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In this paper we deal with the problem of measuring the similarity between training and tests datasets in the context of transfer learning (TL) for image classification. TL tries to transfer knowledge...
Virtually all segmentation methods require parameter tuning, quite a difficult task, mostly performed manually through a troublesome trial-and-error process. To overcome this difficulty, an earlier ...
A methodology for the automatic adaptation of object models consisting of parallel line-type objects parts to a lower image resolution was developed previously. This paper aims at the verification of ...
A methodology for the automatic adaptation of object models consisting of parallel line-type objects parts to a lower image resolution was developed previously. This paper aims at the verification of ...
The key step in object-oriented image classification is the segmentation of the image into discrete meaningful objects. Generally the relation between the segmentation parameters and the corresponding...
A methodology for the automatic adaptation of models for the extraction of landscape objects with a variable number of similar object parts to a coarser image resolution is proposed in this paper. A g...
The spatial resolution of available image data plays an important role at the creation of object models for road extraction. The type and perceptibility of roads changes with increasing ground pixel s...
Depending on the spatial resolution, the appearance of roads differs in images. In high resolution aerial images aroad might be distinguishable as an area with visible road markings, while in a satell...
The relation between the segmentation parameters values and the segmentation result is far from being obvious. Therefore, in order to produce the desired outcome a complex and time consuming trial and...

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