This PDF file contains the front matter associated with SPIE Proceedings Volume 11509, including the Title Page, Copyright information, and Table of Contents.

In this talk, Scott Acton will first talk about recent advances in image analysis that impact the study of the brain. Highlights of this work include the segmentation of neurons, glia and meningeal lymphatics as well as the degeneration and generation of neurons. A graph theoretic model that incorporates elasticity via a shape manifold will be introduced. Connections between the immune system and the brain made by way of image analysis will be discussed. As a program manager in Computer and Information Science and Engineering at the NSF, Acton will discuss programs and opportunities relevant to the SPIE Signal, Image and Data Processing Community.

The present paper explores the implementation of the RRT* path planning algorithm aided with a depth sensor in a physical robot for path planning and re-planning in a partially-known or unknown environment, the robot is capable of omnidirectional motion and aims to move from a starting location to a goal location in different environments. The proposed algorithm allows the robot to move through a map while avoiding collision by detecting unknown obstacles and updating the map for further planning and motion if required. The implementation and experimental results are presented for indoor environments with partial or non-knowledge of the environment in order to achieve autonomous navigation for a holonomic drive robot in an unknown environment using a depth camera as an optical sensing device.

Gender classification of dynamic motion using machine learning has been an active research area using mostly periodic human motion action analysis. This work proposes gender classification for aperiodic dynamic overarm throwing motion actions in an unrestricted environment. Skeletal data from motion capture recordings of throwing actions are represented using posture matrices, and pre-selected kinematic variables are extracted from the skeletal sequences to create biomechanical matrices for each throw action. The posture and biomechanical time-series matrices are converted to a feature vector describing the dynamic characteristics of each sequence. Gender classification is performed on the feature vectors using multiple machine learning models, including support vector machine, k-nearest neighbor, and decision tree with and without AdaBoost. The proposed machine learning classifier models demonstrate high level of gender recognition using aperiodic overarm throwing motion measured with motion capture data. Gender classification accuracy is shown to be further improved with the novel application of biomechanical features.

This paper presents the implementation of mapping, localization, and navigation algorithms for a mobile service robot in an unknown environment. The implementation uses a 3D LiDAR sensor to detect the environment and map an occupancy grid that allows global localization and navigation through the environment. The robot estimates the current position through the Monte Carlo localization algorithm with LiDAR sensor and odometry data. The navigation stack uses inflation to determine if the service robot can safely navigate through the environment, and avoid obstacles. Experimental results were considered using a simulated robot in an indoor environment without given prior knowledge of obstacles presented in the environment.

A parallel implementation of a proposed stereo vision algorithm for three-dimensional scene reconstruction is presented. The algorithm firstly estimates the disparity map of a scene from a pair of rectified stereo images using an adaptive template matched filter. Next, the estimated disparity is utilized to retrieve the three-dimensional information of the scene, by considering the stereo camera's intrinsic parameters. The proposed algorithm is implemented on an embedded graphics processing unit by exploiting massive parallelism for high-rate image processing. The performance of the proposed algorithm is evaluated in real-life scenes captured on a laboratory experimental platform in terms of accuracy and processing speed.

Registration of point cloud with its CAD model can be used to detect products. To register point cloud with its CAD model, feature characters are used. In this article, a registration method based on line contour matching is proposed. The registration process includes two parts. The one is to extract line contour points (LCPs) from point cloud. The other is to match LCPs of point cloud with LCPs of CAD model. To extract LCPs from point cloud, a slice set is constructed to extract section line data (SLD) containing LCPs from point cloud. Then LCPs are identified by judging the feature end of SLD and analyzing normal angle sequence (NAS) of SLD. LCPs of CAD model are non-uniformly distributed. So to make the matching process more efficient and robust, two groups of LCPs are uniformly sampled. Then best fitting method is used to match the two groups of sampled LCPs to obtain transformation parameters. Verification experiments are conducted on three line contours. And the calculated transformation parameters are compared with reference values. The results show that the deviations of rotation angle are within (-0.9°~0.3°). And deviations of translation elements are within ±0.4mm.

