Sub-pixel accurate marker segmentation is an important task for many computer vision systems. The 3D-positions
of markers are used in control loops to determine the position of machine tools or robot end-effectors.
Accurate segmentation of the marker position in the image plane is crucial for accurate reconstruction. Many subpixel
segmentation algorithms are computationally intensive, especially when the number of markers increases.
Modern graphics hardware with its massively parallel architecture provides a powerful tool for many image
segmentation tasks. Especially, the time consuming sub-pixel refinement steps in marker segmentation can
benefit from the recent progress. This article presents an implementation of a sub-pixel marker segmentation
framework using the GPU to accelerate the processing time. The image segmentation chain consists of two
stages. The first is a pre-processing stage which segments the initial position of the marker with pixel accuracy,
the second stage refines the initial marker position to sub-pixel accuracy. Both stages are implemented as shader
programs on the GPU. The flexible architecture allows it to combine different pre-processing and sub-pixel
refinement algorithms. Experimental results show that significant speed-up can be achieved compared to CPU
implementations, especially when the number of markers increases.
This article presents an analysis of thermal influences on the image acquisition process of an electronic camera.
It is shown that temperature changes lead to thermal expansion of the mechanical camera components and thus
to a displacement of the camera sensor and/or to a displacement of the center of projection. This change in the
imaging geometry leads to changing camera parameters which have to be adjusted if the camera is used for high
accurate measurements. The slight change in the imaging geometry can be determined by an analysis of the
optical flow of a static calibration pattern. The dependency between temperature change and the change of the
camera parameters can be modeled using methods from linear time invariant system theory. Once the model is
determined for a specific camera it can be used for adjusting the camera parameters according to the current
temperature and thus increasing the accuracy of an optical measurement device.