Separating a Real-Life Nonlinear Image Mixture

When acquiring an image of a paper document, the image printed on the back page sometimes shows through. The mixture of the front- and back-page images thus obtained is markedly nonlinear, and thus constitutes a good real-life test case for nonlinear blind source separation. This paper addresses a difficult version of this problem, corresponding to the use of "onion skin" paper, which results in a relatively strong nonlinearity of the mixture, which becomes close to singular in the lighter regions of the images. The separation is achieved through the MISEP technique, which is an extension of the well known INFOMAX method. The separation results are assessed with objective quality measures. They show an improvement over the results obtained with linear separation, but have room for further improvement

WAVELET BASED NONLINEAR SEPARATION OF IMAGES

This work addresses a real-life problem corresponding to the separation of the nonlinear mixture of images which arises when we scan a paper document and the image from the back page shows through. The proposed solution consists of a non-iterative procedure that is based on two simple observations: (1) the high frequency content of images is sparse, and (2) the image printed on each side of the paper appears more strongly in the mixture acquired from that side than in the mixture acquired from the opposite side. These ideas had already been used in the context of nonlinear denoising source separation (DSS). However, in that method the degree of separation achieved by applying these ideas was relatively weak, and the separation had to be improved by iterating within the DSS scheme. In this paper the application of these ideas is improved by changing the competition function and the wavelet transform that is used. These improvements allow us to achieve a good separation in one shot, without the need to integrate the process into an iterative DSS scheme. The resulting separation process is both nonlinear and non-local. We present experimental results that show that the method achieves a good separation quality

Laplacian Mixture Modeling for Network Analysis and Unsupervised Learning on Graphs

Laplacian mixture models identify overlapping regions of influence in unlabeled graph and network data in a scalable and computationally efficient way, yielding useful low-dimensional representations. By combining Laplacian eigenspace and finite mixture modeling methods, they provide probabilistic or fuzzy dimensionality reductions or domain decompositions for a variety of input data types, including mixture distributions, feature vectors, and graphs or networks. Provable optimal recovery using the algorithm is analytically shown for a nontrivial class of cluster graphs. Heuristic approximations for scalable high-performance implementations are described and empirically tested. Connections to PageRank and community detection in network analysis demonstrate the wide applicability of this approach. The origins of fuzzy spectral methods, beginning with generalized heat or diffusion equations in physics, are reviewed and summarized. Comparisons to other dimensionality reduction and clustering methods for challenging unsupervised machine learning problems are also discussed.Comment: 13 figures, 35 reference

Slipping friction of an optically and magnetically manipulated microsphere rolling at a glass-water interface

The motion of submerged magnetic microspheres rolling at a glass-water interface has been studied using magnetic rotation and optical tweezers combined with bright-field microscopy particle tracking techniques. Individual microspheres of varying surface roughness were magnetically rotated both in and out of an optical trap to induce rolling, along either plain glass cover slides or glass cover slides functionalized with polyethylene glycol. It has been observed that the manipulated microspheres exhibited nonlinear dynamic rolling-while-slipping motion characterized by two motional regimes: At low rotational frequencies, the speed of microspheres free-rolling along the surface increased proportionately with magnetic rotation rate; however, a further increase in the rotation frequency beyond a certain threshold revealed a sharp transition to a motion in which the microspheres slipped with respect to the external magnetic field resulting in decreased rolling speeds. The effects of surface-microsphere interactions on the position of this threshold frequency are posed and investigated. Similar experiments with microspheres rolling while slipping in an optical trap showed congruent results.Comment: submitted to Journal of Applied Physics, 11 figure

Moxifloxacin: Clinically compatible contrast agent for multiphoton imaging

Multiphoton microscopy (MPM) is a nonlinear fluorescence microscopic technique widely used for cellular imaging of thick tissues and live animals in biological studies. However, MPM application to human tissues is limited by weak endogenous fluorescence in tissue and cytotoxicity of exogenous probes. Herein, we describe the applications of moxifloxacin, an FDA-approved antibiotic, as a cell-labeling agent for MPM. Moxifloxacin has bright intrinsic multiphoton fluorescence, good tissue penetration and high intracellular concentration. MPM with moxifloxacin was demonstrated in various cell lines, and animal tissues of cornea, skin, small intestine and bladder. Clinical application is promising since imaging based on moxifloxacin labeling could be 10 times faster than imaging based on endogenous fluorescence.1152sciescopu

Evolution of oil droplets in a chemorobotic platform

Evolution, once the preserve of biology, has been widely emulated in software, while physically embodied systems that can evolve have been limited to electronic and robotic devices and have never been artificially implemented in populations of physically interacting chemical entities. Herein we present a liquid-handling robot built with the aim of investigating the properties of oil droplets as a function of composition via an automated evolutionary process. The robot makes the droplets by mixing four different compounds in different ratios and placing them in a Petri dish after which they are recorded using a camera and the behaviour of the droplets analysed using image recognition software to give a fitness value. In separate experiments, the fitness function discriminates based on movement, division and vibration over 21 cycles, giving successive fitness increases. Analysis and theoretical modelling of the data yields fitness landscapes analogous to the genotype–phenotype correlations found in biological evolution. , Trevor Hinkley, James Ward Taylor Kliment Yane