Mehdi Daneshpanah

3D imaging with axially distributed sensing

A new (to our knowledge) multiperspective 3D imaging architecture is proposed that uses imagers distributed along a common optical axis. In this axially distributed sensing method, either a single imager is translated along its optical axis or objects are moved parallel to the optical axis of a single imager. The 3D information collection capability of the proposed architecture is analyzed and a computational 3D reconstruction algorithm based on ray back-projection is proposed. It is shown analytically and experimentally that the collection capacity of this architecture is not uniform over the field of view. Experimental results are presented to verify the proposed approach. We believe this is the first report on 3D sensing and imaging with axially distributed sensing.

Real-time automated 3D sensing, detection, and recognition of dynamic biological micro-organic events

In this paper, we present an overview of three-dimensional (3D) optical imaging techniques for real-time automated sensing, visualization, and recognition of dynamic biological microorganisms. Real time sensing and 3D reconstruction of the dynamic biological microscopic objects can be performed by single-exposure on-line (SEOL) digital holographic microscopy. A coherent 3D microscope-based interferometer is constructed to record digital holograms of dynamic micro biological events. Complex amplitude 3D images of the biological microorganisms are computationally reconstructed at different depths by digital signal processing. Bayesian segmentation algorithms are applied to identify regions of interest for further processing. A number of pattern recognition approaches are addressed to identify and recognize the microorganisms. One uses 3D morphology of the microorganisms by analyzing 3D geometrical shapes which is composed of magnitude and phase. Segmentation, feature extraction, graph matching, feature selection, and training and decision rules are used to recognize the biological microorganisms. In a different approach, 3D technique is used that are tolerant to the varying shapes of the non-rigid biological microorganisms. After segmentation, a number of sampling patches are arbitrarily extracted from the complex amplitudes of the reconstructed 3D biological microorganism. These patches are processed using a number of cost functions and statistical inference theory for the equality of means and equality of variances between the sampling segments. Also, we discuss the possibility of employing computational integral imaging for 3D sensing, visualization, and recognition of biological microorganisms illuminated under incoherent light. Experimental results with several biological microorganisms are presented to illustrate detection, segmentation, and identification of micro biological events.

Profilometry and optical slicing by passive three-dimensional imaging.

Passive three-dimensional (3D) imaging is an enabling technology for a number of applications. We present a technique for profilometry and optical slicing of objects using 3D multiperspective imaging. We use the spectral radiation pattern (SRP) in object space and establish its relationship to different perspective images. A method is proposed to infer the depth of Lambertian surfaces from the statistics of the SRP. Experimental results are presented to show the feasibility of this method. To the best of our knowledge, this is the first time that statistics of the ray intensity-angle is used for 3D depth mapping.

Cell Identification Computational 3-D Holographic Microscopy

Recent developments in 3-D computational optical imaging have ushered in a new era for biological research. Techniques in 3-D holographic microscopy integrated with numerical processing are enabling researchers to obtain rich, quantitative information about the structure of cells and microorganisms in noninvasive, real-time conditions.

3D Holographic Imaging and Trapping for Non-Invasive Cell Identification and Tracking

Real-time high-throughput identification, screening, characterization, and processing of biological specimen is of great interest to a host of areas spanning from cell biology and medicine to security and defense. Much like human biometrics, microorganisms exhibit natural signatures that can be used for identification. In this paper, we first overview two optical techniques, namely digital holographic microscopy and holographic optical tweezers which can non-invasively image, manipulate, and identify microorganisms in three dimensions. The two methods bear similarities in their optics and implementation. Thus, we have proposed a new approach to identification of micro/nano organisms and cells by combining the two methods of digital holographic microscopy and holographic optical tweezers which can be integrated into a single compact hardware. The proposed system can simultaneously sense, control, identify, and track cells and microorganisms in three dimensions. New possibilities that arise from the proposed method are discussed.

Optofluidic system for three-dimensional sensing and identification of micro-organisms with digital holographic microscopy

Optofluidic devices offer flexibility for a variety of tasks involving biological specimen. We propose a system for three-dimensional (3D) sensing and identification of biological micro-organisms. This system consists of a microfluidic device along with a digital holographic microscope and relevant statistical recognition algorithms. The microfluidic channel is used to house the micro-organisms, while the holographic microscope and a CCD camera record their digital holograms. The holograms can be computationally reconstructed in 3D using a variety of algorithms, such as the Fresnel transform. Statistical recognition algorithms are used to analyze and identify the micro-organisms from the reconstructed wavefront. Experimental results are presented. Because of computational reconstruction of wavefronts in holographic imaging, this technique offers unique advantages that allow one to image micro-organisms within a deep channel while removing the inherent microfluidic-induced aberration through interferometery.

Tracking biological microorganisms in sequence of 3D holographic microscopy images.

We develop a 3D region tracking method based on Maximum A Posteriori (MAP) tracker and adapt it to digital hologram sequences to efficiently track biological microorganisms in holographic microscopy data. In our approach, the target surface is modeled as the iso-surface of a level set function which is evolved at each frame via level set Hamilton Jacobian update rule in Euler-Lagrangian framework. The statistical characteristics of the target microorganism versus the background are exploited to evolve the interface at each frame, thus the algorithm works independent of the shape or morphology of the target. We use the bivariate Gaussian distribution to model the reconstructed hologram data which enables us to take into account the correlation between the amplitude and phase of the reconstructed wavefront to obtain a more accurate tracking solution.

Three dimensional object recognition with photon counting imagery in the presence of noise

Three dimensional (3D) imaging systems have been recently suggested for passive sensing and recognition of objects in photon-starved environments where only a few photons are emitted or reflected from the object. In this paradigm, it is important to make optimal use of limited information carried by photons. We present a statistical framework for 3D passive object recognition in presence of noise. Since in quantum-limited regime, detector dark noise is present, our approach takes into account the effect of noise on information bearing photons. The model is tested when background noise and dark noise sources are present for identifying a target in a 3D scene. It is shown that reliable object recognition is possible in photon-counting domain. The results suggest that with proper translation of physical characteristics of the imaging system into the information processing algorithms, photon-counting imagery can be used for object classification.

Three dimensional imaging with randomly distributed sensors

As a promising three dimensional passive imaging modality, Integral Imaging (II) has been investigated widely within the research community. In virtually all of such investigations, there is an implicit assumption that the collection of elemental images lie on a simple geometric surface (e.g. flat, concave, etc), also known as pickup surface. In this paper, we present a generalized framework for 3D II with arbitrary pickup surface geometry and randomly distributed sensor configuration. In particular, we will study the case of Synthetic Aperture Integral Imaging (SAII) with random location of cameras in space, while all cameras have parallel optical axes but different distances from the 3D scene. We assume that the sensors are randomly distributed in 3D volume of pick up space. For 3D reconstruction, a finite number of sensors with known coordinates are randomly selected from within this volume. The mathematical framework for 3D scene reconstruction is developed based on an affine transform representation of imaging under geometrical optics regime. We demonstrate the feasibility of the methods proposed here by experimental results. To the best of our knowledge, this is the first report on 3D imaging using randomly distributed sensors.

Digital slicing of 3D scenes by Fourier filtering of integral images

We present a novel technique to extract depth information from 3D scenes recorded using an Integral Imaging system. The technique exploits the periodic structure of the recorded integral image to implement a Fourier-domain filtering algorithm. A proper projection of the filtered integral image permits reconstruction of different planes that constitute the 3D scene. The main feature of our method is that the Fourier-domain filtering allows the reduction of out-of-focus information, providing the InI system with real optical sectioning capacity.