Performance Of Camera Research Articles

Enhancing the performance of light field camera by pattern projection

For industrial three-dimensional (3D)-measuring applications as well as for other applications such as driver assistance systems in vehicles, a high-quality reproduction in a depth map calculation is of paramount importance. Yet to date, light field cameras still exhibit severe problems in imaging homogeneous surfaces such as smooth metallic surfaces, plastics, or semiconductor materials. On those, the necessary contrast for determining the topography is not present or too weak. Here, we present an innovative approach to improve the performance of light field cameras (also called plenoptic cameras) for various applications in 3D measurement technology. More specifically, we show that this disadvantage can be avoided using structured lighting pattern and analyze various lighting patterns (with respect to geometry, size, regularity, etc.) used to improve the performance of the algorithm in greater detail. In addition, different ways of projecting the pattern have been examined with regard to their advantages and disadvantages. We perform a systematic investigation of the impact of structured light illumination with regard to different object materials, object geometries, and illumination patterns as well as illumination sources. These examinations further comprise the analysis of environmental influences and magnitude of measurement deviations in a light field camera measuring set-up. Summing up, we investigated experimentally the influence of structured illumination on the performance of 3D depth information without the need of additional phase information.

Handheld fundus camera performance, image quality and outcomes of diabetic retinopathy grading in a pilot screening study

To compare the performance and image quality of the handheld fundus camera to standard table-top fundus cameras in diabetic retinopathy (DR) screening. The reliability and diagnostic accuracy of DR grading performed by an ophthalmologist and a photographer reader were evaluated. 157 patients with diabetes, attending screening or follow-up of DR, were evaluated by fundus photographs taken in mydriasis by Optomed Aurora and Canon or Zeiss Visucam fundus cameras. The image quality and the severity of DR were evaluated independently by an ophthalmologist and experienced photographer. The sensitivity, specificity and reliability of the assessments were determined. 1884 fundus images from 314 eyes were analysed. In 53% of all eyes, DR was not present. 10% had mild non-proliferative diabetic retinopathy (NPDR), 16% moderate NPDR, 6% severe NPDR and 16% proliferative diabetic retinopathy (PDR). The DR grading outcomes by Aurora highly equalled to those of Canon or Zeiss (κ = 0.93, 95% CI 0.91 to 0.94), and there was almost perfect agreement in grading between the ophthalmologist and photographer (κ = 0.96, 95% CI 0.95 to 0.97). The image quality of Aurora was sufficient for reliable assessment according to both graders in 84-88% of the cases. The Optomed Aurora fundus camera seems appropriate for DR screening. The sufficient image quality and high diagnostic accuracy for DR grading are supportive for a less expensive and easily transportable screening system for DR. Immediate image grading carried out by a photographer would further improve and speed up the screening process in all settings.

A Method to Compensate for the Errors Caused by Temperature in Structured-Light 3D Cameras.

Although low cost red-green-blue-depth (RGB-D) cameras are factory calibrated, to meet the accuracy requirements needed in many industrial applications proper calibration strategies have to be applied. Generally, these strategies do not consider the effect of temperature on the camera measurements. The aim of this paper is to evaluate this effect considering an Orbbec Astra camera. To analyze this camera performance, an experimental study in a thermal chamber has been carried out. From this experiment, it has been seen that produced errors can be modeled as an hyperbolic paraboloid function. To compensate for this error, a two-step method that first computes the error and then corrects it has been proposed. To compute the error two possible strategies are proposed, one based on the infrared distortion map and the other on the depth map. The proposed method has been tested in an experimental scenario with different Orbbec Astra cameras and also in a real environment. In both cases, its good performance has been demonstrated. In addition, the method has been compared with the Kinect v1 achieving similar results. Therefore, the proposed method corrects the error due to temperature, is simple, requires a low computational cost and might be applicable to other similar cameras.

Lensless inference camera: incoherent object recognition through a thin mask with LBP map generation.

We propose a preliminary lensless inference camera (LLI camera) specialized for object recognition. The LLI camera performs computationally efficient data preprocessing on the optically encoded pattern through the mask, rather than performing computationally expensive image reconstruction before inference. Therefore, the LLI camera avoids expensive computation and achieves real-time inference. This work proposes a new data preprocessing approach, named local binary patterns map generation, dedicated for optically encoded pattern through the mask. This preprocessing approach greatly improves encoded pattern's robustness to local disturbances in the scene, making the LLI camera's practical application possible. The performance of the LLI camera is analyzed through optical experiments on handwritten digit recognition and gender estimation under conditions with changing illumination and a moving target.

