Circular Hough Transform and Local Circularity Measure for Weight Estimation of a Graph-Cut based Wood Stack Measurement

One of the time consuming tasks in the timber industry
is the manually measurement of features of wood stacks.
Such features include, but are not limited to, the number
of the logs in a stack, their diameters distribution, and their
volumes. Computer vision techniques have recently been
used for solving this real-world industrial application. Such
techniques are facing many challenges as the task is usually
performed in outdoor, uncontrolled, environments. Furthermore,
the logs can vary in texture and they can be occluded
by different obstacles. These all make the segmentation of
the wood logs a difficult task. Graph-cut has shown to be
good enough for such a segmentation. However, it is hard
to find proper graph weights. This is exactly the contribution
of this paper to propose a method for setting the
weights of the graph. To do so, we use Circular Hough
Transform (CHT) for obtaining information about the foreand
background regions of a stack image, and then use this
together with a Local Circularity Measure (LCM) to modify
the weights of the graph to segment the wood logs from the
rest of the image. We further improve the segmentation by
separating overlapping logs. These segmented wood logs
are finally scaled and used to acquire the necessary wood
stack measurements in real-world scale (in cm). The proposed
system, which works automatically, has been tested
on two different datasets, containing real outdoor images
of logs which vary in shapes and sizes. The experimental
results show that the proposed approach not only achieves
the same results as the state-of-the-art systems, it produces
more stable results.