Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

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Computational localization microscopy with extended axial range

A new single-aperture 3D particle-localization and tracking technique is presented that demonstrates an increase in depth range by more than an order of magnitude without compromising optical resolution and throughput. We exploit the extended depth range and depth-dependent translation of an Airy-beam PSF for 3D localization over an extended volume in a single snapshot. The technique is applicable to all bright-field and fluorescence modalities for particle localization and tracking, ranging from super-resolution microscopy through to the tracking of fluorescent beads and endogenous particles within cells. We demonstrate and validate its application to real-time 3D velocity imaging of fluid flow in capillaries using fluorescent tracer beads. An axial localization precision of 50 nm was obtained over a depth range of 120μm using a 0.4NA, 20× microscope objective. We believe this to be the highest ratio of axial range-to-precision reported to date

Hybrid Particle and Kalman Filtering for Pupil Tracking in Active IR Illumination Gaze Tracking System

A novel pupil tracking method is proposed by combining particle filtering and Kalman filtering for the fast and accurate detection of pupil target in an active infrared source gaze tracking system. Firstly, we utilize particle filtering to track pupil in synthesis triple-channel color map (STCCM) for the fast detection and develop a comprehensive pupil motion model to conduct and analyze the randomness of pupil motion. Moreover, we built a pupil observational model based on the similarity measurement with generated histogram to improve the credibility of particle weights. Particle filtering can detect pupil region in adjacent frames rapidly. Secondly, we adopted Kalman filtering to estimate the pupil parameters more precisely. The state transitional equation of the Kalman filtering is determined by the particle filtering estimation, and the observation of the Kalman filtering is dependent on the detected pupil parameters in the corresponding region of difference images estimated by particle filtering. Tracking results of Kalman filtering are the final pupil target parameters. Experimental results demonstrated the effectiveness and feasibility of this method.Published versio

Differential Zernike filter for phasing of segmented mirror and image processing

The major objective of this thesis is to study the differential Zernike filter and its applications in phasing segmented mirror and image processing. In terms of phasing, we provide both theoretical analysis and simulation for a differential Zernike filter based phasing technique, and find that the differential Zernike filter perform consistently better than its counterpart, traditional Zernike filter. We also combine the differential Zernike filter with a feedback loop, to represent a gradient-flow optimization dynamic system. This system is shown to be capable of separating (static) misalignment errors of segmented mirrors from (dynamical) atmospheric turbulence, and therefore compress the effects of atmospheric turbulence. Except for segmented mirror phasing, we also apply the Zernike feedback system in image processing. For the same system dynamics as well as in segment phasing, the Zernike filter feedback system is capable of compress the static noisy background, and makes the single particle tracking algorithm even working in case of very low signal-to-noise ratio. Finally, we apply an efficient multiple-particle tracking algorithm on a living cell image sequence. This algorithm is shown to be able to deal with higher particle density, while the single particle tracking methods are not working under this condition

Three dimensional tracking of gold nanoparticles using digital holographic microscopy

In this paper we present a digital holographic microscope to track gold colloids in three dimensions. We report observations of 100nm gold particles in motion in water. The expected signal and the chosen method of reconstruction are described. We also discuss about how to implement the numerical calculation to reach real-time 3D tracking