Synergistic effect of functionalized ionic liquid and alkanolamines mixed solution on enhancing the mass transfer of CO2 absorption in microchannel

A novel hybrid absorbent by combining functionalized ionic liquid (IL) 1-(3-aminopropyl)-3-methylimidazolium tetrafluoroborate [Apmim][BF4] with alkanolamines (monoethanolamine (MEA) and N, N-diethylethanolamine (DEEA)) was proposed for CO2 capture. By means of a high-speed camera, the capacity and mass transfer performance of CO2 absorption into the hybrid absorbent were investigated experimentally in microchannel over a wide range of concentration ratios (IL: amine) at the room temperature and atmosphere pressure. Three flow patterns were observed: Taylor-bubbly flow, Taylor flow and Taylor-annular flow. The results show that the CO2 loading and mass transfer coefficient of alkanolamine solution could be improved remarkably by the addition of an appropriate amount of [Apmim][BF4]. The molar ratio 3:2 of IL to amine was found to be optimal for CO2 loading and mass transfer. Furthermore, the influences of gas and liquid flow rates on mass transfer were studied, and a new predicting correlation for CO2 volumetric mass transfer coefficient was proposed.

Gamma camera characterization at high holmium-166 activity in liver radioembolization

BackgroundHigh activities of holmium-166 (166Ho)–labeled microspheres are used for therapeutic radioembolization, ideally directly followed by SPECT imaging for dosimetry purposes. The resulting high-count rate potentially impacts dead time, affecting the image quality and dosimetric accuracy. This study assesses gamma camera performance and SPECT image quality at high 166Ho activities of several GBq. To this purpose, the liver compartment, including two tumors, of an anthropomorphic phantom was filled with 166Ho-chloride, with a tumor to non-tumorous liver activity concentration ratio of 10:1. Multiple SPECT/CT scans were acquired over a range of activities up to 2.7 GBq. Images were reconstructed using a commercially available protocol incorporating attenuation and scatter correction. Dead time effects were assessed from the observed count rate in the photopeak (81 keV, 15% width) and upper scatter (118 keV, 12% width) window. Post reconstruction, each image was scaled with an individual conversion factor to match the known total activity in the phantom at scanning time. The resulting activity concentration was measured in the tumors and non-tumorous liver. The image quality as a function of activity was assessed by a visual check of the absence of artifacts by a nuclear medicine physician. The apparent lung shunt fraction (nonzero due to scatter) was estimated on planar and SPECT images.ResultsA 20% count loss due to dead time was observed around 0.7 GBq in the photopeak window. Independent of the count losses, the measured activity concentration was up to 100% of the real value for non-tumorous liver, when reconstructions were normalized to the known activity at scanning time. However, for tumor spheres, activity concentration recovery was ~80% at the lowest activity, decreasing with increasing activity in the phantom. Measured lung shunt fractions were relatively constant over the considered activity range.ConclusionsAt high 166Ho count rate, all images, visually assessed, presented no artifacts, even at considerable dead time losses. A quantitative evaluation revealed the possibility of reliable dosimetry within the healthy liver, as long as a post-reconstruction scaling to scanning activity is applied. Reliable tumor dosimetry, instead, remained hampered by the dead time.

스마트폰 카메라의 피부색 재현과 선호에 관한 연구

As the performance of smartphone cameras improves, the role of the smartphone image processor has become important. Smartphone camera users want to reproduce the color they prefer according to their intended use rather than using the original color of the photo as it is. Smartphone manufacturers release products by applying image processing settings appropriate to each situation to smartphone cameras in order to meet these consumer needs. Objective targets were photographed to confirm the reproduction of skin color by type of smartphone camera. Color reproduction was confirmed through the color difference with the Lab value of the standard target that was taken. And, in order to check the skin color preference according to the model of the smartphone camera and the shooting mode, male and female portraits were photographed under the same conditions, and the preference of portrait photography was surveyed. As a result of the study, all smartphones showed less difference in color reproduction in portrait mode than in normal mode. It can be seen that it reproduces close to the standard color in portrait mode. It was found that this was a reproduction considering the low tolerance for skin color. In addition, the preference for portrait photography was different according to the sex of the subject and the sex of the subject, which is the experimental stimulus. This study confirmed users

Characterizing and correcting camera noise in back-illuminated sCMOS cameras.

