Turbulence at the edge of the plasma in a nuclear fusion reactor can cause loss of confinement of the plasma. In
an effort to study the edge turbulence, the National Spherical Torus Experiment uses a gas puff imaging (GPI)
diagnostic to capture images of the turbulence. A gas puff is injected into the torus and visible light emission
from the gas cloud is captured by an ultra high-speed camera. Our goal is to detect and track coherent structures
in the GPI images to improve our understanding of plasma edge turbulence. In this paper, we present results
from various segmentation methods for the identification of the coherent structures. We consider three categories
of methods - immersion-based, region-growing, and model-based - and empirically evaluate their performance on
four sample sequences. Our preliminary results indicate that while some methods can be sensitive to the settings
of parameters, others show promise in being able to detect the coherent structures.
The detection of moving objects in complex scenes is the basis of many applications in surveillance, event
detection, and tracking. Complex scenes are difficult to analyze due to camera noise and lighting conditions.
Currently, moving objects are detected primarily using background subtraction algorithms, with block matching
techniques as an alternative. In this paper, we complement our earlier work on the comparison of background
subtraction methods by performing a similar study of block matching techniques. Block matching techniques
first divide a frame of a video into blocks and then determine where each block has moved from in the preceding
frame. These techniques are composed of three main components: block determination, which specifies the
blocks; search methods, which specify where to look for a match; and, the matching criteria, which determine
when a good match has been found. In our study, we compare various options for each component using publicly
available video sequences of a traffic intersection taken under different traffic and weather conditions. Our results
indicate that a simple block determination approach is significantly faster with minimum performance reduction,
the three step search method detects more moving objects, and the mean-squared-difference matching criteria
provides the best performance overall.