The purpose of the TREES project is to develop a computer-assisted environment for tree-ring analysis through the use of digital image processing and computer vision techniques.
Here is an example of tree ring detection.
Researchers invest many hours manually examining tree ring samples under a microscope. By tediously measuring tree ring features such as ring boundaries and widths and correlating data from different samples, they are able to perform dating so precise that Carbon-14 dating is calibrated from their results. Despite advancements in computational speed and image processing algorithms, manual measurement techniques remain the primary approach for collecting tree ring data since no previously developed automated technique is reliable enough for widespread use.
The methods being developed in the TREES project provide a means for
increasing the efficiency of tree ring analysis. Rather than attempting
to produce a fully automated solution, the philosophy of this project is
to create a computer-assisted environment that integrates analyst intervention
with algorithmic decisions. This approach allows the development
of a robust system that can emulate the complex human vision analysis of
tree samples, which often tend to have unpredictable features.
For the first step of the tree ring analysis process, a series of overlapping images are acquired across the tree sample, from the center of the tree (the pith) to the outer edge (the bark). These images must be corrected for any illumination distortion and normalized for any variation in camera gain and brightness offset during capture. The individual frames are then registered to one another through correlation techniques into a single mosaic. It is then possible to apply edge detection techniques to find the locations of tree ring boundaries in the sample mosaic and tree ring measurements can be made. Ring widths and other characteristics may be used to match ring patterns from one tree to another, allowing cross-dating to be performed.
Several major philosophies guide the development of this system. First, the goal is to create a computer-assisted, not automated, tree ring dating system. When dealing with natural phenomena such as tree rings, it is virtually impossible to predict all special cases. Because every tree ring must be accounted for to accurately cross-date a tree sample, it is better to allow analyst intervention when difficulties arise rather than attempting to develop a complex algorithm to make decisions with a low probability of error under all conditions. Also, to improve accuracy and reliability, the system is wood-centered. Rather than relying solely on digital images, the analyst can visually explore the wood surface through the microscope at any point in the tree ring analysis process to resolve problems of ring identification and dating. The system is primarily meant to increase the efficiency of tree ring analysis by detecting and measuring the majority of the tree rings in a sample with minimum operator intervention. However, the operator may use expertise in dendrochronology for verification, to provide suggestions to the system, or to detect subtle or ``false'' rings.
At the time of writing, the computer vision problems described in this
paper are only partially solved. The algorithms and techniques included
here represent the best methods found to date to solve the challenges of
tree ring analysis.