Deep robust image deblurring via blur distilling and information comparison in latent space

Abstract. Machiavellianism, narcissism, and psychopathy are personality traits understood to be dispositions toward amoral and antisocial behavior. Recent research has suggested that sadism should also be added to this set of traits. In the present study, we tested a hypothesis proposing that these four traits are expressions of one superordinate construct: The Dark Tetrad. Exploration of the latent space of four “dark” traits suggested that the singular second-order factor which represents the Dark Tetrad can be extracted. Analysis has shown that Dark Tetrad traits can be located in the space of basic personality traits, especially on the negative pole of the Honesty-Humility, Agreeableness, Conscientiousness, and Emotionality dimensions. We conclude that sadism behaves in a similar manner as the other dark traits, but it cannot be reduced to them. The results support the concept of “Dark Tetrad.”

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Positional Estimation Within a Latent Space Model for Networks

Methodology ◽

10.1027/1614-2241.2.1.24 ◽

2006 ◽

Vol 2 (1) ◽

pp. 24-33 ◽

Cited By ~ 17

Author(s):

Susan Shortreed ◽

Mark S. Handcock ◽

Peter Hoff

Keyword(s):

Random Effects ◽

Network Data ◽

Random Effects Models ◽

Space Model ◽

Network Analyses ◽

Recent Advances ◽

Latent Space ◽

Latent Space Model ◽

Modeling Data

Recent advances in latent space and related random effects models hold much promise for representing network data. The inherent dependency between ties in a network makes modeling data of this type difficult. In this article we consider a recently developed latent space model that is particularly appropriate for the visualization of networks. We suggest a new estimator of the latent positions and perform two network analyses, comparing four alternative estimators. We demonstrate a method of checking the validity of the positional estimates. These estimators are implemented via a package in the freeware statistical language R. The package allows researchers to efficiently fit the latent space model to data and to visualize the results.

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Practical Algorithm Design for the FFT-based Robust Image Registration Method

IEEJ Transactions on Industry Applications ◽

10.1541/ieejias.139.22 ◽

2019 ◽

Vol 139 (1) ◽

pp. 22-29

Author(s):

Yoshi Ri ◽

Hiroshi Fujimoto

Keyword(s):

Image Registration ◽

Algorithm Design ◽

Registration Method ◽

Practical Algorithm ◽

Robust Image

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Multiresolution Bio-Medical Image Segmentation using Fuzzy C-Means Clustering

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d9918.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 9548-9551

Keyword(s):

Image Segmentation ◽

Medical Image Segmentation ◽

Experimental Result ◽

Segmentation Method ◽

Fuzzy C Means ◽

Fuzzy C Means Clustering ◽

Robust Image ◽

Multiple Clusters ◽

Single Pixel ◽

Popular Image

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.

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