No abstract

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.

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.

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.

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.