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In this article, we present a segmentation algorithm based on the proposed representation for effectively segmenting spectral images of natural scenes and bare circuit boards.
The objective of the paper is to present a segmentation method, based on maximum a posteriori probability (MAP) estimator, with application in seismic signal processing; some interpretations and connections with other approaches in change detection and segmentation, as well as computational aspects in this field are also discussed.
We present a segmentation of the landscape of cloud computing that we believe can best inform this process.
Finally, we present a segmentation of the cloud landscape that we believe can best support a sharing of learning between projects in individual clusters.
The relationships among variables (NIST characteristics) lets us present a segmentation of the cloud landscape that we believe can inform where different projects may find examples of best practice or technology choices suitable in those type of projects.
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Figure 3 presents a segmentation example.
Figure 2b presents a segmentation where the foreground blob represents a removed object and Figure 2c shows the blob boundary projected over the corresponding input frame.
This paper presents a segmentation-free optical character recognition system for printed Urdu Nastaliq font using ligatures as units of recognition.
In this paper we present a motion segmentation method attempting to address some of the major issues in the area.
We present a novel image segmentation approach which exploits multiple over-segmentations and achieves segmentation results by hierarchical region merging.
In this paper, we also present a new shape segmentation approach based on SFCN.
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