This PDF file contains the front matter associated with SPIE Proceedings Volume 11841 including the Title Page, Copyright information, and Table of Contents.

To simplify the implementation of photoelasticity studies, the recently introduced Thermal Transient Stepping (TTS) method produces a stress field, from images with fringe displacements induced by temperature. These images are acquired without using mechanically-induced load variations, nor rotating optical devices. However, TTS produces stress fields with unwrapping errors, due to the lack of a strategy to select adequately the fringe displacements. We addressed this limitation by evaluating different thermal stimulations, and their effects in the performance of TTS. This allows us to achieve stress fields with higher fringe orders.

Nowadays, computer vision is an essential part of modern autonomous mobile robots. Fisheye cameras are employed to capture large scenes with a single camera, but the hard radial distortion limits the accuracy of measurements. In this research, a vision system with multiple low-distortion cameras to capture large flat scenes from different viewpoints is proposed. This system applies a homography-based image mosaicing method and linear image interpolation. The obtained results show that the proposed system is useful for visual navigation of ground mobile robots.

In this work we present a new method to optically obtain the convolution / correlation between two functions. The technique is based on obtaining higher powers of a properly designed phase-only hologram. The approach combines some of the advantages of the classical 4f and the JFTC correlator systems. These higher powers of the hologram transmission function can be obtained easily using a spatial light modulator (SLM) with an extended phase modulation of various cycles of 2π radians. SLMs with such extended phase modulation present useful unusual diffraction properties that can be exploited to surpass some limits of SLM technology. In this case we exploit them to obtain higher order optical correlation terms. Computational simulations and experimental results agree with theory.

Evaluation of residual and thermal stresses using temporal analysis of color in photoelasticity images was applied to three discs with residual stresses in different zones. The stress field generated by a compressive load is deformed under residual stress presence. 3D color trajectories for interest pixels show behavior differences between locations with and without residual stress. Finally, k-means analysis for three experiments shows the presence of residual stresses and relates their temporal behavior with a high stress level zone.

Perspective distortion is a typical transformation reproduced by the pinhole model. However, camera lenses introduce radial distortion that reduces the accuracy of image processing tasks, such as lane detection for visual navigation. This paper proposes an image warping method based on the distorted pinhole camera model for lane detection applications. The theoretical principles of the imaging process are analyzed. The usefulness of this method is illustrated by estimating the pose of a ground vehicle using lane lines. The results show that the proposed approach is feasible for visual feedback in robot navigation applications.

Here we report on a prototype optical convolutional neural network accelerator capable of processing large amounts of information, on the order of petabytes, per second. Unlike the current paradigm in electronic machine learning hardware that processes information sequentially, this processor uses the Fourier optics, a concept of frequency filtering which allows for performing the required convolutions of the neural network as much simpler element-wise multiplications using the digital mirror technology. To achieve a breakthrough in this optical machine learning system, we replace spatial light modulators with digital mirror-based technology, thus developing a system over 100 times faster. This innovation, which harnesses the massive parallelism of light, heralds a new era of optical signal processing for machine learning with numerous applications, including in self-driving cars, 5G networks, data-centers, biomedical diagnostics, data-security and more.

Parallel-aligned liquid crystal on silicon devices (PA-LCoS) can be found nowadays in most of the advanced areas in optics and photonics. Many works have been dedicated to their characterization for optimum utilization in applications. However, usual techniques are based on diffractive or interferometric measurements. Recently, we proposed the use of Stokes polarimetry for a versatile yet easy to implement characterization. We show that the LCoS can modelled as a nonabsorbent reciprocal device which, combined with time-average Stokes polarimetry, enables to demonstrate robust measurements across the whole applied voltage range for the retardance and its flicker. One of the main novelties is that we also obtain the director orientation, which we show that changes across the voltage range, especially at larger applied voltages. This might affect in very sensitive applications. It might also provide a deeper insight into the internal dynamics in the LC layer.

We reveal that synthetic photonic lattices based on coupled fiber loops can realize deep neural networks for coherent manipulation of optical pulse trains, which could lead to advances in communications and real-time data analysis. We show the platform feasibility for an essential task of telecommunication signal equalization. We also demonstrated that utilizing all-optical Kerr nonlinearity within the system may open a path to complex signal manipulation.

This paper used digital photoelasticity to evaluate the temporal variations of the stress field in an epoxy-metal embedded actuator. Stress variations were generated with magnetic induction heating-cooling cycles and they were analyzed with frames of a color digital video acquired with a circular polariscope. A phase wavelength stepping algorithm and an unwrapped standard algorithm were applied to obtained unwrapped map. The modification of the fields of bi-material stresses opens the opportunity to generate photoelastic actuators, in consequence they can be used as phase modulators. In consequence, digital photoelasticity is an excellent technique to characterize this effect in bimaterials.