With promising properties of fast imaging speed, large field-of-view, relative low cost and many others, back-illuminated sCMOS cameras have been receiving intensive attention for low light level imaging in the past several years. However, due to the pixel-to-pixel difference of camera noise (called noise non-uniformity) in sCMOS cameras, researchers may hesitate to use them in some application fields, and sometimes wonder whether they should optimize the noise non-uniformity of their sCMOS cameras before using them in a specific application scenario. In this paper, we systematically characterize the impact of different types of sCMOS noise on image quality and perform corrections to these types of sCMOS noise using three representative algorithms (PURE, NCS and MLEsCMOS). We verify that it is possible to use appropriate correction methods to push the non-uniformity of major types of camera noise, including readout noise, offset, and photon response, to a satisfactory level for conventional microscopy and single molecule localization microscopy. We further find out that, after these corrections, global read noise becomes a major concern that limits the imaging performance of back-illuminated sCMOS cameras. We believe this study provides new insights into the understanding of camera noise in back-illuminated sCMOS cameras, and also provides useful information for future development of this promising camera technology.

Design and validation of an inertial measurement unit (IMU)-based sensor for capturing camera movement in the operating room

Intraoperative surgical video enables better surgical training, continued performance enhancement for surgeons and system-level quality improvement initiatives, however the capture of high-quality intraoperative video of open surgical procedures is difficult. Wearable cameras, typically in the form of a head-mounted action camera are frequently used for this purpose, although the video from these devices often contains significant motion artifact due to movement of the surgeon’s head. When trying to compare the performance of various wearable cameras in the surgical setting, we could not find a motion sensor appropriate for this purpose. We therefore describe in this article the design, assembly and validation of a small sensor that can be attached to wearable cameras in the operating room to objectively quantify camera motion. The sensor incorporates an inertial measurement unit coupled to a microcontroller. Concurrent validity is established by comparing the positional sensing of the device to a geared tripod head that allows for fine, measured manipulations of the sensor in three orthogonal axes. The methodology of capturing, processing and reporting camera movement for a surgical procedure is also detailed.

Impact of microvibration on the optical performance of an airborne camera.

Aiming to investigate the connection between camera structure and optical systems, a comprehensive analysis needs to be performed for the airborne camera. An integrated analysis method was proposed to design and analyze optical and mechanical structures. Based on the designed small airborne camera, the impact of microvibration on the optical performance of the airborne camera was studied by integrated optomechanical analysis. In addition, the change of optical surface accuracy was analyzed. First, static and dynamic analysis of the designed airborne camera was performed to verify the stability of the camera structure and obtain the data for integrated optomechanical analysis. Then, a calculation method for rigid body displacement was proposed, and the impact of rigid body displacements on the optical system was analyzed. To evaluate the change of surface accuracy, the parameters root mean square (RMS) and peak to valley (PV) were calculated by fitting the surface distortion data. Based on the Zernike polynomial coefficients, the response of the optical system was calculated and analyzed utilizing ZEMAX to analyze the impact of microvibration on the optical performance of the airborne camera. The analysis results show that microvibration has no significant impact on optical performance of the designed small airborne camera. Finally, the analysis results were verified through experiments.

Laboratory quick near edge x-ray absorption fine structure spectroscopy in the soft x-ray range with 100Hz frame rate using CMOS technology.

In laboratory based x-ray absorption fine structure (XAFS) spectroscopy, the slow readout speed of conventional CCD cameras can prolong the measuring times by multiple orders of magnitude. Using pulsed sources, e.g., laser-based x-ray sources, the pulse repetition rate often exceeds the frame rate of the CCD camera. We report the use of a scientific CMOS (sCMOS) camera for XAFS spectroscopy with a laser-produced plasma source facilitating measurements at 100Hz. With this technological improvement, a new class of experiments becomes possible, starting from the time consuming analysis of samples with small absorption to pump-probe investigations. Furthermore, laboratory quick soft x-ray absorption fine structure (QXAFS) measurements with 10ms time resolution are rendered feasible. We present the characterization of the sCMOS camera concerning noise characteristics and a comparison to conventional CCD camera performance. The feasibility of time resolved QXAFS measurements is shown by analyzing the statistical uncertainty of single shot spectra. Finally, XAFS spectroscopy on a complex sandwich structure with minute amounts of NiO exemplifies the additional merits of fast detectors.