In edge enhancement, edges in all directions are usually evenly highlighted in image to demarcate the shape of biological cells apart from other applications. Many diseased cells and parasites deviate from the regularity of a normal cell in terms of shape with deformity increasing with stages of the disease. In this proceeding, a directional / selective edge highlighting using Walsh filters has been proposed. A differentiation between a normal cell and a diseased cell using this selective edge enhancement followed by arc convolutions has been demonstrated. Here, the sample image in spectral domain is multiplied with the Walsh filter before inverse Fourier transform is taken to obtain selectively highlighted edges. Subsequently, arcs of different orientations are convolved with the edge enhanced image. Resulting convolution peaks are isolated from the background by proper thresholding and counted using morphological techniques. For a symmetric cell, convolution with arcs at different orientations gives same number of peaks whereas for asymmetric cells different number of peaks will be obtained. Here, proper thresholding to eliminate lower peaks and selective edge enhancement to highlight or suppress (for example suppressing diameter in semi-circle) certain edges is very important. Also, the positions of the convolution peaks depending upon the arc orientation give edge direction. Spatial Light Modulator (SLM) is usable for the edge enhanced image generation. Additionally, Hilbert transform for edge enhancement has also been used.

A thermal approach for measuring the stress field was developed by using digital photoelasticity. The approach relies on applying a thermal stimulation to the examined model, in conjunction with a computational hybrid algorithm of load stepping, to determine the isochromatic phase value from only three experimental images. The proposal was validated by using a PMMA disk under compressive load and exposed to thermal variations. This experiment was conducted in reflection photoelasticity where a face of the disk was used to observe the fringe patterns, and the back face to capture thermal variations. The results obtained in synthetic and experimental images, indicate that the approach is effective, easy to reproduce, and could enhance the capabilities of existing approaches to analyze stress fields.

Estimation of projective transformations is an essential process in modern vision-based applications. Usually, the provided experimental point correspondences required to estimate the projective transformations are corrupted with random noise. Thus, for an accurate estimation of the actual projective transformation, a robust optimization criterion must be employed. In this work, we analyze a two-step estimation approach for robust projective transformation estimation. First, the algebraic distance is employed to obtain an initial guess. Then, the geometric distance is used to refine this initial guess. Three geometric-based refining methods are evaluated, namely, the one-image error, the symmetric-transfer error, and reprojection. The obtained results confirm a high accuracy and robustness of the analyzed approach.

Fatigue tests impose experimental conditions that are difficult to achieve, and require using highly specialized equipment. We propose an inexpensive and easy to implement approach, to evaluate fatigue caused by cyclic loading. The approach includes a custom-built assembly and a modified jigsaw. The later impacts a circular plate of epoxy resin repeatedly, which produces their cyclic loading. After evaluating the plate with different loading frequencies, we found a direct relation between photoelasticity fringes and temperature at the plate geometry, despite experiencing some difficulties when obtaining photoelasticity data due to high loading frequencies and stress saturation of the samples. This approach also provides an integration tool between photoelasticity and infrared thermography for experimental stress analysis related to fatigue.

An asymmetric single-channel color-image cryptosystem is presented that uses singular value decomposition and Tinkerbell map in the fractional Fourier domain. The input image is split into red, green, and blue channels. The red component is used as plaintext, and green and blue components are used as phase masks in the cryptosystem. MATLAB is used for computations of results presented in this paper. The proposed cryptosystem is validated for color images of size 256 × 256 × 3 pixels. The analysis of the attacks establishes the scheme’s resistance to occlusion and noise attack, and the key-space is big enough to endure a brute-force attack. The analysis based on the correlation distribution of neighboring pixels, along with statistical analysis has been performed to examine the efficacy of the cryptosystem. The results indicated high levels of security possessed by the proposed encryption scheme.

We implemented a lens-free holographic microscope (LFHM) that can image ~105 2-μm microspheres in solution over an ultra-large field-of-view >15 mm2 with sub-micron resolution and enhanced signal-to-noise ratio in less than 1 second. This performance is achieved using a high-speed hardware design for multi-frame pixel super-resolution and a novel sparsity-promoting reconstruction algorithm. We use the microscope as a biomolecular sensor to detect and quantify NeutrAvidin protein molecules. We coat 2-μm microspheres with biotin, which binds strongly to NAv, causing observable bead binding and clustering. This is quantified through an automated image processing algorithm and is used to infer NAv concentrations.

In this paper, we present a reliable method for three-dimensional object reconstruction based on multi-camera sensor arrays. Camera arrays are an efficient strategy for high-performance imaging and view interpolation. However, challenges can be present such as data size, real-time processing, and the need for automatic calibration. We analyze several methods for three-dimensional object digitalization using camera arrays. This analysis will help us to compare different algorithms and combine several 3D reconstruction methods in a different, efficient and quick way. Experimental results are presented using real and synthetic laboratory objects in terms of objective metrics for 3D reconstruction accuracy. The robustness of these methods for real applications is discussed.

