Fisheye Lens Research Articles

A Reflection on the Islamic Portrait of Jesus

When the name ‘Jesus’ is mentioned, one’s instant thought turns towards Christianity. Discourses on Jesus are made often with reference to Christendom. No doubt, Christianity has volumes to say about God and man; crucial claims are made about the meanings of life and the significance of death. The distinctive nature isn’t only about these crucial insights, but the process in which they were known. The Christian faith is based on the personhood of Jesus Christ. This is because the Christian understanding of God and man arises from the Christian understanding of the identity of Jesus Christ as a God-man. There have been several attempts by scholars to study the person and importance of Jesus. Nevertheless, a Christian searches the Bible for answers. This article takes a wide angle lens in the sense that its periscope includes the Quranic account of Jesus with the aim of ascertaining Jesus’ identity. Jesus is confirmed by both religion and non-religious historians to have lived historically. Christians adhere strictly to the messianic role and other significant theological events like the cross which upholds the Christian faith, but, Islam presents Jesus as a man (though with high honour and relevance) who lived specially as a prophetic bridge leading to Prophet Muhammad. Therefore, Islam makes it extremely necessary to emphasize and re-emphasize the fact that Jesus Christ, the Messiah did not come merely to teach and heal, but to redeem people from their sins.

Global Convolutional Neural Networks With Self-Attention for Fisheye Image Rectification

Fisheye images are attracting attention in computer vision such as autonomous vehicles and virtual reality because of the wide field of view (WFoV). However, fisheye images have geometric distortions caused by the refractive index of the lens. Conventional fisheye rectification methods require multiple images to calculate distortion coefficients and lens intrinsic parameters. This means that if the fisheye lens is changed, the same operation will have to be repeated. On the other hand, by using deep learning, images with different distortion coefficients can be rectified. Also, with end-to-end learning, no feature engineering is required. To improve the performance of fisheye image rectification, we propose global convolutional neural networks with self-attention to rectify the fisheye images. The proposed method employs dilated convolutional neural networks (D-CNNs) to enlarge receptive fields, and self-attention to extract the most important features of input images. In this way, the proposed method can extract global features from input images. To better train and evaluate the proposed method, we generate fisheye images from the Place2 dataset with Cartesian and polar coordinates, and label them with original images (ground-truth). we also schedule the learning rate with cosine annealing and use an integrated loss function. The experimental results show that the proposed method achieves an excellent performance in both qualitative and quantitative evaluations.

Применение виртуальных панорам в цифровом тематическом картографировании

The article discusses the development of thematic mapping in terms of creating virtual panoramas, as an element of a modern digital thematic map. The features of mapping using virtual panoramas are described, the advantages and disadvantages of this method of displaying geospace are given. Based on the experimental work, the authors propose a general technological scheme for creating a thematic map with elements of a virtual panoramic tour. All technological stages are considered in detail: the creation of a digital cartographic basis, planning of field work, photography, image processing, for-mation of panoramic images, creation of a panoramic tour and its publication. The use of various shooting equipment (smartphone, camera with standard and fisheye lens, specialized camera for pano-ramic shooting) is analyzed in terms of the speed, quality and cost of post-processing images. Frag-ments of virtual tours created in the course of the study are shown.

Generation of Panoramic Images by Two Hemispherical Cameras Independent of Installation Location

Single-shot capture of panoramic images has become possible in recent years, expanding applications for omnidirectional cameras. Retrofitting omnidirectional cameras onto existing objects can be problematic, however, because placing lenses on either side can cause the mounting structure to be included in captured images. To address this issue, we propose using two hemispherical cameras equipped with 185º field-of-view fisheye lenses and combining the images from each while compensating for differences in separation and height to produce a panoramic image like that from an omnidirectional camera. In other words, we investigate acquisition of omnidirectional panoramic images from two hemispherical cameras installed at some distance from each other. In this article, we propose a method for generating spherical images by stitching together images from two cameras. Specifically, we prepare a circular object, include it in images from two separated hemispherical cameras, detect the circular object in each image, and match the two detected circles. We also describe multiple experiments confirming the utility and effectiveness of the proposed method. This work is expected to contribute to expanding the range of applications for omnidirectional imaging.

