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Vacuum pressure impregnation with ultra-low viscosity epoxy was used for the preparation of concrete samples for image analysis.
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Triangular meshes have recently received considerable interest in adaptive sampling for image representation [1 17].
Cells were directly sampled for image acquisition.
Due to the cost and difficulty of truth labeling for SAR images, the newly labeled samples available for image classification are very limited.
The samples used for image analysis were not counterstained in order to avoid the background signal for hematoxylin.
In fact, the processing and understanding of SAR image is the problem of small samples, for instance, image filtering where with a few observations within a window a new value is computed.
We develop a corresponding sampling design methodology that deterministically yields a near optimal sampling distribution for image reconstructions incorporating knowledge of the image geometry.
In addition to using white cement instead of OPC, the incorporation of nylon fiber was omitted in the preparation of the samples for the image analysis since crack control was unnecessary.
A novel irregular and nonuniform sampling scheme for image data is presented in this paper.
A hybrid pixel/object-based change detection approach was developed to effectively collect training samples for historical images with sparse reference data.
It must be borne in mind that vision presents a remarkably high accuracy from very few training samples for unaltered images.
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