Radiometry and Photometry for Autonomous Vehicles and Machines - Fundamental Performance Limits

As autonomous vehicles and machines, such as self-driving cars, agricultural drones and industrial robots, become ubiquitous, there is an increasing need to understand the objective performance of cameras to support these functions. Images go beyond aesthetic and subjective roles as they assume increasing aspects of control, safety, and diagnostic capabilities. Radiometry and photometry are fundamental to describing the behavior of light and modeling the signal chain for imaging systems, and as such, are crucial for establishing objective behavior. As an engineer or scientist, having an intuitive feel for the magnitude of units and the physical behavior of components or systems in any field improves development capabilities and guards against rudimentary errors. Back-of-the-envelope estimations provide comparisons against which detailed calculations may be tested and will urge a developer to “try again” if the order of magnitude is off for example. They also provide a quick check for the feasibility of ideas, a “giggle” or “straight-face” test as it is sometimes known. This paper is a response to the observation of the authors that, amongst participants that are newly relying on the imaging field and existing image scientists alike, there is a general deficit of intuition around the units and order of magnitude of signals in typical cameras for autonomous vehicles and the conditions within which they operate. Further, there persists a number of misconceptions regarding general radiometric and photometric behavior. Confusion between the inverse square law as applied to illumination and consistency of image luminance versus distance is a common example. The authors detail radiometric and photometric model for an imaging system, using it to clarify vocabulary, units and behaviors. The model is then used to estimate the number of quanta expected in pixels for typical imaging systems for each of the patches of a MacBeth color checker under a wide variety of illumination conditions. These results form the basis to establish the fundamental limits of performance for passive camera systems based both solely on camera geometry and additionally considering typical quantum efficiencies available presently. Further a mental model is given which will quickly allow user to estimate numbers of photoelectrons in pixel.

VCX Version 2020 - Further development of a transparent and objective evaluation scheme for mobile phone cameras

VCX or Valued Camera eXperience is a nonprofit organization dedicated to the objective and transparent evaluation of mobile phone cameras. The members continuously work on the development of a test scheme that can provide an objective score for the camera performance. Every device is tested for a variety of image quality factors while these typically based on existing standards. Tests include texture loss, resolution, low light performance, shooting time lag, image stabilisation performance and more, all for a variety of different capture conditions. This paper presents that latest development with the newly released version 2020 and the process behind it.

Diabetic Retinopathy Screening Using Artificial Intelligence and Handheld Smartphone-Based Retinal Camera.

Portable retinal cameras and deep learning (DL) algorithms are novel tools adopted by diabetic retinopathy (DR) screening programs. Our objective is to evaluate the diagnostic accuracy of a DL algorithm and the performance of portable handheld retinal cameras in the detection of DR in a large and heterogenous type 2 diabetes population in a real-world, high burden setting. Participants underwent fundus photographs of both eyes with a portable retinal camera (Phelcom Eyer). Classification of DR was performed by human reading and a DL algorithm (PhelcomNet), consisting of a convolutional neural network trained on a dataset of fundus images captured exclusively with the portable device; both methods were compared. We calculated the area under the curve (AUC), sensitivity, and specificity for more than mild DR. A total of 824 individuals with type 2 diabetes were enrolled at Itabuna Diabetes Campaign, a subset of 679 (82.4%) of whom could be fully assessed. The algorithm sensitivity/specificity was 97.8 % (95% CI 96.7-98.9)/61.4 % (95% CI 57.7-65.1); AUC was 0·89. All false negative cases were classified as moderate non-proliferative diabetic retinopathy (NPDR) by human grading. The DL algorithm reached a good diagnostic accuracy for more than mild DR in a real-world, high burden setting. The performance of the handheld portable retinal camera was adequate, with over 80% of individuals presenting with images of sufficient quality. Portable devices and artificial intelligence tools may increase coverage of DR screening programs.

