1,282 research outputs found
CNN based Learning using Reflection and Retinex Models for Intrinsic Image Decomposition
Most of the traditional work on intrinsic image decomposition rely on
deriving priors about scene characteristics. On the other hand, recent research
use deep learning models as in-and-out black box and do not consider the
well-established, traditional image formation process as the basis of their
intrinsic learning process. As a consequence, although current deep learning
approaches show superior performance when considering quantitative benchmark
results, traditional approaches are still dominant in achieving high
qualitative results. In this paper, the aim is to exploit the best of the two
worlds. A method is proposed that (1) is empowered by deep learning
capabilities, (2) considers a physics-based reflection model to steer the
learning process, and (3) exploits the traditional approach to obtain intrinsic
images by exploiting reflectance and shading gradient information. The proposed
model is fast to compute and allows for the integration of all intrinsic
components. To train the new model, an object centered large-scale datasets
with intrinsic ground-truth images are created. The evaluation results
demonstrate that the new model outperforms existing methods. Visual inspection
shows that the image formation loss function augments color reproduction and
the use of gradient information produces sharper edges. Datasets, models and
higher resolution images are available at https://ivi.fnwi.uva.nl/cv/retinet.Comment: CVPR 201
Identifiability of dynamic networks: the essential r\^ole of dources and dinks
The paper [1] presented the first results on generic identifiability of
dynamic networks with partial excitation and partial measurements, i.e.
networks where not all nodes are excited or not all nodes are measured. One key
contribution of that paper was to establish a set of necessary conditions on
the excitation and measurement pattern (EMP) that guarantee generic
identifiability. In a nutshell, these conditions established that all sources
must be excited and all sinks measured, and that all other nodes must be either
excited or measured. In the present paper, we show that two other types of
nodes, which are defined by the local topology of the network, play an
essential r\^ole in the search for a valid EMP, i.e. one that guarantees
generic identifiability. We have called these nodes dources and dinks. We show
that a network is generically identifiable only if, in addition to the above
mentioned conditions, all dources are excited and all dinks are measured. We
also show that sources and dources are the only nodes in a network that always
need to be excited, and that sinks and dinks are the only nodes that need to be
measured for an EMP to be valid.Comment: Submitted to IEEE Transactions on Automatic Contro
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