Spherical Images Research Articles

Calibration of paracatadioptric cameras based on sphere images and pole-polar relationship

With a paracatadioptric camera, according to the projective antipodal properties of the space sphere in the unit sphere model, two infinity points and a symmetric axis can be obtained. On the image plane, the image of the symmetric axis is the vanishing point’s polar with respect to the image of the absolute conic (IAC), and an infinity point is orthogonal to the polar direction of a circle. Thus, we obtain three vanishing points orthogonal to each other. This study extends research into the internal sphere parameters and the vanishing point in a paracatadioptric camera. The results of tests confirm the feasibility and effectiveness of the proposed calibration algorithms.

Systematic and random errors of PET-based 90 Y 3D dose quantification.

The objective was to characterize both systematic and random errors in Positron Emission Tomography (PET)-based 90 Y three-dimensional (3D) dose quantification. A modified NEMA-IEC phantom was used to emulate 90 Y-microsphere PET imaging conditions: sphere activity concentrations of 1.6and 4.8MBq/cc, sphere-to-background ratios of 4 and 13, and sphere diameters of 13, 17, and 37mm. PET data were acquired using a GE D690 PET/CT scanner for 300min on days 0-11. The data were downsampled to 60-5min for multiple realizations to evaluate the count starvation effect. The image reconstruction algorithm was 3D-OSEM with PSF+TOF modeling; the parameters were optimized for dose-volume histogram (DVH), as a 90 Y 3D dose quantification. 90 Y-PET images were converted to dose maps using the local deposition method, then the sphere DVHs were calculated. The ground truth for the DVH was calculated using convolution method. Dose linearity was evaluated in decaying 90 Y activity (reduced count rate and total count) and decreasing acquisition durations (reduced total count only). Finally, the impacts of the low 32-ppm positron yield on PET-based 3D 90 Y-dose quantification were evaluated; the bias and variability of resulting DVHs were characterized. We observed nonlinear errors that depended on the 90 Y activity (count rate) and not on the total true prompt counts. These nonlinear errors in mean dose underestimated the measured mean dose by>20% for a measured dose range of 40-230Gy; although the shapes of the DVH were not altered. Compensation based on empirical models reduced the nonlinearity errors to be within 5% for measured dose range of 40-230Gy. In contrast, the errors due to nonuniformity introduced by image noise dominated the systematic errors in the DVH and stretched the DVH on both tails. For the 37-mm sphere, the magnitude of errors in D80 increased from -25% to -36% when acquisition duration was decreased from 300 to 10min. The effect of image noise on DVH was more extensive in smaller spheres; for the 17-mm sphere, the magnitude of errors in D80 increased from -29% to -45% acquisition duration was decreased from 300 to 10min. For the 37-mm sphere, the errors in D20 increased from +3.5% to only +10.5% when the acquisition duration was decreased from 300 to 10min; in the 17-mm sphere, the errors in D20 were 6.5% for both 300- and 10-min sphere images. Count-starved 90 Y-PET data introduce both systematic and random errors. The systematic error increases the apparent nonuniformity of the DVH, while the random error increases the uncertainty in the DVH. The systematic errors were larger than the random errors. Lower count rate of 90 Y-PET also introduces systematic bias, which is scanner specific. The errors of bias-compensated mean tumor dose were <10% when 90 Y-PET scan time was >15min/bed for tumors >37mm. Dmedian and Dmean were the most stable dose metrics. An acquisition duration of 30min is recommended to keep the random errors<10% for a typical tumor with sphere equivalent diameter >17mm and average tumor dose >40Gy.

Automatic Tree Detection from Three-Dimensional Images Reconstructed from 360° Spherical Camera Using YOLO v2

It is important to grasp the number and location of trees, and measure tree structure attributes, such as tree trunk diameter and height. The accurate measurement of these parameters will lead to efficient forest resource utilization, maintenance of trees in urban cities, and feasible afforestation planning in the future. Recently, light detection and ranging (LiDAR) has been receiving considerable attention, compared with conventional manual measurement techniques. However, it is difficult to use LiDAR for widespread applications, mainly because of the costs. We propose a method for tree measurement using 360° spherical cameras, which takes omnidirectional images. For the structural measurement, the three-dimensional (3D) images were reconstructed using a photogrammetric approach called structure from motion. Moreover, an automatic tree detection method from the 3D images was presented. First, the trees included in the 360° spherical images were detected using YOLO v2. Then, these trees were detected with the tree information obtained from the 3D images reconstructed using structure from motion algorithm. As a result, the trunk diameter and height could be accurately estimated from the 3D images. The tree detection model had an F-measure value of 0.94. This method could automatically estimate some of the structural parameters of trees and contribute to more efficient tree measurement.

