Abstract
An image pattern tracking algorithm is described in this paper for time-resolved measurements of mini- and micro-scale movements of complex objects. This algorithm works with a high-speed digital imaging system, which records thousands of successive image frames in a short time period. The image pattern of the observed object is tracked among successively recorded image frames with a correlation-based algorithm, so that the time histories of the position and displacement of the investigated object in the camera focus plane are determined with high accuracy. The speed, acceleration and harmonic content of the investigated motion are obtained by post processing the position and displacement time histories. The described image pattern tracking algorithm is tested with synthetic image patterns and verified with tests on live insects.
Highlights
Imaging techniques including photography, cinematography and analog/digital video techniques are always favorite tools for research scientists to investigate motion of fluids and solid objects
The described image pattern tracking algorithm is developed on the basis of the correlation-based interrogation with continuous window-shift (CCWS), which is an algorithm for digital particle image velocimetry (PIV) recording evaluation
When a fixed reference pattern is used, i.e. it is chosen in the first frame and does not change anymore, there will be a large error for determining the image pattern displacement between frames, and the instantaneous insect body moving velocity cannot be measured at high accuracy
Summary
Cinematography and analog/digital video techniques are always favorite tools for research scientists to investigate motion of fluids and solid objects. According to publications on the particle image velocimetry (PIV), i.e. an image pattern tracking technique for fluid flow measurements, the uncertainties for determining the displacement of image pattern of 32 32 pixels may be less than 0.1 pixels [3,4,5,6,7] that ensures the high accuracy of determining the insect body part motion. The image pattern distortion were considered in PIV algorithms by using particle image displacements near to the tracked image pattern [9,10,11], but those ideas cannot be applied in the insect body part tracking because no neighborhood information is available to correct the distorted image pattern of the insect body part It seems that the only effective way to solve the problem of strong image pattern distortion is to use a high frame rate so that the image pattern distortion between two neighbored frames may be small enough. Two application examples from termite and fire ant, respectively, are used to verify the described algorithm
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