Millimeter-Wave Retro-Directive Frequency Coded Lens by Curved One-Dimensional Photonic Crystal Resonator

Innovating passive and chipless coded landmarks have recently emerged for high accuracy self-localization systems. Existing landmarks make use of the combination of retro-directive devices, corner reflectors and lenses, with a coding particle in order to give a high RCS response over a wide angle. In this paper, with a consideration of important practical parameters unappreciated in existing designs, we propose a wide-angle retro-directive frequency-coded lens by a curved one-dimensional Photonic Crystal (PhC) resonator. The proposed frequency-coded lens is made of two parts: a homogenous lens and a curved PhC resonator where the resonator is located along the lens focal line. A frequency coding is used, where the presence or absence of a notch frequency in a specified information channel encodes an information bit. A PhC resonator provides unique advantages over existing coding particles due to its continuity along the lens focal line which creates a stable ID appearance over wide-angle. In addition, the potential of coding in its volume, rather than on the surface, entitles to a high coding capacity. Two frequency-coded lenses with single and dual defect resonators are EM simulated, fabricated, and experimentally validated in the W-band (75 GHz-110 GHz). Simulated results show that a wide detection angle of 170° can be achieved where the tag ID is maintained over all angles. A wide retro-directivity of 80° and 60° is experimentally demonstrated for frequency-coded lenses by a single defect (single notch) and a dual defect (double notch) PhC resonator, respectively.

2Pb6-3 Design and Focusing Characteristic of Wide-angle and Thin Acoustic lens

2Pb6-3 Design and Focusing Characteristic of Wide-angle and Thin Acoustic lens

Square Maxwell’s fish-eye lens for near-field broadband achromatic super-resolution imaging

Square Maxwell’s fish-eye lens for near-field broadband achromatic super-resolution imaging

SCARA Self Posture Recognition Using a Monocular Camera

Robotic manipulators rely on feedback obtained from rotary encoders for control purposes. This article introduces a vision-based feedback system that can be used in an agricultural context, where the shapes and sizes of fruits are uncertain. We aim to mimic a human, using vision and touch as manipulator control feedback. This work explores the use of a fish-eye lens camera to track a SCARA manipulator with coloured markers on its joints for the position estimation with the goal to reduce costs and increase reliability. The Kalman Filter and the Particle Filter are compared and evaluated in terms of accuracy and tracking abilities of the marker’s positions. The estimated image coordinates of the markers are converted to world coordinates using planar homography, as the SCARA manipulator has co-planar joints and the coloured markers share the same plane. Three laboratory experiments were conducted to evaluate the system’s performance in joint angle estimation of a manipulator. The obtained results are promising, for future cost effective agricultural robotic arms developments. Besides, this work presents solutions and future directions to increase the joint position estimation accuracy.

Hemispherical Photographs: A Review of Acquisition Methods and Applications in the Context of Urban Energy and Environment Assessments

Abstract From the use of a pinhole camera placed under a water tank, which was proposed almost 100 years ago, to the application of modern digital cameras mounted with sophisticated fisheye lenses, acquisition methods for capturing hemispherical photographs undergone vigorous research and development. Over the past few decades, such photographs have been extensively used in evaluating energy and environmental aspects in urban contexts. In this review article, the advantages, limitations, and challenges of the various methods of acquiring photographs are described and compared. This involves both the devices themselves and the software tools. Several methods of direct acquisition of hemispheric photographs involving digital cameras, smartphones, the use of drones for photographs at elevations, and the application of thermal imaging technologies are discussed in detail. Indirect methods for generating hemispheric photographs are also discussed, highlighting the use of images from applications such as Google Street View (GSV). Based on a review of technical literature, several applications in energy and the environment that use information from hemispheric photographs as an analysis tool are presented and discussed. Among others, the following are discussed: the quantification of solar radiation potential; the assessment of indicators of local temperature and level of thermal comfort for pedestrians in urban areas; indoor and outdoor daylighting; and air and light pollution. Finally, several potential future research directions for the use of hemispherical photographs in built environments are discussed. These include advances in image processing, use of thermal imaging, solar potential assessment of solar-powered vehicles, applications of drone-mounted hemispherical photography, and fisheye videos.

