The central idea of Hebbian Learning and Negative Feedback Networks is that artificial neural networks using negative feedback of activation can use simple Hebbian learning to self-organise so that they uncover interesting structures in data sets. T
In this paper, we tackle the problem of unsupervised segmentation in the form of superpixels. Our main emphasis is on speed and accuracy. We build on [31] to define the problem as a boundary and topology preserving Markov random field. We propose a
Dr. Rosenfeld was widely regarded as the leading researcher in the world in the field of computer image analysis. Over a period of nearly 40 years he made many fundamental and pioneering contributions to nearly every area of that field. He wrote the
TopoJSON 是 GeoJSON 的扩展，增加了拓扑逻辑的编码。 Rather than representing geometries discretely, geometries in TopoJSON files are stitched together from shared line segments called arcs. TopoJSON eliminates redundancy, offering much more compact representations o
Topology-Preserving Deep Image Segmentation
Segmentation algorithms are prone to make topological errors on fine-scale structures,
e.g., broken connections. We propose a novel method that learns to segment
with correct topology. In particular, we
We present our experiences to date building ONOS (Open Network Operating System), an experimental distributed SDN control platform motivated by the performance, scalability, and availability requirements of large operator networks. We describe and ev
开关电源原理性书籍，主要介绍了开关电源的脉宽调制技术。Newnes is an imprint of Butterworth-Heinemann
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