The detection of varying 2D shapes is a recurrent task for Computer Vision applications, and camera based object recognition has become a standard procedure. Due to the discrete nature of digital images and aliasing effects, shape recognition can be complicated. There are many existing algorithms that discuss the identification of circles and ellipses, but they are very often limited in flexibility or speed or require high quality input data. Our work considers the application of shape recognition for processes in industrial environments and, especially the automatization requires reliable and fast algorithms at the same time. We take a very practical look at the automated shape recognition for common industrial tasks and present a very fast novel approach for the detection of deformed shapes which are in the broadest sense elliptic. Furthermore, we consider the automated recognition of bacteria colonies and coded markers for both 3D object tracking and an automated camera calibration procedure
The digitalization of real-world objects is of great importance in various application domains. E. g.in industrial processes quality assurance is very important. Geometric properties of workpieces have to be measured. Traditionally, this is done with gauges which is somewhat subjective and time-consuming. We developed a robust optical laser scanner for the digitalization of arbitrary objects, primary, industrial workpieces. As measuring principle we use triangulation with structured lighting and a multi-axis locomotor system. Measurements on the generated data leads to incorrect results if the contained error is too high. Therefore, processes for geometric inspection under non-laboratory conditions are needed that are robust in permanent use and provide high accuracy as well as high operation speed. The many existing methods for polygonal mesh optimization produce very esthetic 3D models but often require user interaction and are limited in processing speed and/or accuracy. Furthermore, operations on optimized meshes consider the entire model and pay only little attention to individual measurements. However, many measurements contribute to parts or single scans with possibly strong differences between neighboring scans being lost during mesh construction. Also, most algorithms consider unsorted point clouds although the scanned data is structured through device properties and measuring principles. We use this underlying structure to achieve high processing speeds and extract intrinsic system parameters to use them for fast pre-processing.
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