Vladimir Curic

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Spatially adaptive structuring elements adjust their shape to the local structures in the image, and are often defined by a ball in a geodesic distance or gray-weighted distance metric space. This paper introduces salience adaptive structuring elements as spatially variant structuring elements that modify not only their shape, but also their size according(More)
We present an up-to-date survey on the topic of adaptive mathematical morphology. A broad review of research performed within the field is provided, as well as an in-depth summary of the theoretical advances within the field. Adaptivity can come in many different ways, based on different attributes, measures, and parameters. Similarities and differences(More)
Small synthetic fluorophores are in many ways superior to fluorescent proteins as labels for imaging. A major challenge is to use them for a protein-specific labeling in living cells. Here, we report on our use of noncanonical amino acids that are genetically encoded via the pyrrolysyl-tRNA/pyrrolysyl-RNA synthetase pair at artificially introduced TAG(More)
We propose a new distance measure, called Complement weighted sum of minimal distances, between finite sets in $${\mathbb Z }^n$$ and evaluate its usefulness for shape registration and matching. In this set distance the contribution of each point of each set is weighted according to its distance to the complement of the set. In this way, outliers and noise(More)
Adaptive mathematical morphology has recently become a popular topic in the mathematical morphology community. In particular, the construction of adaptive structuring elements that adjust their size and shape to the local image structures have increased a lot of attention in recent years. Apart from that, there is a growing interest for representation of(More)
UNLABELLED SMeagol is a software tool to simulate highly realistic microscopy data based on spatial systems biology models, in order to facilitate development, validation and optimization of advanced analysis methods for live cell single molecule microscopy data. AVAILABILITY AND IMPLEMENTATION SMeagol runs on Matlab R2014 and later, and uses compiled(More)
This paper introduces a new descriptor for characterizing and classifying the pixels of texture images by means of General Adaptive Neighborhoods (GANs). The GAN of a pixel is a spatial region surrounding it and fitting its local image structure. The features describing each pixel are then regionbased and intensity-based measurements of its corresponding(More)