A Research on the Spherical Indicatrices of Directional Space Curve

In this paper, we give a parametrization of a directional space curve by using adapted frame called q-frame.. The spherical images of both directional normal indicatrix and directional tangent indicatrix of this space curve with q-frame are studied. Later using the geodesic curvature of the spherical image of the directional normal indicatrices, we work on the condition that a space curve to be slant helix. Finally, we give an application of the results.

Directional dependent variation in mechanical properties of planar anisotropic porcine skin tissue

Nonlinear and anisotropic mechanical behavior of skin is essential in various applications such as dermatology, cosmetic products, forensic science, and computational studies. The present study quantifies the mechanical anisotropy of skin using the bulge method and full-field imaging technique. In bulging, the saline solution at 37 °C mimics the in vivo body temperature and fluid conditions, and all experiments were performed in the control environment. Assumption of thin spherical shell membrane theory and imaging techniques were implemented to obtain the anisotropic stress strain relations. Further, stress strain relations at an interval of 10° were calculated to obtain the variation in modulus with direction. Histological examinations were performed to signify the role of the collagen fibers orientation on the mechanical properties. The maximum and minimum linear modulus and collagen fiber orientation intensity were found in good agreement. The angular difference between maximum and minimum linear modulus and orientation intensity was found 71° ± 7° and 76° ± 5° respectively, and the percentage difference was 43.4 ± 8.2 and 52.5 ± 6.4 respectively. Further, a significant difference in the maximum and minimum collagen orientation intensity between the untested and tested specimens indicates the realignment of the fibers. Additionally, a cubic polynomial empirical relation was established to calculate the quantitative variation in the apparent modulus with the directions, which serves for the anisotropic modeling of the skin. The experimental technique used in this study can be applied for anisotropic quantification of planar soft tissues as well as can be utilized to imitate the tissue expansion procedure used in reconstructive surgery.

The Characterizations For The Spherical Images of Timelike W-Curves in Minkowski Space-Time

We know that W-curve is a curve which has constant Frenet curvatures. In this study, firstly, we have investigated the principal normal and binormal spherical images of a timelike W-curve on pseudohyperbolic space in Minkowski space-time . Besides, the binormal spherical image of the timelike W-curve is a spacelike curve which lies on pseudohyperbolic space Hence, we have obtained the Frenet-Serret invariants of the mentioned image curve in terms of the invariants of the timelike W-curve in the same space. Finally, we have given some characterizations of the spherical image in the case of being helix for the timelike W-curve in Minkowski space-time .

Multi-color optical microscopy using modulated excitation lasers and single-frequency demux

Multi-color microscopy has been a powerful tool for industrial and biomedical applications. Traditionally, image acquisition through different color channels has to be carried out separately using a single detector for sequential detection or multi-detectors for simultaneous detection. This costly and complex process may introduce colocalization errors due to the alignment. This paper describes a compact multi-color microscope using modulated excitation lasers and a single electronic frequency demux for signal detection. To mark fluorescence from different labels in the sample separately, the excitation lasers are intensity modulated with different frequencies. After fluorescence is detected, the electronic signals are demultiplexed with a single spectrum analyzer. This method allows the realization of multi-color imaging with minimal amount of detection elements. Multi-color imaging of fluorescent spheres, gold nanoparticles, and biological tissues are carried out. A fiber-coupling system has also been used to simplify the optical alignments, while its focal chromatic aberration has been characterized.