Experimental Determination of the Duration of Insolation Usinga Wide-Angle Lens and Solar Maps with Equidistant Projection

Experimental Determination of the Duration of Insolation Usinga Wide-Angle Lens and Solar Maps with Equidistant Projection

CALIBRATION OF SMARTPHONE’S REAR DUAL CAMERA SYSTEM

This paper aims to calibrate smartphone’s rear dual camera system which is composed of two lenses, namely; wide-angle lens and telephoto lens. The proposed approach handles large sized images. Calibration was done by capturing 13 photos for a chessboard pattern from different exposure positions. First, photos were captured in dual camera mode. Then, for both wide-angle and telephoto lenses, image coordinates for node points of the chessboard were extracted. Afterwards, intrinsic, extrinsic, and lens distortion parameters for each lens were calculated. In order to enhance the accuracy of the calibration model, a constrained least-squares solution was applied. The applied constraint was that the relative extrinsic parameters of both wide-angle and telephoto lenses were set as constant regardless of the exposure position. Moreover, photos were rectified in order to eliminate the effect of lens distortion. For results evaluation, two oriented photos were chosen to perform a stereo-pair intersection. Then, the node points of the chessboard pattern were used as check points.

A Mini-Survey and Feasibility Study of Deep-Learning-Based Human Activity Recognition from Slight Feature Signals Obtained Using Privacy-Aware Environmental Sensors

Numerous methods and applications have been proposed in human activity recognition (HAR). This paper presents a mini-survey of recent HAR studies and our originally developed benchmark datasets of two types using environmental sensors. For the first dataset, we specifically examine human pose estimation and slight motion recognition related to activities of daily living (ADL). Our proposed method employs OpenPose. It describes feature vectors without effects of objects or scene features, but with a convolutional neural network (CNN) with the VGG-16 backbone, which recognizes behavior patterns after classifying the obtained images into learning and verification subsets. The first dataset comprises time-series panoramic images obtained using a fisheye lens monocular camera with a wide field of view. We attempted to recognize five behavior patterns: eating, reading, operating a smartphone, operating a laptop computer, and sitting. Even when using panoramic images including distortions, results demonstrate the capability of recognizing properties and characteristics of slight motions and pose-based behavioral patterns. The second dataset was obtained using five environmental sensors: a thermopile sensor, a CO2 sensor, and air pressure, humidity, and temperature sensors. Our proposed sensor system obviates the need for constraint; it also preserves each subject’s privacy. Using a long short-term memory (LSTM) network combined with CNN, which is a deep-learning model dealing with time-series features, we recognized eight behavior patterns: eating, operating a laptop computer, operating a smartphone, playing a game, reading, exiting, taking a nap, and sitting. The recognition accuracy for the second dataset was lower than for the first dataset consisting of images, but we demonstrated recognition of behavior patterns from time-series of weak sensor signals. The recognition results for the first dataset, after accuracy evaluation, can be reused for automatically annotated labels applied to the second dataset. Our proposed method actualizes semi-automatic annotation, false recognized category detection, and sensor calibration. Feasibility study results show the new possibility of HAR used for ADL based on unique sensors of two types.

ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual–Inertial, and Multimap SLAM

This paper presents ORB-SLAM3, the first system able to perform visual, visual-inertial and multi-map SLAM with monocular, stereo and RGB-D cameras, using pin-hole and fisheye lens models. The first main novelty is a feature-based tightly-integrated visual-inertial SLAM system that fully relies on Maximum-a-Posteriori (MAP) estimation, even during the IMU initialization phase. The result is a system that operates robustly in real-time, in small and large, indoor and outdoor environments, and is 2 to 5 times more accurate than previous approaches. The second main novelty is a multiple map system that relies on a new place recognition method with improved recall. Thanks to it, ORB-SLAM3 is able to survive to long periods of poor visual information: when it gets lost, it starts a new map that will be seamlessly merged with previous maps when revisiting mapped areas. Compared with visual odometry systems that only use information from the last few seconds, ORB-SLAM3 is the first system able to reuse in all the algorithm stages all previous information. This allows to include in bundle adjustment co-visible keyframes, that provide high parallax observations boosting accuracy, even if they are widely separated in time or if they come from a previous mapping session. Our experiments show that, in all sensor configurations, ORB-SLAM3 is as robust as the best systems available in the literature, and significantly more accurate. Notably, our stereo-inertial SLAM achieves an average accuracy of 3.6 cm on the EuRoC drone and 9 mm under quick hand-held motions in the room of TUM-VI dataset, a setting representative of AR/VR scenarios. For the benefit of the community we make public the source code.