The angular spectrum method (ASM) is commonly used for reconstructing images in digital holography for applications such as lens-free holography and metasurface design. The lack of Fraunhofer or Fresnel approximations and computational speed due to the fast Fourier transform makes ASM a competitive field propagation method. Using a thin-object approximation, ASM can also efficiently compute fields over large areas, enabling faster calculations than those using other methods such as finite difference time domain or Mie theory. However, thin-object approximations are not accurate for nanoscale objects and so ASM is currently unable to accurately recover nanoscale object information. Here we test three ASM transmission models that use a scalar description to model the interaction of a plane wave with a plane of randomly assembled nanoparticles and evaluate the accuracy of each against the discrete dipole method (DDA). Random distributions of nanoparticles are often used in super-resolution, sub-diffraction limit, or specialized sensing applications as they are easy to place. We study the performance of the three transmission models for gold and polystyrene nanospheres of 30 nm, 60 nm, and 100 nm in diameter for different particle densities. The performance of the models is evaluated against simulations using DDA, which is validated against Mie theory calculations, for the same configurations. We show transmission models in ASM that perform within 20% accuracy of the fields calculated using DDA.

Different camera models are usually employed to address specific imaging processes such as telecentric, pinhole, and radial distortion. Recently, the distorted pinhole camera model was developed, and several imaging processes were analyzed as particular cases. This paper shows that the tangential distortion is also a particular case of the distorted pinhole camera model. The mathematical principles of this generalized approach are presented, and its usefulness is illustrated by distorting test images. The results show that the distorted pinhole camera model provides an advanced mathematical framework for computer vision and optical metrology applications

In this paper, we introduce a full-reference quality assessment model for laparoscopic videos. The perceived quality of medical imaging in general is of upmost importance, as visual degradations may lead to severe negative impacts on diagnostic accuracy. Laparoscopy, also known as keyhole surgery, is a camera-assisted operation performed in the abdomen or the pelvis of the patient. Unlike the conventional utilization of telescopic rod lens systems, digital laparoscopy uses a miniature digital camera at the end of the laparoscope, and therefore, the surgeon fully relies on the quality of the medical video. In our scientific contribution, we utilize different image quality measures for each frame of the laparoscopic videos. We implement a regression neural network architecture on the frame-level features with the associated mean opinion score as labels. Finally, we calculate the average of the predicted frame-level scores to compute the overall quality score. The performance of the proposed model is evaluated on the well-known LVQ laparoscopic video dataset. The evaluation results confirm that our model is competitive with the state-of-the-art 2D full-reference and no-reference supervised algorithms. Furthermore, the model demonstrates robust performance across all distortion types of the dataset.

In recent years, photoacoustic imaging as a high sensitivity nondestructive testing technology has been widely studied．It can image biological tissues according to the photoacoustic effect of biological tissues and the differences in optical absorption coefficients of various parts of biological tissues．At the same time, it has the high penetration characteristics of pure ultrasonic imaging and the high contrast characteristics of pure optical imaging, which can provide high resolution and high contrast tissue imaging. Therefore, it can be used as an important means to study the structure and function information of biological tissues, and it is one of the most important real-time medical imaging technologies in the future biomedical field．However, in photoacoustic microscopy, an important branch of photoacoustic imaging, in order to obtain tissue imaging with high resolution and high contrast, it often requires strong focusing, which makes the imaging depth of field smaller and cannot obtain a wide range of biological tissue structure and function information, which is not conducive to medical diagnosis．In order to solve this problem, a three-dimensional information fusion algorithm for photoacoustic microscopy imaging is proposed in this paper．Firstly, we use the virtual photoacoustic microscopy imaging platform to generate two sets of vascular data (only the focus position is different). Then we take out the B-scan data corresponding to the three-dimensional data set, and use the fusion algorithm based on wavelet transform to fuse them in turn．Finally, we use the maximum projection to restore the original data and the fused data, and compare the maximum projection maps before and after fusion．The experimental results show that the algorithm maintains the advantages of high resolution and high contrast, extends the depth of field and obtains a wide range of clear vascular structure.

