Modern Cameras Research Articles

Compression for Bayer CFA Images: Review and Performance Comparison.

Bayer color filter array (CFA) images are captured by a single-chip image sensor covered with a Bayer CFA pattern which has been widely used in modern digital cameras. In the past two decades, many compression methods have been proposed to compress Bayer CFA images. These compression methods can be roughly divided into the compression-first-based (CF-based) scheme and the demosaicing-first-based (DF-based) scheme. However, in the literature, no review article for the two compression schemes and their compression performance is reported. In this article, the related CF-based and DF-based compression works are reviewed first. Then, the testing Bayer CFA images created from the Kodak, IMAX, screen content images, videos, and classical image datasets are compressed on the Joint Photographic Experts Group-2000 (JPEG-2000) and the newly released Versatile Video Coding (VVC) platform VTM-16.2. In terms of the commonly used objective quality, perceptual quality metrics, the perceptual effect, and the quality-bitrate tradeoff metric, the compression performance comparison of the CF-based compression methods, in particular the reversible color transform-based compression methods and the DF-based compression methods, is reported and discussed.

Cost-effective micro-CT system for non-destructive testing of titanium 3D printed medical components

Micro-CT imaging can be used as an effective method for non-destructive testing (NDT) of metal 3D printed parts-including titanium biomedical components fabricated using laser powder-bed-fusion (LPBF). Unfortunately, the cost of commercially available micro-CT scanners renders routine NDT for biomedical applications prohibitively expensive. This study describes the design, manufacturing, and implementation of a cost-effective scanner tailored for NDT of medium-size titanium 3D printed biomedical components. The main elements of the scanner; which include a low-energy (80 kVp) portable x-ray unit, and a low-cost lens-coupled detector; can be acquired with a budget less than $ 11000 USD. The low-cost detector system uses a rare-earth phosphor screen, lens-coupled to a dSLR camera (Nikon D800) in a front-lit tilted configuration. This strategy takes advantage of the improved light-sensitivity of modern full-frame CMOS camera sensors and minimizes source-to-detector distance to maximize x-ray flux. The imaging performance of the system is characterized using a comprehensive CT quality-assurance phantom, and two titanium 3D-printed test specimens. Results show that the cost-effective scanner can survey the porosity and cracks in titanium parts with thicknesses of up to 13 mm of solid metal. Quantitatively, the scanner produced geometrically stable reconstructions, with a voxel size of 118 μm, and noise levels under 55 HU. The cost-effective scanner was able to estimate the porosity of a 17 mm diameter titanium 3D-printed cylindrical lattice structure, with a 0.3% relative error. The proposed scanner will facilitate the implementation of titanium LPBF-printed components for biomedical applications by incorporating routine cost-effective NDT as part of the process control and validation steps of medical-device quality-management systems. By reducing the cost of the x-ray detector and shielding, the scan cost will be commensurate with the overall cost of the validated component.

Applying Modern Camera Technology to Guide the Visually Impaired Persons in Society: The Power of Improved Orientation and Mobility Skills for Visual Impairment

Applying Modern Camera Technology to Guide the Visually Impaired Persons in Society: The Power of Improved Orientation and Mobility Skills for Visual Impairment

Camera trapping with photos and videos: implications for ecology and citizen science

AbstractCamera traps are increasingly used in wildlife monitoring and citizen science to address an array of ecological questions on a wide variety of species. However, despite the ability of modern camera traps to capture high‐quality video, the majority of studies collect still images, in part because of concerns with video performance. We conducted a camera trap survey of a forested landscape in the UK, using a grid of paired camera traps, to quantify the impact of using video compared to photos on the outcomes of ecological research and for participation and engagement of citizen scientists. Ecological outputs showed no difference between photo and video datasets, but comparison between expert and citizen science classifications showed citizen scientists were able to classify videos more accurately (average accuracy of 95% for video, 86% for photo). Furthermore, citizen scientists were more likely to volunteer additional information on age (provided for 61% videos and 30% photos) and sex (provided for 63% videos and 45% photos) of animals in video footage. Concerns over slow trigger speeds for videos did not appear to affect our datasets or the inferences gained. When combined with citizen science, video datasets are likely to be of higher quality due to increased classification accuracy. Consequently, we encourage researchers to consider the use of video for future camera‐trapping projects.

Machine-Learning-Inspired Workflow for Camera Calibration.

The performance of modern digital cameras approaches physical limits and enables high-precision measurements in optical metrology and in computer vision. All camera-assisted geometrical measurements are fundamentally limited by the quality of camera calibration. Unfortunately, this procedure is often effectively considered a nuisance: calibration data are collected in a non-systematic way and lack quality specifications; imaging models are selected in an ad hoc fashion without proper justification; and calibration results are evaluated, interpreted, and reported inconsistently. We outline an (arguably more) systematic and metrologically sound approach to calibrating cameras and characterizing the calibration outcomes that is inspired by typical machine learning workflows and practical requirements of camera-based measurements. Combining standard calibration tools and the technique of active targets with phase-shifted cosine patterns, we demonstrate that the imaging geometry of a typical industrial camera can be characterized with sub-mm uncertainty up to distances of a few meters even with simple parametric models, while the quality of data and resulting parameters can be known and controlled at all stages.

