .

[9]  Anna Fabijanska,et al.  A survey of thresholding algorithms on yarn images , 2010, 2010 Proceedings of VIth International Conference on Perspective Technologies and Methods in MEMS Design.

[10]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[11]  Nor Hazlyna Harun,et al.  Performance comparison between RGB and HSI linear stretching for tuberculosis bacilli detection in Ziehl-Neelsen tissue slide images , 2009, 2009 IEEE International Conference on Signal and Image Processing Applications.

[12]  Chen Rongbao,et al.  License plate location method based on modified HSI model of color image , 2009, 2009 9th International Conference on Electronic Measurement & Instruments.

[13]  Baoping Guo,et al.  Color image morphology based on distances in the HSI color space , 2009, 2009 ISECS International Colloquium on Computing, Communication, Control, and Management.

[14]  Christophe Chesnaud,et al.  Statistical Region Snake-Based Segmentation Adapted to Different Physical Noise Models , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  H. Irshad,et al.  Image segmentation using fuzzy clustering: A survey , 2010, 2010 6th International Conference on Emerging Technologies (ICET).

[16]  Takeshi Ikenaga,et al.  An FPGA-Based Real-Time Hardware Accelerator for Orientation Calculation Part in SIFT , 2009, 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing.

[17]  Ivar Weibull,et al.  Image Analysis — Principles and Applications in Materials Technology , 1995 .

[18]  Xiaobo Zhou,et al.  Nuclei Segmentation Using Marker-Controlled Watershed, Tracking Using Mean-Shift, and Kalman Filter in Time-Lapse Microscopy , 2006, IEEE Transactions on Circuits and Systems I: Regular Papers.

[19]  Imen Karoui,et al.  Variational Region-Based Segmentation Using Multiple Texture Statistics , 2010, IEEE Transactions on Image Processing.

[20]  Chanho Jung,et al.  Segmenting Clustered Nuclei Using H-minima Transform-Based Marker Extraction and Contour Parameterization , 2010, IEEE Transactions on Biomedical Engineering.

[21]  Bahram Javidi,et al.  Segmentation of 3D holographic images using bivariate jointly distributed region snake. , 2006, Optics express.