35,600 research outputs found
DARC: Distribution-Aware Re-Coloring Model for Generalizable Nucleus Segmentation
Nucleus segmentation is usually the first step in pathological image analysis
tasks. Generalizable nucleus segmentation refers to the problem of training a
segmentation model that is robust to domain gaps between the source and target
domains. The domain gaps are usually believed to be caused by the varied image
acquisition conditions, e.g., different scanners, tissues, or staining
protocols. In this paper, we argue that domain gaps can also be caused by
different foreground (nucleus)-background ratios, as this ratio significantly
affects feature statistics that are critical to normalization layers. We
propose a Distribution-Aware Re-Coloring (DARC) model that handles the above
challenges from two perspectives. First, we introduce a re-coloring method that
relieves dramatic image color variations between different domains. Second, we
propose a new instance normalization method that is robust to the variation in
foreground-background ratios. We evaluate the proposed methods on two HE
stained image datasets, named CoNSeP and CPM17, and two IHC stained image
datasets, called DeepLIIF and BC-DeepLIIF. Extensive experimental results
justify the effectiveness of our proposed DARC model. Codes are available at
\url{https://github.com/csccsccsccsc/DARCComment: Accepted by MICCAI 202
The Dynamics of Twisted Tent Maps
This paper is a study of the dynamics of a new family of maps from the
complex plane to itself, which we call twisted tent maps. A twisted tent map is
a complex generalization of a real tent map. The action of this map can be
visualized as the complex scaling of the plane followed by folding the plane
once. Most of the time, scaling by a complex number will "twist" the plane,
hence the name. The "folding" both breaks analyticity (and even smoothness) and
leads to interesting dynamics ranging from easily understood and highly
geometric behavior to chaotic behavior and fractals.Comment: 87 pages. This is my Ph.D. thesis from IUPU
Return of Frustratingly Easy Domain Adaptation
Unlike human learning, machine learning often fails to handle changes between
training (source) and test (target) input distributions. Such domain shifts,
common in practical scenarios, severely damage the performance of conventional
machine learning methods. Supervised domain adaptation methods have been
proposed for the case when the target data have labels, including some that
perform very well despite being "frustratingly easy" to implement. However, in
practice, the target domain is often unlabeled, requiring unsupervised
adaptation. We propose a simple, effective, and efficient method for
unsupervised domain adaptation called CORrelation ALignment (CORAL). CORAL
minimizes domain shift by aligning the second-order statistics of source and
target distributions, without requiring any target labels. Even though it is
extraordinarily simple--it can be implemented in four lines of Matlab
code--CORAL performs remarkably well in extensive evaluations on standard
benchmark datasets.Comment: Fixed typos. Full paper to appear in AAAI-16. Extended Abstract of
the full paper to appear in TASK-CV 2015 worksho
3D Reconstruction: Novel Method for Finding of Corresponding Points using Pseudo Colors
This paper deals with the reconstruction of spatial coordinates of an arbitrary point in a scene using two images scanned by a 3D camera or two displaced cameras. Calculations are based on the perspective geom-etry. Accurate determination of corresponding points is a fundamental step in this process. The usually used methods can have a problem with points, which lie in areas without sufficient contrast. This paper describes our proposed method based on the use of the relationship between the selected points and area feature points. The proposed method finds correspondence using a set of feature points found by SURF. An algorithm is proposed and described for quick removal of false correspondences, which could ruin the correct reconstruction. The new method, which makes use of pseudo color image representation (pseudo coloring) has been proposed subsequently. By means of this method it is possible to significantly increase the color contrast of the surveyed image, and therefore add more information to find the correct correspondence. Reliability of the found correspondence can be verified by reconstruction of 3D position of selected points. Executed experiments confirm our assumption
A Review of Interference Reduction in Wireless Networks Using Graph Coloring Methods
The interference imposes a significant negative impact on the performance of
wireless networks. With the continuous deployment of larger and more
sophisticated wireless networks, reducing interference in such networks is
quickly being focused upon as a problem in today's world. In this paper we
analyze the interference reduction problem from a graph theoretical viewpoint.
A graph coloring methods are exploited to model the interference reduction
problem. However, additional constraints to graph coloring scenarios that
account for various networking conditions result in additional complexity to
standard graph coloring. This paper reviews a variety of algorithmic solutions
for specific network topologies.Comment: 10 pages, 5 figure
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