Raimund Leitner

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a r t i c l e i n f o Detecting edges in multispectral images is difficult because different spectral bands may contain different edges. Existing approaches calculate the edge strength of a pixel locally, based on the variation in intensity between this pixel and its neighbors. Thus, they often fail to detect the edges of objects embedded in background(More)
Many industrial object-sorting applications leverage benefits of hyperspectral imaging technology. Design of object sorting algorithms is a challenging pattern recognition problem due to its multi-level nature. Objects represented by sets of pixels/spectra in hyperspectral images are to be allocated into pre-specified sorting categories. Sorting categories(More)
The sensitivity of two commercial metal oxide (MOx) sensors to ethylene is tested at different relative humidities. One sensor (MiCS-5914) is based on tungsten oxide, the other (MQ-3) on tin oxide. Both sensors were found to be sensitive to ethylene concentrations down to 10 ppm. Both sensors have significant response times; however, the tungsten sensor is(More)
Multispectral endoscopy images provide potential for early stage cancer detection. This paper considers this relatively novel imaging technique and presents a supervised method for cancer detection using such mul-tispectral data. The data under consideration include different types of cancer. This poses a challenge for the detection as different cancer(More)
Edge detection in hyperspectral images is an intrinsically difficult problem as the gray value intensity images related to single spectral bands may show different edges. The few existing approaches are either based on a straight forward combining of these individual edge images, or on finding the outliers in a region segmentation. As an alternative, we(More)