Simulation-Based Design of Optimized Symmetric MRI Magnet Modified With Ferromagnetic Shell

This article presents a methodology of simulation-based design of magnetic resonance imaging (MRI) magnets, modified with a symmetric ferromagnetic shell. The design methodology employs the gradient-based optimization solver of COMSOL Multiphysics to obtain an optimum geometrical arrangement of symmetrical electromagnets in the MRI system. The aim of this article is to design an MRI magnet system, modified with a ferromagnetic shell, which can operate at minimum possible current, can provide a critical level of field homogeneity within the spherical imaging volume, and has magnetic coils with shorter radii. The optimized design solution provides the dimensions of coil blocks, dimensions of ferromagnetic shell, the total length of conductor in each coil block, positions of the coil blocks, current, and the resultant magnetic field. This article reveals that the modification of an MRI system with a ferromagnetic shell significantly improves the field uniformity within a central spherical zone having a diameter of 40-50 cm and greatly reduces the current requirement. The design methodology adopted in this study can be extended to other kinds of configurations.

Calculating the Fold Angles of Any Vertex Roof Using a Spherical Image Technique

Abstract The shape of a vertex roof is defined by the geometry of its constituent flat facets and the relative angle of folding across them. The spherical image of the roof, originally from Gauss, expresses these properties simultaneously. We present a method for calculating the image properties and thence the shape of any vertex roof based on subdividing the image into an array of suitable spherical triangles. In particular, we introduce a truss representation of the vertex for choosing viable subdivisions of the image, which allow full calculations to be made. Additionally, this allows us to construct generalized closed-form expressions for the fold angles of vertex roofs, presented here for roofs with up to six facets.

Explanation of the Sense of Visual Perception in Cornering Based on Gaze Position and Optical Flows

In the recently developed self-driving technologies, users often experience strangeness when performing cornering control such as in lane-keeping assist. To realize human-centered cornering control, we had proposed a human perception model that considers optical flow and gaze position for cornering. In this paper, we propose a divergence point detection method for identifying the target position on the spherical image corresponding to the gaze position. We first discuss the relationship between optical flow and gaze movement in cornering, and we identify optical flows radiating outward from the gaze position. Next, we explain a divergence point detection method that uses optical flow vectors based on particle swarm optimization. For verifying the proposed method, we perform an experiment using an actual vehicle; the results show that the divergence detection method can detect a divergence point.

Discrete Spherical Image Representation for CNN-Based Inclination Estimation

How an image is represented as the input of a convolutional neural network (CNN) is important because this input directly influences the performance of the CNN. In this paper, we investigate the representation of spherical images by focusing on the inclination estimation of a spherical camera. Unlike other approaches to CNN-based inclination estimation, a spherical image is represented as a geodesic-division-based discrete spherical image (DSI) that is obtained by sampling a sphere as uniformly as possible. The input of the CNN is a single image that consists of five parallelograms flattened from a regular icosahedron. To demonstrate the advantage of the proposed method, comparative experiments are conducted with two other spherical image representations, namely, equirectangular projection (ERP) and cubemap projection (CMP). The experimental results show that the proposed method using a geodesic-division-based discrete spherical image as the CNN input obtains the best performance-better than that of the cubemap and far superior to that of the equirectangular image. The effect of the image representations used becomes more significant as the relative inclination decreases. Moreover, comparative experiments are conducted using the state-of-the-art methods for spherical camera inclination compensation to further illustrate the superiority of the DSI representation. Consequently, the proposed method provides an important reference for the development of CNNs intended for spherical images.

Vector Based Object Identification in Spherical Images

Abstract 3D virtual environment of the real world library is built using the spherical images. Nowadays identifying an object in a spherical image is a challenging task in itself, a technique is introduced to identify a particular object in a spherical image. Our case study is towards identifying a particular book in a book-shelf image which is spherical in nature. The proposed work is divided into 3 modules, the first module includes Speeded Up Robust Feature (SURF) detection and extraction. Second module incorporates application of vector based mismatch feature removal algorithm on detected SURF features. Finally the boundary box or a marker is drawn on the book detected. Proposed technique examines which format of spherical image yields better result. Experimental results show that SURF on cubic image yield better results than SURF on equirectangular image.