Differential SfM and image correction for a rolling shutter stereo rig

• A simple and flexible generalized RS stereo differential SfM algorithm over two consecutive frames. • Two effective and effcient RS-stereo non-linear optimization techniques based on the maximum likelihood criterion. • An RS-stereo image correction method to remove the inaccuracies induced by the RS effect. • The proposed model and method are universal and tractable. Most modern consumer-grade cameras are equipped with an electronic rolling shutter (RS), leading to image distortions when the camera moves during image acquisition. We explore the first structure and motion estimation problem of a dynamic generalized RS stereo camera. Such a general configuration is commonplace in robots and autonomous driving applications. We propose a tractable RS stereo differential structure from motion (SfM) algorithm, taking into account the RS effect during consecutive imaging, which effectively compensates for the RS-stereo image distortion by a linear scaling operation on each optical flow. We further propose embedding the cheirality into RANSAC and develop a robust RS-stereo-aware full-motion estimation framework. We demonstrate that the RS stereo motion and depth map refined by our non-linear optimization schemes within the maximum likelihood criterion can be used for image correction to recover high-quality global shutter (GS) stereo images. Moreover, using the proposed generalized RS stereo differential SfM pipeline, the corrected images produce an accurate 3D scene structure as the ground-truth structure. Extensive experiments on both synthetic and real RS stereo data demonstrate the effectiveness of our model and method in various configurations.

Capturing multiple full-scene images with a single camera via aperture stop exploitation.

In an effort to increase the capability of modern camera systems, recent advances in imaging technology have seen the maturation of postprocessing and demosaicing algorithms, multispectral imagers, and scene-splitting techniques. Although highly enabling, each of these methods faces an inherent limitation imposed by the camera's geometry. By reevaluating the fundamental components of the camera, this study presents a new method and paradigm in capturing and processing scene information. The proposed camera design is validated and optimized using Zemax simulations. The results show that light entering a camera can be split into three independent, spatially separated, full-scene images, wherein each image retains all spectral, polarimetric, and relative intensity information of the original scene.

One-net: Convolutional color constancy simplified

• Proposed model achieves state-of-the-art results on tested datasets. • Proposed neural network is simple and suitable for real-time applications. • Simple image augmentation results in improved cross-camera illumination estimation. • Architecture with small kernal outperforms larger kernels in speed and accuracy. Images have an ever-increasing presence in our daily lives. This increases the need for accurate and efficient image processing . One of the first processing steps in modern cameras is image white-balancing, the process of making the image invariant to the illumination of the scene. This can be achieved by estimating the illumination of the scene, which is used to chromatically adapt the image. Many existing state-of-the-art approaches use pre-trained models as feature extractors. These models are pre-trained on ImageNet and usually have several million parameters. In this paper, we introduce a simple convolutional neural network without pre-trained layers, that achieves state-of-the-art results. The model contains five convolutional layers , and all of them have a small kernel of size (1,1). Experiments with different model complexities and different kernel sizes have shown that high-level semantic information obtained using larger kernels is not required to achieve state-of-the-art results. Cross camera experiments were also performed and they showed that simple image pre-processing can significantly decrease the effect of camera-sensor on the method. The proposed method has less than 22 000 parameters and achieves state-of-the-art results. The model was tested on three different datasets: the Cube+ dataset, the NUS-8 dataset, and the Intel-TAU dataset.

Multi-size drone detection using YOLOv5 network

With increasingly modern technology, advanced and flexible functions, compact design, and low cost, drones are recently used in many fields with different effective purposes. Unlike beneficial applications, hostile forces leverage drones to explore the terrain, carry illegal explosives, and so on. Those applications can seriously threaten national security and defense. To prevent illegal drones effectively, we apply deep neural networks to detect the illegal drones in a variety of conditions and different sizes of drones. Accordingly, a computer-based system using modern cameras combined with an algorithmic model can solve the complex drone detection problem. Therefore, an emerging complex neural network approach based on YOLOv5 is proposed in this paper. With the method, we achieve a very expected result (confidence of 0.993 @0.5IOU), which meets the requirements of the drone detection problem.

Compact light field photography towards versatile three-dimensional vision

Inspired by natural living systems, modern cameras can attain three-dimensional vision via multi-view geometry like compound eyes in flies, or time-of-flight sensing like echolocation in bats. However, high-speed, accurate three-dimensional sensing capable of scaling over an extensive distance range and coping well with severe occlusions remains challenging. Here, we report compact light field photography for acquiring large-scale light fields with simple optics and a small number of sensors in arbitrary formats ranging from two-dimensional area to single-point detectors, culminating in a dense multi-view measurement with orders of magnitude lower dataload. We demonstrated compact light field photography for efficient multi-view acquisition of time-of-flight signals to enable snapshot three-dimensional imaging with an extended depth range and through severe scene occlusions. Moreover, we show how compact light field photography can exploit curved and disconnected surfaces for real-time non-line-of-sight 3D vision. Compact light field photography will broadly benefit high-speed 3D imaging and open up new avenues in various disciplines.