Design and analysis of Maxwell fisheye lens based beamformer

Antenna arrays and multi-antenna systems are essential in beyond 5G wireless networks for providing wireless connectivity, especially in the context of Internet-of-Everything. To facilitate this requirement, beamforming technology is emerging as a key enabling solution for adaptive on-demand wireless coverage. Despite digital beamforming being the primary choice for adaptive wireless coverage, a set of applications rely on pure analogue beamforming approaches, e.g., in point-to-multi point and physical-layer secure communication links. In this work, we present a novel scalable analogue beamforming hardware architecture that is capable of adaptive 2.5-dimensional beam steering and beam shaping to fulfil the coverage requirements. Beamformer hardware comprises of a finite size Maxwell fisheye lens used as a scalable feed network solution for a semi-circular array of monopole antennas. This unique hardware architecture enables a flexibility of using 2 to 8 antenna elements. Beamformer development stages are presented while experimental beam steering and beam shaping results show good agreement with the estimated performance.

Magnetically tunable Maxwell fisheye lens for spin waves focusing

In this paper, a Maxwell fisheye lens (MFL) for spin waves (SWs) focusing on a ferromagnetic film was presented via micromagnetic simulation. The Maxwell fisheye lens with graded refractive index distribution, achieved with gradient saturation magnetization, is designed to manipulate the wavefront of spin waves. The simulation results show that sub-wavelength focusing can be achieved by means of MFL with appropriate chosen parameters. Furthermore, simulation results also prove that the focusing properties of MFL can be tuned by external magnetic field. The MFL would be promising device for SWs focusing and wavefront manipulation.

2-D Deformed Half Fisheye Lens as Beamforming Network to Excite Planar Arrays of Antennas for Multibeam Radiations

Multibeam (MB) radiations of antenna arrays rely on effective beamforming networks (BFNs). A 2-D fisheye lens-based BFN (FL-BFN) was developed using a half 2-D lens realizable on metal parallel-plate sandwiched substrates to excite a planar antenna array. This FL-BFN implemented the beam and antenna excitation ports on its half-circular circumference and straight boundaries. An optimization scheme was developed to minimize the ray propagation distortion due to an incomplete half-lens propagation path for an offset beam, which results in an aperiodic array of antennas for MB radiations. Excitations for periodic arrays can also be implemented by considering an aperiodic output port establishment. The rule to determine the positions of antenna elements is also shown for easy design. This FL-BFN performance is demonstrated using its output amplitudes and phases to excite a Vivaldi antenna array for MB radiation. Numerical simulations and measurements over a prototype were performed to validate its feasibility with distinguished agreements.

Image-tag-based indoor localization using end-to-end learning

Image or feature matching-based indoor localization still faces many technical challenges. Image-tag-based schemes using pose estimation are accurate and robust, but they still cannot be deployed widely because their performance degrades significantly when the tag-camera distance is large, which requires densely distributed tags, and the designed system generally is specific to some special tags and lenses. Also, the lens distortion degrades the performance appreciably and is difficult to correct, especially for the wide-angle lenses. This article develops an image-tag-based indoor localization system using end-to-end learning to overcome these issues. It is a deep learning–based system that can learn the mapping from the original tag image to the final 2D location directly from training examples through self-learned features. It achieves consistent performance even when the tag-camera distance is large or when the image has a low resolution. The mapping learned by the deep learning model factors in all kinds of distortions without requiring any distortion estimation. The tag design is based on shape features to make it robust to lighting changes. The system can be easily adapted to new lenses/cameras and/or new tags. Thus, it facilitates easy and rapid deployment without requiring knowledge from domain experts. A drawback of the general deep learning model is its high computational requirements. We discuss practical solutions to enable real-time applications of the proposed scheme even when it is running on a mobile or embedded device. The performance of the proposed scheme is evaluated via a set of experiments in a real setting and has achieved less than 20 cm of positioning errors.