This Conference Presentation, "Characterization of position and two-dimensional profiles of high-density beam spot array for digital lithography system," was recorded for SPIE Optics + Photonics Digital Forum 2020.

For avoiding fails in loaded structures, adjust their geometry, removing material, or quantify residual stresses, photoelasticity studies often is limited by complex experiments, excessive computational procedures, expert supervision, narrow applications, and static focus. This paper proposes a pattern recognition-based strategy for evaluating the stress field from simplex dynamic experiments. Here, temporal color variations are processed to extract, select and classify stress magnitudes, isotropic points, and inconsistent information. This approach used synthetic photoelasticity videos from analytical stress models about disk and ring under diametric compression. Additional to improve limitations in conventional photoelasticity approaches, this strategy identifies isotropic and inconsistent points.

Pose estimation is an essential task in many mobile robot navigation systems. Visual guidance provides a feasible means for pose estimation using the observed scene information as reference. This work presents an approach to estimate the pose of a mobile robot based on projective transformations. First, the Hough transform is used for lane detection. Next, a projective transformation is computed using the detected lines as reference. Finally, the robot's pose is estimated from the resulting projective transformation. The theoretical principles and computational implementation are analyzed. Experimental results of a visual navigation experiment are presented to validate the usefulness of the proposed approach.

One of the biggest challenges of free-space optical (FSO) communication is the wave-front aberration due to atmospheric turbulence. In FSO links the wave-front distortion manifests as a significant drop in received power, beam wander, information loss, and scintillation effects. The performance of FSO communication system is degraded significantly by the atmospheric turbulence effects. Fortunately, the adaptive optics system offers potential to mitigate the performance degradation, which is relevant for quantum communication applications as well. In our FSO experiment, we perform the transmission of 6.25 GBd QPSK signal over an FSO link without and with adaptive optics, operating at 1550nm. We emulate the atmospheric aberration in our indoor experimental setup by applying random Kolmogorov phase screens on spatial light modulators (SLMs). We demonstrate significant improvements in the power-collected, signal-to-noise-ratio (SNR), and bit-error-rate (BER) performance due to the application of adaptive optics.

With the evolution of cellular network generations to offer higher data rate to the users, 5G aims to offer a capacity up to 10Gbps. This puts an additional impetus for the backbone networks to be able to carry the large volume of data at aggregation points using optical fiber communication network. The voluminous data transportation should be achieved for longer link length with optimum Quality of Service. We have proposed and evaluated an Optical Fiber Communication (OFC) link based on Polarization Division Multiplexing (PDM) in conjunction with Hybrid Optical Amplifier (HOA) for 16/64 QAM. Polarization Mode Dispersion (PMD) has been a critical issue. Although PMD increases the capacity, it also results in cross talk and cross phase modulation. In order to counter the PMD, Polarization Controller (PC), Polarization Beam Combiner (PBC) and splitter have been included besides the HOA over the fiber length varying from 30 to 160Km at a data rate of 5Gbps. The performance of the proposed link is compared with a conventional OFC link without using hybrid configuration. Also, the hybrid amplifier configuration has been evaluated for bit rates varying from 5 to 8Gbps at 100km fiber length. BER, Q-factor and Eye diagram have been chosen as the performance metrics.

Scattering light imaging technique has attracted extensive research because of its huge potential in the fields of biomedical microscopy, remote-sensing mapping etc. For most methods now available to reconstruct an object hidden behind scattering media, the main focus is on reconstructing the shape of the object without considering its spectral information. While imaging a color object, it is often necessary to measure a series of Point Spread Functions (PSFs) or Wavelength-Dependent Speckle Patterns (WDSPs) under various wavelengths of illumination. It’s obvious that these methods are either invasive to the object or require multiple exposures. Here, by taking advantage of the Wavelength- Dependent Response Characteristics (WDRC) of the Liquid Crystal Spatial Light Modulator (LC-SLM), we propose an alternative way to reconstruct a hidden color object with noninvasive and single-exposure strategy. A monochromatic camera is adopted to capture the wavelength-multiplexing gray-scale speckle pattern, which can be then demultiplexed into a number of WDSPs by utilizing of a designed Multi-modal Phase Retrieval Algorithm (MM-PRA). Then, a typical speckle correlation technique (SCT) is applied to reconstruct each component of the hidden color object. The feasibility and effectiveness of the proposed method are demonstrated by numerical results in this work while the optical experiments are on the way.