Current deepfake production methods use auto-encoders augmented by a generative adversarial network (GAN) to create fraudulent but convincing video footage. Developing neural networks to counteract these deepfakes is a highly active area of research—but software-based methods can be immediately used to benchmark even better deepfakes. Thus, there is a need for hardware based solutions to complement existing deepfake detection methods. Here, we present on-chip silicon spectrometer arrays to enhance the number of color channels detected in the imaging system by a factor of 100. These arrays are made up of unique photodiodes engineered to have distinctive spectral responsivities that arise from their photon-trapping, surface based, nanostructures. Videos recorded with this hyperspectral imaging device could complicate the training process for deepfake producers because it collects information that a standard camera cannot. It could also assist novel authentication methods, such as heartbeat monitoring, camera fingerprinting techniques, etc. These spectrometer arrays show a promising direction for continued research in deepfake detection.

The warfighter’s effectiveness in current and future combat missions can be severely limited by a lack of adequate situational awareness (SA). Better target discrimination, better view of the operational scene with larger fields of regard and longer standoff distances are some of the important criteria. SA also strongly depends on the information, signal and data processing that can provide visual and analytics at the edge. This presentation will highlight current and future challenges as well as discuss a path forward to leverage the AI/ML and other imaging technologies. In addition, highlights of the new innovation platform will be presented.

The National Ignition Facility (NIF), the world’s largest and most energetic laser, employs 192 laser beams to achieve inertial confinement fusion by irradiating a mm scale fusion target. The optical Thomson scattering (OTS) laser is being deployed to probe the target and understand the target implosion physics using 3ω and 5ω probe beams. Understanding of the plasma physics will enable improvements of the fusion performance. OTS is a complex system employing many automated alignment loops. Centroid based approach is one of the common approaches for detecting the position of normal Gaussian beams within the OTS for beam alignment. However, due to several optical effects the alignment beam used for one of the pointing loops is shaped like a comet. The pointing beam exhibits a faint tail and a brighter head. Centroiding with a high threshold usually is able to select the brightest spot located within the head. However, there are cases when the brightest region moves around, in some cases the brightest spot appears in the tail, leading to an oscillation in the beam position with the current centroid-based detection scheme. The purpose of this work is to investigate approaches to find a suitable solution to this oscillation and implement a stable beam positioning algorithm.

An algorithm for the recognition and tracking of several objects in color image sequences is presented. First, each three-channel color input image are encoded into a single-channel complex-valued image. Next, a set of prespecified targets are recognized and located in the scene by a composite matched filtering with complex constraints. Afterwards, the set of targets are tracked by adapting the matched filtering to each input image and by processing small image fragments extracted at the predicted coordinates of the targets in the scene. Results obtained with the proposed algorithm in a test image sequence are presented and analyzed in terms of efficiency of target recognition and accuracy of target tracking.

This paper presents the implementation of a mobile service robot with a manipulator and a navigation stack to interact and move through an environment providing a delivery type service. The implementation uses a LiDAR sensor and an RGB-D camera to navigate and detect objects that can be picked up by the manipulator and delivered to a target location. The robot navigation stack includes mapping, localization, obstacle avoidance, and trajectory planning for robust autonomous navigation across an office environment. The manipulator uses the RGB-D camera to recognize specific objects that can be picked up. Experimental results are presented to validate the implementation and robustness.

Identifying the state of stress around a concentrator is essential in a loaded structure. However, most studies are based on circular geometries, leaving aside complex ones such as fractals. In this paper, the effect of fractal concentrators are evaluated by means of digital photoelasticity by considering a circular disc of epoxy resin, a Canon color camera and a Baumer VCXU-50MP polarized camera. Additionally, a phase map was obtained with phase shifting, and phase wavelengths stepping algorithms. The digital photoelasticity executed detection of stress fields related to the fractal concentrator.

This paper presents the implementation of a simultaneous localization and mapping (SLAM) algorithm for autonomous mobile robot navigation. The proposed implementation uses an RGB-D camera to detect the environment and map an occupancy grid that allows the mobile robot to perform autonomous navigation through the environment. The implementation employs the Robot Operating System (ROS) and the Adaptive Monte Carlo Localization to estimate the mobile robot’s current position in the environment with the data retrieved from the RGB-D camera and the odometry data. The mobile robot performs autonomous navigation considering if the robot can safely navigate while avoiding obstacles. Experimental results are presented to validate the implementation.

Digital photoelasticity is used for evaluating the stress in loaded bodies. However, when dynamic analyses are needed, the motions of optical elements are an experimental challenge. This new computational hybrid approach calculates the stress field by extract the phase steps from RGB color channels of a photoelastic color image. Our approach integrated the load stepping strategy with a computational hybrid phase algorithm, hence only bright field images are required. Although, our method has a lower performance than phase shifting methods evaluated, the principal advantage of this hybrid strategy is that only a color- image is required to analyze stress field, avoided capture multiple images for analyzing phase maps.

