An optical extensometer was tested using artificially deformed images with a known strain field. A real image series from a tensile test was used to obtain realistic deformation parameters, including spatial and temporal strain characteristics, changes in tonal pixel properties due to deformation, and the effect of nonuniform illumination. These parameters are used to artificially deform a real image taken from an object with a random speckle pattern. The signal-to-noise ratio of the resulting artificially deformed images is varied by applying a blurring pillbox filter and additive Gaussian noise to them. The optical extensometer uses digital image correlation to track homologous points of the object, and further to measure strains. The strain measurement algorithm includes a heuristic to dynamically control the template size in image correlation. Furthermore, several other methods to improve the accuracy-complexity ratio of the algorithm exist. The effects of different parameters and heuristics on the accuracy of the algorithm as well as its robustness against blur and noise are studied. Results show that the proposed test method is practical, and the heuristics improve the accuracy and robustness of the algorithm.
Image matching is a common procedure in computer vision. Usually the size of the image template is fixed. If the matching is done repeatedly, as e.g. in stereo vision, object tracking, and strain measurements, it is beneficial, in terms of computational cost, to use as small templates as possible. On the other hand larger templates usually give more reliable matches, unless e.g. projective distortions become too great. If the template size is controlled locally dynamically, both computational efficiency and reliability can be achieved simultaneously. Adaptive template size requires though that a larger template can be sampled anytime.
This paper introduces a method to adaptively control the template size in a digital image correlation based strain measurement algorithm. The control inputs are measures of confidence of match. Some new measures are proposed in this paper, and the ones found in the literature are reviewed. The measures of confidence are tested and compared with each other as well as with a reference method using templates of fixed size. The comparison is done with respect to computational complexity and accuracy of the algorithm. Due to complex inter-actions of the free parameters of the algorithm, random search is used to find an optimal parameter combination to attain a more reliable comparison. The results show that with some confidence measures the dynamic scheme outperforms the static reference method. However, in order to benefit from the dynamic scheme, optimization of the parameters is needed.
This paper studies the applicability of genetic algorithms and imaging to measure deformations. Genetic algorithms are used to search for the strain field parameters of images from a uniaxial tensile test. The non-deformed image is artificially deformed according to the estimated strain field parameters, and the resulting image is compared with the true deformed image. The mean difference of intensities is used as a fitness function. Results are compared with a node-based strain measurement algorithm developed by Koljonen et al. The reference method slightly outperforms the genetic algorithm as for mean difference of intensities. The root-mean-square difference of the displacement fields is less than one pixel. However, with some improvements suggested in this paper the genetic algorithm based method may be worth considering, also in other similar applications: Surface matching instead of individual landmarks can be used in camera calibration and image registration. Search of deformation parameters by genetic algorithms could be applied in pattern recognition tasks e.g. in robotics, object tracking and remote sensing if the objects are subject to deformation. In addition, other transformation parameters could be simultaneously looked for.
Virtual Keeper is a goalkeeper simulator. It estimates the trajectory of a ball thrown by a player using machine vision and animates a goalkeeper with a video projector. The first version of Virtual Keeper used only one camera. In this paper, a new version that uses two gray-scale cameras for trajectory estimation is proposed. In addition, a color camera and a microphone are used to determine the intersection-point of the ball trajectory and the goal line, in order to enable feedback and online calibration of the machine vision with neural networks, which in turn allows varying external parameters of the cameras and the video projector.
The color camera takes images of the goal and determines the positions of the goalposts with pattern matching. After the gray-scale cameras have observed the ball and estimated its trajectory, the sound processing block is triggered. When the ball hits the screen, the noise pattern is recognized with a neural network, whose input consists of temporal and spectral features. The sound processing block in turn triggers the color camera image processing block. The color of the ball differs from the colors of the background and goalkeeper to make the segmentation problem easier. The ball is recognized with fuzzy color based segmentation and fuzzy pattern matching.