A new approach of carbon nanoparticle using for the optical diagnostics of а complex scalar optical field obtained by scattering and diffraction of radiation on a surface with roughness is suggested in this paper. Luminescence of carbon nanoparticles made it possible to register their coordinate position in time. The algorithm for the reconstruction of scalar optical field intensity distribution through the analysis of nanoparticle position was proposed in the paper.

We propose a method of detecting respiration curve for the Drowsiness Predicting system based on optical image information processing. It is well known that a falling sleep relate to respiration. This paper focus on a frequency and amplitude of respiration while driving to predict a moment of a falling sleep. Proposed system composed of video camera and image processing unit. Video camera capture time-series image while driving. Image processing unit calculate subtract images per any frame. The respiration ratio is extracted to analysis the time-series signal. In this paper, we have developed a test bed system, and evaluate our system in comparison with a medical device.

By Mueller-matrix mapping of changes in the distribution of the magnitude of matrix invariants characterizing the degree of crystallization of histological sections of the brain, liver and kidney, the forensic medical criteria were determined and the interval for determining the damage duration of 24 hours with an accuracy of 45min. - 50 min. was established. For azimuthally invariant polarization mapping of changes in the distributions of Mueller-matrix invariants characterizing the optical activity of molecular complexes of histological sections of the brain, liver, and kidney, forensic medical criteria were developed for the first time and accuracy was improved to 35 min. - 40 min. in the interval for determining the damage duration period of 72 hours. For highly accurate objective determination of the damage duration in a long time interval (1 hour - 120 hours), a polarization tomography method has been developed (reproducing the distributions of birefringence of fibrillar networks of histological sections of internal organs), which provides an accuracy of 25 minutes. (1 hour - 24 hours) to 45 minutes (24 hours - 120 hours).

Often during research and development a precise knowledge on derivatives is required. In many cases it is very difficult or impossible to obtain derivatives analytically. This usually occurs in situations when the data to be processed are from an experiment and, therefore, is discrete and with a mixture of noise. The same situation is observed when data to be processed are obtained from numerical simulations. Here we present a detailed comparison of four methods to obtain higher-order derivatives from digital/discrete data. Finite differences method, complex step method, Richardson's extrapolation method, and complex integration method are compared to get an accurate higher-order derivative approximation. Each of them has different properties which make them reliable for a variety of applications and can be easily implemented using software tools.

In Silicon photonics, there is an ever-growing need for optical switches capable of high-speed operation, needed for applications such as optical computing, microwave photonics, and LiDAR. One potential candidate for a highspeed optical switch consists of two PIN junctions connected in series, reverse-biased. When one PIN junction is fed with an oscillating optical input at some frequency and the other with a constant threshold input, the output displays bistable hysteresis, ideal for an optical binary switch. In this research, PSpice and MATLAB are used to develop mathematical models of the circuit and simulate the operation of the Symmetric PIN, or S-PIN switch at various frequencies. While the work done so far has been highly theoretical, measurements have been carried out on an optoelectronic prototype of the S-PIN in order to observe the basic operation of the device. Additionally, a model of the S-PIN developed in MATLAB agrees closely with simulations of the switch in PSpice. In the physical circuit, the presence of parasitic capacitance and inductance in the PIN junctions cause the shape of the output hysteresis to deteriorate as the running frequency is increased. Simulations show that if parasitic capacitance is minimized, the switch can operate in the GHz range. One method explored to mitigate this capacitance is impedance matching, which has shown to improve output bistability to some extent. Results from modeling indicate that the use of components with low capacitance has the most potential to allow the S-PIN to operate at higher frequencies competitive with modern electronic switches.

The report contains a structural-logical diagram and design of studies using polarization and Mueller-matrix microscopy methods of histological sections of the brain. The results of the differential diagnosis of the formation of hemorrhages of traumatic origin, ischemic cerebral infarction and hemorrhagic genesis using Mueller-matrix microscopy are presented.

Azimuthally invariant Mueller-matrix images of linear birefringence (MMI LB) of histological sections of the brain and operational characteristics of the method of their statistical analysis.

Azimuthally invariant Mueller-matrix images of circular birefringence (MMI CB) of histological sections of the brain and operational characteristics of the method of their statistical analysis

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Differential diagnosis of the duration of the formation of hemorrhages of traumatic origin, ischemic cerebral infarction and hemorrhagic genesis using Mueller matrix mapping.

Temporal dynamics of changes in the statistical structure of maps of Mueller-matrix invariants of optical activity (MMI OA) of histological sections of the brain.

