A method is proposed for the automated quantitative analysis of vessel characteristics in birch and poplar species. The method combines image-processing techniques with robust statistical approaches for automatically identifying vessels from digital microscopy images obtained by transmitted red light. The proposed method has been tested over a wide range of birch and poplar samples from different growth environments. Performance of the automatic vessel identification routine was assessed using results obtained by manual counting. The automated method produced fast and reliable vessel measurements and was robust to variations within and between samples. The approach has been merged into the wood property measurement system SilviScan as a core component of the hardwood analysis set for research and commercial use.
In this contribution we propose a feature-based method for motion estimation and correction in intraoperative thermal imaging during brain surgery. The motion is estimated from co-registered white-light images in order to perform a robust motion correction on the thermographic data. To ensure real-time performance of an intraoperative application, we optimise the processing time which essentially depends on the number of key points found by our algorithm. For this purpose we evaluate the effect of applying an non-maximum suppression (NMS) to improve the feature detection efficiency. Furthermore we propose an adaptive method to determine the size of the suppression area, resulting in a trade-off between accuracy and processing time.