A New Information-Theoretic Measure to Control the Robustness-Sensitivity Trade-Off for DMFFD Point-Set Registration

Author(s):  
Nicholas J. Tustison ◽  
Suyash P. Awate ◽  
Gang Song ◽  
Tessa S. Cook ◽  
James C. Gee
2010 ◽  
Author(s):  
Nicholas J. Tustison ◽  
Suyash Awate ◽  
James Gee

Our previous contributions to the ITK community include a generalized B-spline approximation scheme as well as a generalized information-theoretic measure for assessing point-set correspondence known as the Jensen-Havrda-Charvat-Tsallis (JHCT) divergence. In this submission, we combine these two contributions for the registration of labeled point-sets. The transformation model which uses the former contribution is denoted as directly manipulated free-form deformation (DMFFD) and has been used for image registration. The information-theoretic approach described not only eliminates exact cardinality constraints which plague exact landmark matching algorithms, but it also incorporates the local point-set structure into the similarity measure calculation. Although theoretical discussion of these two components is deferred to other venues, the implementation details given in this submission should be adequate for those wishing to use our algorithm. Visualization of results is aided by another of our previous contributions. Additionally, we provide the rudimentary command line parsing classes used in our testing routines which were written in the ITK style and also available to use consistent with the open-source paradigm.


2008 ◽  
Author(s):  
Nicholas J. Tustison ◽  
Suyash Awate ◽  
James Gee

A novel point-set registration algorithm was proposed in [6] based on minimization of the Jensen-Shannon divergence. In this contribution, we generalize this Jensen-Shannon divergence point-set measure framework to the Jensen-Havrda-Charvat-Tsallis divergence. This generalization permits a fine-tuning of the actual divergence measure between robustness and specificity. The principle contribution of this submission is theitk::JensenHavrdaCharvatTsallisPointSetMetric class which is derived from the existing itk::PointSetToPointSetMetric. In addition, we provide other classes with utility that would extend beyond the point-set measure framework that we provide in this paper. This includes a point-set analogue of the itk::ImageFunction, i.e. itk::PointSetFunction. From this class we derive the class itk::ManifoldParzenWindowsPointSetFunction which provides a Parzen windowing scheme for learning the local structure of point-sets. Finally, we include the itk::DecomposeTensorFunction class which wraps the different vnl matrix decomposition schemes for easy use within ITK.


2021 ◽  
Author(s):  
Hyeonwoo Jeong ◽  
Byunghyun Yoon ◽  
Honggu Jeong ◽  
Kang-Sun Choi

Network ◽  
2021 ◽  
Vol 1 (2) ◽  
pp. 50-74
Author(s):  
Divyanshu Pandey ◽  
Adithya Venugopal ◽  
Harry Leib

Most modern communication systems, such as those intended for deployment in IoT applications or 5G and beyond networks, utilize multiple domains for transmission and reception at the physical layer. Depending on the application, these domains can include space, time, frequency, users, code sequences, and transmission media, to name a few. As such, the design criteria of future communication systems must be cognizant of the opportunities and the challenges that exist in exploiting the multi-domain nature of the signals and systems involved for information transmission. Focussing on the Physical Layer, this paper presents a novel mathematical framework using tensors, to represent, design, and analyze multi-domain systems. Various domains can be integrated into the transceiver design scheme using tensors. Tools from multi-linear algebra can be used to develop simultaneous signal processing techniques across all the domains. In particular, we present tensor partial response signaling (TPRS) which allows the introduction of controlled interference within elements of a domain and also across domains. We develop the TPRS system using the tensor contracted convolution to generate a multi-domain signal with desired spectral and cross-spectral properties across domains. In addition, by studying the information theoretic properties of the multi-domain tensor channel, we present the trade-off between different domains that can be harnessed using this framework. Numerical examples for capacity and mean square error are presented to highlight the domain trade-off revealed by the tensor formulation. Furthermore, an application of the tensor framework to MIMO Generalized Frequency Division Multiplexing (GFDM) is also presented.


2017 ◽  
Vol 34 (10) ◽  
pp. 1399-1414 ◽  
Author(s):  
Wanxia Deng ◽  
Huanxin Zou ◽  
Fang Guo ◽  
Lin Lei ◽  
Shilin Zhou ◽  
...  

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Sk Aziz Ali ◽  
Kerem Kahraman ◽  
Christian Theobalt ◽  
Didier Stricker ◽  
Vladislav Golyanik

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