Temporal dynamics of changes in the statistical structure of maps of Mueller-matrix invariants of linear birefringence (MMI LB) of histological sections of the brain.

This manuscript presents materials on the study of the spectral dependences of the parameters of the Stokes vector of electromagnetic radiation transmitted through layers of synovial fluid of the knee joint with various types of pathology.The spectral coefficients of Gram - Charlier decomposition of spectral Stokes - polarimetric dependences are established and the sensitivity and specificity of this method for the diagnosis or differentiation of the type of knee joint pathology are determined.

This section contains the results of a study of the relationships between the 3D distributions of the optical anisotropy parameters of polycrystalline networks of biological fluid films of different biochemical composition and the layered phase sections of volume distributions of the magnitude and parameter phase of the "two-point" Stokes vector of the microscopic image.

The method of stock-polarimetry with spatial-frequency filtering of the distributions of the Muller-matrix invariants (MMI) for analytically differentiating the manifestations of the phase anisotropy mechanisms of different-scale fibrillar networks of histological sections of biological tissues of different morphological structure and physiological state is analytically substantiated. It has been established that all statistical moments of the first and fourth orders characterizing the distribution of the values of the matrix element of the linear birefringence of the large-scale fibrillar optically anisotropic component of benign and malignant tumors are diagnostically sensitive. The following levels of balanced accuracy have been achieved: 77,8% R_i=3;4 (good quality diagnostic test), R_i=1;2 = 91,7% 94,4% (excellent quality diagnostic test). For the first time, a polarization reconstruction method with spatial-frequency differentiation of the distributions of MMI values characterizing circular birefringence of histological sections of multilayer biological tissues was developed to differentiate changes in the phase anisotropy of polycrystalline networks in the early stages of oncological pathology - precancerous conditions (simple atrophy, polyp) of the rectal wall. On this basis, a high level of balanced accuracy R ~ 85% of the diagnostic test for differentiating the manifestations of the optical activity of endometrial networks in a precancerous state has been achieved.

Manuscript contains:

Structural-logical scheme and analytical description of the differential diagnosis of aseptic and septic loosening of the artificial hip joint endoprosthesis using the methods of differential Mueller-matrix mapping of circular birefringence (CB) distributions of polycrystalline synovial fluid (SF) films.

Results of statistical analysis of the distributions of CB of polycrystalline SF films of patients from the control group and groups with different severity of the hip joint pathology.

Results of establishing the strength of the differential Mueller-matrix mapping method of the distributions of CB polycrystalline films of SF by means of information analysis based on sensitivity determination, specificity and accuracy of the polarization tomography technique.

Diffuse reflectance spectroscopy (DRS) has been widely used to interrogate the metabolic state and structure of several biological tissues with diagnostic purposes. The aim of this research is to compare the melanin content (melanosome volume fraction) of normal non-pigmented human skin sites versus skin sites containing tiny moles ranging from 1.2 mm to 2 mm diameter. The study was carried out in a group of ten volunteers presenting five different skin phototypes (I-V). Diffuse reflectance spectra of these sites were acquired with a USB 4000 fiber optic spectrometer and homemade optical probe consisting of two optical fibers separated 2.5 mm. In order to extract optical properties of skin sites containing tiny moles, a relative simple but novel skin model was proposed and used in combination with a slightly modified version of the inversion algorithm analyze2.m which is freely available. We found that melanin content ranges from 3.3 % to 29.2 % for the normal skin sites surrounding pigmented skin sites, while for skin sites with tiny moles melanin content varies from 7.6 % to 78.4 %. Our finding shows that for each volunteer the melanin content of pigmented areas is twice or more the melanin content of non-pigmented normal skin areas. In addition, we have evaluated the sensibility of the inverse extraction algorithm when the melanin content is varied while the remaining "guess parameters" (S, B, a, const) are kept constant. We have found that all the remaining parameters are strongly affected when the melanin content is changed.

Deconvolution-based techniques have been widely used for imaging through scattering medium due to the optical memory effect (OME) in speckles. Once the point spread function (PSF) of a scattering system is measured, a smallscale object within the OME region can be easily recovered. However, an extended object larger than the OME region can only be partially reconstructed due to the limited field of view (FOV). Here, we find a way to get an integrated PSF by exploiting a point source with different locations in object plane. Thereafter, an extended object, within the FOV but exceeding the OME region, could be recovered by the integrated PSF without knowing any other system parameters even the locations of the point source.