Deep High-Resolution Representation Learning for Cross-Resolution Person Re-Identification.

Person re-identification (re-ID) tackles the problem of matching person images with the same identity from different cameras. In practical applications, due to the differences in camera performance and distance between cameras and persons of interest, captured person images usually have various resolutions. This problem, named Cross-Resolution Person Re-identification, presents a great challenge for the accurate person matching. In this paper, we propose a Deep High-Resolution Pseudo-Siamese Framework (PS-HRNet) to solve the above problem. Specifically, we first improve the VDSR by introducing existing channel attention (CA) mechanism and harvest a new module, i.e., VDSR-CA, to restore the resolution of low-resolution images and make full use of the different channel information of feature maps. Then we reform the HRNet by designing a novel representation head, HRNet-ReID, to extract discriminating features. In addition, a pseudo-siamese framework is developed to reduce the difference of feature distributions between low-resolution images and high-resolution images. The experimental results on five cross-resolution person datasets verify the effectiveness of our proposed approach. Compared with the state-of-the-art methods, the proposed PS-HRNet improves the Rank-1 accuracy by 3.4%, 6.2%, 2.5%,1.1% and 4.2% on MLR-Market-1501, MLR-CUHK03, MLR-VIPeR, MLR-DukeMTMC-reID, and CAVIAR datasets, respectively, which demonstrates the superiority of our method in handling the Cross-Resolution Person Re-ID task. Our code is available at https://github.com/zhguoqing.

A prototype of SiPM-based scintillator Compton camera with capacitive multiplexing readout

Compton camera is capable of visualizing the distribution of radioactivity based on the Compton scattering kinematics, and can be applied in nuclear accident emergency response, radioactivity decontamination, homeland security and etc. A prototype of Compton camera has been constructed consisting of two 4×4 SiPM-Ce: GAGG scintillator arrays. The signals of a SiPM array were converted into 4 position-weighted outputs with capacitive network circuit and collected by a CAEN digitizer. Two-dimensional flood images and energy responses of two detector modules were obtained. The average energy resolution of single elements of the two detector modules was 6.6% FWHM at 662 keV. Imaging performance of the prototype camera was evaluated with a 137Cs point source and a 22Na point source. Efficiency of the prototype for the 137Cs point source located right in front was measured to be 0.09%. The angular resolution of a 137Cs point source placed right in front of the camera was 7.2° FWHM. Isotopic images of the two gamma-ray sources were obtained with spatial resolutions of 8.2° FWHM for 137Cs and 8.8° FWHM for 22Na.

Event-Stream Representation for Human Gaits Identification Using Deep Neural Networks.

Dynamic vision sensors (event cameras) have recently been introduced to solve a number of different vision tasks such as object recognition, activities recognition, tracking, etc. Compared with the traditional RGB sensors, the event cameras have many unique advantages such as ultra low resources consumption, high temporal resolution and much larger dynamic range. However, these cameras only produce noisy and asynchronous events of intensity changes, i.e., event-streams rather than frames, where conventional computer vision algorithms can't be directly applied. In our opinion the key challenge for improving the performance of event cameras in vision tasks is finding the appropriate representations of the event-streams so that cutting-edge learning approaches can be applied to fully uncover the spatio-temporal information contained in the event-streams. In this paper, we focus on the event-based human gait identification task and investigate the possible representations of the event-streams when deep neural networks are applied as the classifier. We propose new event-based gait recognition approaches basing on two different representations of the event-stream, i.e., graph and image-like representations, and use graph-based convolutional network (GCN) and convolutional neural networks (CNN) respectively to recognize gait from the event-streams. The two approaches are termed as EV-Gait-3DGraph and EV-Gait-IMG. To evaluate the performance of the proposed approaches, we collect two event-based gait datasets, one from real-world experiments and the other by converting the publicly available RGB gait recognition benchmark CASIA-B. Extensive experiments show that EV-Gait-3DGraph achieves significantly higher recognition accuracy than other competing methods when sufficient training samples are available. However, EV-Gait-IMG converges more quickly than graph-based approaches while training and shows good accuracy with only few number of training samples (less than ten). So image-like presentation is preferable when the amount of training data is limited.