Modelling extreme wide‐angle lens cameras

AbstractThe use of consumer cameras fitted with extreme wide‐angle (EWA) lenses for photogrammetric measurement is increasing. Conventional modelling of EWA systems relies on the pinhole camera model and up to five radial lens distortion terms. Aiming to reduce model complexity, this paper reports on an investigation into an alternate approach using fisheye lens models for EWA systems, despite them not falling strictly into the fisheye category. Four fisheye models were tested on four different cameras under laboratory conditions. The self‐calibration results show superior model fit for all fisheye models over the pinhole‐plus‐radial model in terms of residual RMS. The number of radial distortion terms required for the fisheye models was lower in all cases, so model complexity was reduced. Independent assessment revealed very similar 3D reconstruction accuracy for all models. The results suggest that fisheye modelling is an advantageous alternative for EWA lens systems.

Amphibian and fish eye lens used as biomarker of remote and chronic environmental contamination

The lens of vertebrates has a general structural plan and reacts similarly to various factors of anthropogenic impact. Rainbow trout (Oncorhynchus mykiss) and grass frog (Rana temporaria L.) are the most well-studied objects of aquatic toxicology in Russia, which react to the level of exposure to toxicants in the urbanized environment and retain the consequences of this exposure. The study aims to determine the possibility of using the lens of fish and amphibians as a biomarker of chronic and long-term background environmental contamination. The fundamental importance of the work lies in the fact that for the first time the mechanisms of the response of the epithelial cells of the eye lens and lens fibers to the spectrum of various toxicants entering the environment with industrial effluents are considered. In chronic experiments, the sensitivity of the lens of Oncorhynchus mykiss fingerlings to three toxicants (dibutyl sebacate-DBS, polyterpenes-PTP, and 3-aminopropyltriethoxysilane-AGM-9) in concentrations close to the normalized valuesin the environment was studied. On adult individuals of Rana temporaria, the appearance of abnormities in the lens was studied over a long period, depending on the total environmental contamination. To assess remote environmental contamination, studies were carried out on the constantly growing lens of the grass frog, which can preserve the resulting fiber abnormities throughout its life in the form of local opacities from the harmful environmental factors impacts. On the epithelium of the lens of fish, it was found that under the influence of certain pollutants, the mitotic index (MI) in the germinal area can change over a wide range. With the combined effect of toxicants, inhibition of mitosis is also observed. Using biomicroscopy of the eye, it was possible to show that frogs from the urbanized zone of Moscow have 1.5 times more genotypic and modification changes in the lens than their relatives taken from an ecologically clean zone of the Moscow region, Mozhaisky district in the water area of the Ozerninskoye reservoir. For the first time, the sensitivity of the lens of aquatic organisms to chronic effects under experimental and background contamination of the terrestrial and aquatic environment has been shown. Therefore, taking into account the uniformity of the structure of the eye lens of fish and amphibians, as well as in other vertebrates, the lens can be used as a biomarker for assessing chronic and remote environmental contamination. The research results can be applied for a comprehensive assessment of the action of anthropogenic factors.

2-D Beam-Steerable Generalized Mikaelian Lens With Unique Flat-Shape Characteristic

A novel two-dimensional (2-D) beam-steerable generalized Mikaelian lens is presented in this letter. The main difference of proposed lens with respect to conventional Luneburg, Maxwell fish-eye and Eaton lens is its unique flat-shape characteristic, which provides great advantages in designing the low-profile flat-shape lens antenna, avoiding using extremely complex conformal mapping methods. The all-dielectric low-cost lens prototype presenting desired graded permittivity profile is fabricated by 3-D printing. The measurement results at 10 GHz show 2-D beam-steering capabilities from −20° to 20° in both the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</i> -and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> -plane with around 18.9 dBi of realized gain with less than ∼1.2 dB variation and low-side lobe can be achieved, revealing great potentials for developing the low-profile and cost-effective lens antenna for microwave applications.