In this keynote address paper, we overview recently published works on the current techniques and methods for automated cell identification with 3D optical imaging using compact and field portable systems. 3D imaging systems including digital holographic microscopy systems as well as lensless pseudorandom phase encoding systems are capable of capturing 3D information of microscopic objects such as biological cells which allows for highly accurate automated cell identification. Systems based on digital holography enable reconstruction of the cell’s 3D optical path length profile. The reconstructed 3D profiles can be used to extract morphological and spatio-temporal cell features from biological samples for classification and cell identification. Similarly, pseudorandom encoding techniques such as single random phase encoding (SRPE) and double random phase encoding (DRPE) can be used to encode 3D cell information into opto-biological signatures which can be used for cell identification tasks. Recent advancements in these areas are presented including compact and field-portable 3D-printed shearing digital holographic microscopy systems, integration of digital holographic microscopy with head mounted augmented reality devices, and the use of spatio-temporal features extracted from cell membrane fluctuations for sickle cell disease diagnosis.
We overview a previously reported method for spatial-temporal human gesture recognition under degraded environmental conditions using three-dimensional (3D) integral imaging (InIm) technology with correlation filters. The degraded conditions include low illumination environment and occlusion in front of the human gesture. The human gesture is captured by passive integral imaging, the signal is then processed using computational reconstruction algorithms and denoising algorithms to decrease the noise and remove partial occlusion. Gesture recognition is finally processed using correlation filters. Experimental results show that the proposed approach is promising for human gesture recognition under degraded environmental conditions compared with conventional recognition algorithms.
We overview a previously reported system for automated diagnosis of sickle cell disease based on red blood cell (RBC) membrane fluctuations measured via digital holographic microscopy. A low-cost, compact, 3D-printed shearing interferometer is used to record video holograms of RBCs. Each hologram frame is reconstructed in order to form a spatio-temporal data cube from which features regarding membrane fluctuations are extracted. The motility-based features are combined with static morphology-based cell features and inputted into a random forest classifier which outputs the disease state of the cell with high accuracy.
KEYWORDS: 3D image reconstruction, 3D image processing, Signal to noise ratio, Cameras, Integral imaging, Photons, Visualization, Optical sensors, Facial recognition systems, Sensors
We overview a recently published work that utilizes three-dimensional (3D) integral imaging (InIm) to capture 3D information of a scene in low illumination conditions using passive imaging sensors. An object behind occlusion is imaged using 3D InIm. A computational 3D reconstructed image is generated from the captured scene information at a particular depth plane, which showed the object without occlusion. Moreover, 3D InIm substantially increases the signal-to-noise ratio of the 3D reconstructed scene compared with a single two-dimensional (2D) image as readout noise is minimized. This occurs due to the 3D InIm reconstruction algorithm being naturally optimum in the maximumlikelihood sense in the presence of additive Gaussian noise. After 3D InIm reconstruction, facial detection using the Viola-Jones object detection framework is successful whereas it fails using a single two-dimensional (2D) elemental image.
KEYWORDS: 3D image processing, Cameras, 3D image reconstruction, Integral imaging, 3D surface sensing, 3D modeling, Object recognition, Sensing systems, Nonlinear dynamics, Visualization
We overview a previously reported method for three-dimensional (3D) profilometric reconstruction with occlusion removal based on flexible sensing integral imaging. With flexible sensing, the field-of-view of the image system can be increased by randomly distributing a camera array on a non-planar surface. The camera matrices are estimated using the captured multi-perspective elemental images, and the estimated matrices are used for 3D reconstruction. Object recognition is then implemented on the reconstructed image by nonlinear correlation to detect the 3D position of the object. Finally, an algorithm is proposed to visualize the 3D profile of the object with occlusion removal.
We review our recently published work on a passive three-dimensional (3D) imaging technique known as integral imaging (II) using a long-wave infrared (LWIR) camera for face detection and depth estimation under low light conditions. Multiple two-dimensional images of a scene using a LWIR camera are taken, known as elemental images (EI), with each image having a different perspective of the scene. This information is combined to generate a 3D reconstruction of the scene. A 3D face detection algorithm is used on the 3D reconstructed scene to detect a face behind occlusion and estimate its depth. Experimental results validate the method of detecting a human face behind occlusion and estimating the depth.
We overview our recent work [1] on utilizing three-dimensional (3D) optical phase codes for object authentication using the random forest classifier. A simple 3D optical phase code (OPC) is generated by combining multiple diffusers and glass slides. This tag is then placed on a quick-response (QR) code, which is a barcode capable of storing information and can be scanned under non-uniform illumination conditions, rotation, and slight degradation. A coherent light source illuminates the OPC and the transmitted light is captured by a CCD to record the unique signature. Feature extraction on the signature is performed and inputted into a pre-trained random-forest classifier for authentication.
KEYWORDS: 3D displays, 3D image processing, Integral imaging, Image compression, Image encryption, 3D image reconstruction, RGB color model, Computer programming, Photons, Internet
Quick-response (QR) codes are barcodes that can store information such as numeric data and hyperlinks. The QR code can be scanned using a QR code reader, such as those built into smartphone devices, revealing the information stored in the code. Moreover, the QR code is robust to noise, rotation, and illumination when scanning due to error correction built in the QR code design. Integral imaging is an imaging technique used to generate a three-dimensional (3D) scene by combining the information from two-dimensional (2D) elemental images (EIs) each with a different perspective of a scene. Transferring these 2D images in a secure manner can be difficult. In this work, we overview two methods to store and encrypt EIs in multiple QR codes. The first method uses run-length encoding with Huffman coding and the double-random-phase encryption (DRPE) to compress and encrypt an EI. This information is then stored in a QR code. An alternative compression scheme is to perform photon-counting on the EI prior to compression. Photon-counting is a non-linear transformation of data that creates redundant information thus improving image compression. The compressed data is encrypted using the DRPE. Once information is stored in the QR codes, it is scanned using a smartphone device. The information scanned is decompressed and decrypted and an EI is recovered. Once all EIs have been recovered, a 3D optical reconstruction is generated.
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