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The application of Raman and infrared (IR) microspectroscopy is leading to hyperspectral data containing complementary information concerning the molecular composition of a sample. The classification of hyperspectral data from the individual spectroscopic approaches is already state-of-the-art in several fields of research. However, more complex structured samples and difficult measuring conditions might affect the accuracy of classification results negatively and could make a successful classification of the sample components challenging. This contribution presents a comprehensive comparison in supervised pixel classification of hyperspectral microscopic images, proving that a combined approach of Raman and IR microspectroscopy has a high potential to improve classification rates by a meaningful extension of the feature space. It shows that the complementary information in spatially co-registered hyperspectral images of polymer samples can be accessed using different feature extraction methods and, once fused on the feature-level, is in general more accurately classifiable in a pattern recognition task than the corresponding classification results for data derived from the individual spectroscopic approaches.
In interactive graphics it is often necessary to introduce large changes in the image in response to updated information about the state of the system. Updating the local state immediately would lead to a sudden transient change in the image, which could be perceptually disruptive. However, introducing the correction gradually using smoothing operations increases latency and degrades precision. It would be beneficial to be able to introduce graphic updates immediately if they were not perceptible. In the paper the use of saccade-contingent updates is exploited to hide graphic updates during the period of visual suppression that accompanies a rapid, or saccadic, eye movement. Sensitivity to many visual stimuli is known to be reduced during a change in fixation compared to when the eye is still. For example, motion of a small object is harder to detect during a rapid eye movement (saccade) than during a fixation. To evaluate if these findings generalize to large scene changes in a virtual environment, gaze behavior in a 180 degree hemispherical display was recorded and analyzed. This data was used to develop a saccade detection algorithm adapted to virtual environments. The detectability of trans-saccadic scene changes was evaluated using images of high resolution real world scenes. The images were translated by 0.4, 0.8 or 1.2 degrees of visual angle during horizontal saccades. The scene updates were rarely noticeable for saccades with a duration greater than 58 ms. The detection rate for the smallest translation was just 6.25%. Qualitatively, even when trans-saccadic scene changes were detectible, they were much less disturbing than equivalent changes in the absence of a saccade.
In this contribution a machine vision inspection system is presented which is designed as a length measuring sensor. It is developed to be applied to a range of heat shrink tubes, varying in length, diameter and color. The challenges of this task were the precision and accuracy demands as well as the real-time applicability of the entire approach since it should be realized in regular industrial line production. In production, heat shrink tubes are cut to specific sizes from a continuous tube. A multi-measurement strategy has been developed, which measures each individual tube segment several times with sub pixel accuracy while being in the visual field. The developed approach allows for a contact-free and fully automatic control of 100% of produced heat shrink tubes according to the given requirements with a measuring precision of 0.1mm. Depending on the color, length and diameter of the tubes considered, a true positive rate of 99.99% to 100% has been reached at a true negative rate of > 99.7.