Millimeter Wave MIMO-Based Depth Maps for Wireless Virtual and Augmented Reality

Augmented and virtual reality systems (AR/VR) are rapidly becoming key components of the wireless landscape. For immersive AR/VR experience, these devices should be able to construct accurate depth perception of the surrounding environment. Current AR/VR devices rely heavily on using RGB-D depth cameras to achieve this goal. The performance of these depth cameras, however, has clear limitations in several scenarios, such as the cases with shiny objects, dark surfaces, and abrupt color transition among other limitations. In this paper, we propose a novel solution for AR/VR depth map construction using mmWave MIMO communication transceivers. This is motivated by the deployment of advanced mmWave communication systems in future AR/VR devices for meeting the high data rate demands and by the interesting propagation characteristics of mmWave signals. Accounting for the constraints on these systems, we develop a comprehensive framework for constructing accurate and high-resolution depth maps using mmWave systems. In this framework, we developed new sensing beamforming codebook approaches that are specific for the depth map construction objective. Using these codebooks, and leveraging tools from successive interference cancellation, we develop a joint beam processing approach that can construct high-resolution depth maps using practical mmWave antenna arrays. Extensive simulation results highlight the potential of the proposed solution in building accurate depth maps. Further, these simulations show the promising gains of mmWave based depth perception compared to RGB-based approaches in several important use cases.

Animal detections increase by using a wide-angle camera trap model but not by periodically repositioning camera traps within study sites

When using camera traps for wildlife studies, determining suitable camera models and deployment methods is essential for achieving study objectives. We aimed to determine if camera trap performance can be increased by (1) using cameras with wider detection angles, and (2) by periodically repositioning cameras within sites. We compared three camera trap groups: stationary Reconyx PC900/HC600 (40° detection angle), and paired, periodically-repositioned Reconyx PC900/HC600 and Swift 3C wide-angle camera traps (110° detection angle). Cameras operated simultaneously at 17 sites over 9 weeks within the Upper Warren region, Western Australia. Swift cameras had significantly higher detection rates, leading to better performance, especially for species <1 kg and >10 kg bodyweight. Reconyx cameras missed 54% of known events, with most being animals that moved within the cameras’ detection zones. Stationary and periodically-repositioned Reconyx camera traps performed similarly, although there were notable differences for some species. The better performance of Swift 3C wide-angle camera traps makes them more useful for community-level and species-level studies. The increased sensitivity of the Swift’s passive infrared sensor along with the wider detection zone played an important role in its success. When choosing camera trap models, detection angle and sensor sensitivity should be considered to produce reliable study results. Periodically repositioning cameras within sites is a technique that warrants further investigation as it may reduce camera placement bias, animal avoidance of camera traps, and increase spatial/habitat information when a limited number of cameras are deployed.

Kernel Phase and Coronagraphy with Automatic Differentiation

Abstract The accumulation of aberrations along the optical path in a telescope produces distortions and speckles in the resulting images, limiting the performance of cameras at high angular resolution. It is important to achieve the highest possible sensitivity to faint sources, using both hardware and data analysis software. While analytic methods are efficient, real systems are better modeled numerically, but numerical models of complicated optical systems with many parameters can be hard to understand, optimize, and apply. Automatic differentiation or “backpropagation” software developed for machine-learning applications now makes calculating derivatives with respect to aberrations in arbitrary planes straightforward for any optical system. We apply this powerful new tool to the problem of high-angular-resolution astronomical imaging. Self-calibrating observables such as the “closure phase” or “bispectrum” have been widely used in optical and radio astronomy to mitigate optical aberrations and achieve high-fidelity imagery. Kernel phases are a generalization of closure phases valid in the limit of small phase errors. Using automatic differentiation, we reproduce existing kernel phase theory within this framework and demonstrate an extension to the case of a Lyot coronagraph, which is found to have self-calibrating combinations of speckles. which are resistant to phase noise, but only in the very high-wave-front-quality regime. As an illustrative example, we reanalyze Palomar adaptive optics observations of the binary α Ophiuchi, finding consistency between the new pipeline and the existing standard. We present a new Python package morphine that incorporates these ideas, with an interface similar to the popular package poppy, for optical simulation with automatic differentiation. These methods may be useful for designing improved astronomical optical systems by gradient descent.