Robust Spherical Panorama Image Watermarking Against Viewpoint Desynchronization

Although quantitative and qualitative growth of spherical panorama content has been achieved, watermarking methods for copyright protection are still insufficient. The spherical panorama image watermarking technique must be able to detect a watermark from the viewed images, but it is difficult to detect the watermark directly from the images due to various distortions. We propose a method for recovering the watermark-detection target viewed image to an equirectangular-formed source image and detecting the watermark after recovery. For recovery, we propose combining the scale-invariant feature transform point matching and the shift and rotation estimation method using a Euclidean transformation matrix. For watermark embedding and detection, a discrete Fourier transform magnitude coefficient is used. With the shift-invariant characteristic, our proposed method is robust against viewpoint desynchronization. Because finding an accurate viewpoint is difficult, our method offers a substantial advantage in the spherical panorama image watermarking scheme. Furthermore, it allows watermark detection on a continuous viewpoint spherical panorama system. In addition, our experiments reveal that our method is robust against signal attacks, such as JPEG compression, Gaussian filter blurring, noise addition, and histogram equalization.

SelfSphNet: Motion Estimation of a Spherical Camera via Self-Supervised Learning

In this paper, we propose SelfSphNet, that is, a self-supervised learning network to estimate the motion of an arbitrarily moving spherical camera without the need for any labeled training data. Recently, numerous learning-based methods for camera motion estimation have been proposed. However, most of these methods require an enormous amount of labeled training data, which is difficult to acquire experimentally. To solve this problem, our SelfSphNet employs two loss functions to estimate the frame-to-frame camera motion, thus giving two supervision signals to the network with the usage of unlabeled training data. First, a 5 DoF epipolar angular loss, which is composed of a dense optical flow of spherical images, estimates the 5 DoF motion between two image frames. This loss function utilizes a unique property of the spherical optical flow, which allows the rotational and translational components to be decoupled by using a derotation operation. This operation is derived from the fact that spherical images can be rotated to any orientation without any loss of information, hence making it possible to “decouple” the dense optical flow between pairs of spherical images to a pure translational state. Next, a photometric reprojection loss estimates the full 6 DoF motion using a depth map generated from the decoupled optical flow. This minimization strategy enables our network to be optimized without using any labeled training data. To confirm the effectiveness of our proposed approach (SelfSphNet), several experiments to estimate the camera trajectory, as well as the camera motion, were conducted in comparison to a previous self-supervised learning approach, SfMLearner, and a fully supervised learning approach whose baseline network is the same as SelfSphNet. Moreover, transfer learning in a new scene was also conducted to verify that our proposed method can optimize the network with newly collected unlabeled data.

Panoramic Image Generation: From 2-D Sketch to Spherical Image

The 360-degree video/image, also called an omnidirectional video/image or panoramic video/image, is very important in some emerging areas such as virtual reality (VR). Therefore, corresponding image generation algorithms are urgently needed. However, existing image generation models mainly focus on 2-D images and do not consider the spherical structures of panoramic images. In this article, we propose a panoramic image generation method based on spherical convolution and generative adversarial networks, called spherical generative adversarial networks (SGANs). We adopt the sketch map as the input, which is a concise geometric structure representation of the panoramic image, e.g., comprising approximately 7% of the pixels for a 583 × 1163 image. Through adversarial learning, a realistic-looking, plausible and high-fidelity spherical image can be obtained from the sparse sketch map. In particular, we build a dataset of the sketch maps using a visual computation-based sketching model. Then, by optimizing SGANs with GAN loss, feature matching loss and perceptual loss, realistic textures and details are recovered gradually. On one hand, it is an improvement using the sparse sketch map as input rather than the denser input, e.g., the features of the textures and colors. On the other hand, spherical convolution helps to remedy space-varying distortions of the planar projection. We conduct extensive experiments on some public panoramic image datasets and compare them with state-of-the-art techniques to validate the superior performance of the proposed approach.

Цифровое пространство как поле политического противостояния власти и оппозиции

Digitalization, rapidly changing the image of the basic social spheres, including the political sphere, transforms the competition conditions for the two basic spheres of the inner political arena – authority and opposition. The level of development of digital technologies and their penetration into modern social processes is so high that it allows speaking about emergence of digital space, which peculiarities are taken into account in building communication and competition in this couple. Both the opposition and authority have seen a lot of new opportunities in reaching their goals and achieving their aims, but at the same time a lot of problems and vulnerabilities, which they can't not consider. In this regard the digital space brings with itself ambiguous and sometimes even controversial prospective for them. In the article the attempt to state the main problems and discussions connected to understanding the digital space as a new field of political confrontation of authority and opposition is made. For that purpose the central notions of the issue investigated such as “digitalization” and “digital space” are analysed; the most important conditions of confrontation of authority and opposition in the digital space are considered; the main outline of the changes in the interaction of authority and society is drawn; the evaluation of the influence of digital space on the “traditional” democratic institutes is given.