Kalman filtering (KF) is a widely used filtering technique in highly predictable temporal-mechanical systems where system noise can be modelled with a gaussian function. Improving the signal quality during acquisition is conventionally accomplished by increasing integration time in acquisition. However, this increases the signal acquisition time in photonic systems. In high noise applications, acquisition time is low, and this post-process filtering technique can be applied to increase signal quality. This work explores the comparison of the KF, and nonlinear filtering methods to a simulated blackbody radiation signal where gaussian noise is added to mimic electrical interference. Three filters are selected for comparison on the ability to improve the root mean square error (RMSE) of a simulated measured signal with respect to a simulated actual signal. The filters that are compared in this work are the Extended Kalman Filter (EKF), the Unscented Kalman (UKF), and the Extended Sliding Innovation Filter (ESIF). The filters use a calibration temperature that the filter model uses to determine expected values. To compare the filters, the RMSE is evaluated when error is introduced to the simulation by changing the actual temperature to values equal, below, and above the calibration temperature. Two additional scenarios were considered to test filter robustness. The first scenario uses changes in model temperature occurring as a function of wavelength (i.e., temperature change mid-scan). The second scenario introduces impurities with different emission values. The ESIF demonstrated favorable performance over the other considered filters, showing promise in optical applications.

Digital photoelasticity allows to evaluate the stress field in loaded bodies. There, load stepping method by Ekman and Nurse allowed to avoid inconsistencies and ambiguities. However, it did not become popular by needing six images from two polariscope configurations a three load steeps. This paper updates the conventional method by introducing a polarizer array camera into a circular polariscope. Hence, polarizations of 0° and 90° from a Baumer VCXU50MP camera conduced to bright, and dark field images, simultaneously.With this work, the stress field can be evaluated by using a single optical configuration into the load stepping method.

In this work, we propose a supervised no-reference (NR) Image Quality Assessment (IQA) model for the objective evaluation of the perceptual quality of 3D virtual reality (VR) images. To achieve such practical algorithm, we first study the scene statistics of saliency maps of the individual left and right views of VR images, and empirically model these statistics with Univariate Generalized Gaussian Distribution (UGGD). We compute the UGGD model parameters at multi-scale and multi-orient steerable subband decomposition, and introduce these features as distortion discriminables. This is followed by the computation of the entropy and normalized root mean square scores of each subband and then these values are utilized as weights to pool the individual view features. We apply the popular 2D supervised BRISQUE model on the individual views to estimate the overall spatial quality of VR images. As the last step of the algorithm, the predicted saliency score and the spatial BRISQUE score are pooled to derive the final quality score of VR images. The performance of the proposed model is evaluated on the popular LIVE 3D VR IQA dataset. The results indicate robust and competitive performance against the off-the-shelf 2D full-reference and NR supervised algorithms.

The article proposes an approach that allows merging a series of images obtained using an electron microscope and fusion data with a camera that records data in the far-infrared spectrum (thermal images). The proposed approach is implemented based on algorithms for stitching images obtained in the visible spectrum. Based on the data on the calibration parameters of the pair cameras, the images obtained by the thermal imaging camera are stitched. The frame overlap is between 30% and 50%. The data received by the thermal imaging camera is noisy and requires primary data processing. To filter thermal imaging images, a multi-criteria method is used in work. The parameters of the method are analyzed in parallel on both pairs of the image. They are used for the subsequent identification of the search boundaries of objects and interframe communication points. For data obtained in the visible spectrum, a simplification algorithm is applied to increase data analysis speed. As test data, we used a combination of images obtained by an electron microscope (a maximum approximation is 300x, the color depth is 8bit, the resolution is 1024x768) and a thermal imaging camera (image resolution is 320x240 pixels).

The electrochemical properties of NiCo₂O₄ for using as electrode materials for the potential applications have been investigated by electrochemical measurement which composed of cyclic voltammetry (CV) and galvanostatic chargedischarge (GCD). These processes are generating the ion exchange between the electrode and electrolyte, wherein induce the surface change of the interface. Especially, since in the cycle ability measurement using the potentiostat needed to repeat the cycle into the electrode for many times, the effective of the electrode is reduced. In this research, digital holographic technique was used to investigate the different properties of interface of the NiCo₂O₄ electrode while operating under the various electrochemical techniques. In our DH recording, the surface change of the electrode was expose at the difference potential scan rate at 1, 5, 10, 50, 100 and 200 mV/s of CV, and the difference current densities at 1, 2, 3, 5, 10 and 20 A/g of GCD and vary times of cycle ability test every 50 cycle times to 1000 cycles. In the end the reconstructed DH images of the electrode have been used to analyze the results from electrochemical measurement.