3D Spherical Panoramic Epipolar Line Based on Essential Matrix

A 3D spherical panoramic epipolar line based on essential matrix is proposed in this study to solve the search range to improve calculation efficiency and reliability of the matching. First, this method uses the essential matrix of computer vision principles to establish the epipolar geometry model of spherical panoramic image. Second, it derives the epipolar line mathematical equations and characteristics of spherical panoramas. Third, it calculates statistics on the distribution law of epipolar images. Experimental results show that the feasibility of the method proposed in this study. The proposed method effectively verified the trajectory of the spherical panoramic epipolar line, finished distribution of epipolar line sequence trajectory and decreased the search range of the corresponding points. The findings help lay a solid foundation for the matching and measurement of the panoramic measurement model and the generation of depth maps.

Characterizing Intra-soma Diffusion with Spherical Mean Spectrum Imaging.

Most brain microstructure models are dedicated to the quantification of white matter microstructure, using for example sticks, cylinders, and zeppelins to model intra- and extra-axonal environments. Gray matter presents unique micro-architecture with cell bodies (somas) exhibiting diffusion characteristics that differ from axons in white matter. In this paper, we introduce a method to quantify soma microstructure, giving measures such as volume fraction, diffusivity, and kurtosis. Our method captures a spectrum of diffusion patterns and scales and does not rely on restrictive model assumptions. We show that our method yields unique and meaningful contrasts that are in agreement with histological data. We demonstrate its application in the mapping of the distinct spatial patterns of soma density in the cortex.

Segmented Spherical Projection-Based Blind Omnidirectional Image Quality Assessment

In contrast with traditional images, omnidirectional image (OI) has a higher resolution and provides the user with an interactive wide field of view. OI with equirectangular projection (ERP) format, as the default for encoding and transmitting omnidirectional visual contents, is not suitable for quality assessment of OI because of serious geometric distortion in the bipolar regions, especially for blind image quality assessment. In this paper, a segmented spherical projection (SSP) based blind omnidirectional image quality assessment (SSP-BOIQA) method is proposed. The OI with ERP format is first converted into that with SSP format, so as to solve the problem of stretching distortion in the bipolar regions of ERP format, but retain the equatorial region of ERP format. On the one hand, considering that the bipolar regions of the SSP format are circular, a local/global perceptual features extraction scheme with fan-shaped window is proposed for estimating the distortion in the bipolar regions of OI. On the other hand, the perceptual features of the equatorial region are extracted with heat map as weighting factor to reflect users' visual behavior. Then, the features extracted from the OI's bipolar and equatorial regions are pooled to predict the quality of distorted OIs. The experiments on two databases, namely CVIQD2018 and MVAQD databases, demonstrate that the proposed SSP-BOIQA method outperforms the state-of-the-art blind quality assessment methods, and is more consistent with human visual perception.

Pathogenic Pathways in Early-Onset Autosomal Recessive Parkinson's Disease Discovered Using Isogenic Human Dopaminergic Neurons.

SummaryParkinson's disease (PD) is a complex and highly variable neurodegenerative disease. Familial PD is caused by mutations in several genes with diverse and mostly unknown functions. It is unclear how dysregulation of these genes results in the relatively selective death of nigral dopaminergic neurons (DNs). To address this question, we modeled PD by knocking out the PD genes PARKIN (PRKN), DJ-1 (PARK7), and ATP13A2 (PARK9) in independent isogenic human pluripotent stem cell (hPSC) lines. We found increased levels of oxidative stress in all PD lines. Increased death of DNs upon differentiation was found only in the PARKIN knockout line. Using quantitative proteomics, we observed dysregulation of mitochondrial and lysosomal function in all of the lines, as well as common and distinct molecular defects caused by the different PD genes. Our results suggest that precise delineation of PD subtypes will require evaluation of molecular and clinical data.