Ray-tracking methods for characterizing the dynamics of curved detonation

Current analyses of curved detonations are mostly limited to the dynamics along the wall or the symmetry axis due to the lack of efficient approaches for reliably tracking stream tubes with curved shock fronts. To address this lack, the present work proposes a novel curved ray-tracking algorithm with two implementation methods. The curved ray is characterized by a specific arc of constant curvature perpendicularly intersecting both successive fronts. The methods were validated against Whitham's exact geometrical shock dynamics solutions of the self-similar shock diffraction problem. As compared to the typical forward straight-ray method, which is of the first order, the proposed methods demonstrate convergence rates greater by more than one order of magnitude and tend to be of the second order. The convergence analysis enabled to determine the range of time resolution required for the proposed methods to provide reliable results. This range is comparable to the acquisition rate of a modern high-speed camera that is commonly used in detonation visualization experiments. Differences between the proposed curved ray-tracking methods and the typical forward straight-ray method have been further examined by analyzing the ray dynamics during the weakly unstable hydrogen–oxygen–argon detonation diffraction. The ray-tube-based velocity-curvature relationship showed satisfactory agreement with those already well-established in quasi-steady experiments and predicted by the generalized Zeldovich–von Neumann–Doering model. This suggests the limited role of unsteadiness and cellular structure in the macro-scale dynamics of weakly unstable detonations.

Tracking using Human Pose Matching with Deep Association Metric

This paper proposes a novel approach to track multiple people utilizing skeletal information combined with visual appearance features to improve the accuracy of tracking people across different frames of a video. We extracted the appearance feature vectors and skeletal feature vectors for each detected person in every frame. Each individual was tracked by considering the cosine distance between the skeletal feature vectors along with the euclidean distance between the appearance feature vectors across different frames of a video. This reduces the dependency of the tracker over appearances of people thus making it more consistent, especially in videos with people having similar appearances such as sports videos with players wearing similar jerseys. The stance of an individual in continuing frames is expected to be similar considering the high frame rate of modern camera devices. Therefore it is befitting to consider skeletal features along with appearance features for tracking. Our paper is an incremental paper demonstrating improvement over SORT with a deep association metric approach. Our approach utilizing skeletal information combined with visual appearance information returns better MOT results on the MOT17 dataset using the yolov3 detector.

JomAttendance: Making Facial Business Sense

The attendance system in education currently used in the world are commonly initial on paper is not effective in recording attendance because people can alter and change the data easily. With modern camera technology, the attendance system should use a facial recognition system when recording attendance. Mixed-mode research concluded that university students have agreed that the traditional attendance verification process is a hassle t because of the large class size. Therefore this system plans to solve problems such as reliability, truant and delays when taking attendance. This system uses a facial recognition attendance algorithm. The system demonstrates the benefits of having a facial detection attendance system in a university setting. The facial detection approach is recommended towards a new standard for university attendance because it is faster and more effective to record student attendance.

Blue Eyes Technology

The Blue Eyes technology aims at creating computational machines that have perceptual and sensory ability like those of human beings. It uses non-obtrusive sensing method, employing most modern video cameras and microphones to identify the user’s actions through the use of imparted sensory abilities. The machine can understand what a user wants, where he is looking at, and even realize his physical or emotional states. The Blue Eyes Technology developed is intended to be a complex solution for monitoring and recording the operator’s conscious brain involvement as well as his/her physiological condition. This shows yet another development in the field of Brain Computer Interface. The basic idea behind this technology is to give the computer the human power. We all have some perceptual abilities. That is we can understand each other’s feelings. For example we can understand one’s emotional state by analysing his facial expression. If we add these perceptual abilities of human to computers would enable computers to work together with human beings as intimate partners. The “BLUE EYES” technology aims at creating computational machines that have perceptual and sensory ability like those of human beings. This paper discusses the concept of blue eyes technology.

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.

Continuously streaming compressed high-speed photography using time delay integration.

An imaging system capable of acquiring high-resolution data at a high speed is in demand. However, the amount of optical information captured by a modern camera is limited by the data transfer bandwidth of electronics, resulting in a reduced spatial and temporal resolution. To overcome this problem, we developed continuously streaming compressed high-speed photography, which can record a dynamic scene with an unprecedented space-bandwidth-time product. By performing compressed imaging in a time-delay-integration manner, we continuously recorded a 0.85 megapixel video at 200 kHz, corresponding to an information flux of 170 gigapixels per second.

Time-travel rephotography

Many historical people were only ever captured by old, faded, black and white photos, that are distorted due to the limitations of early cameras and the passage of time. This paper simulates traveling back in time with a modern camera to rephotograph famous subjects. Unlike conventional image restoration filters which apply independent operations like denoising, colorization, and superresolution, we leverage the StyleGAN2 framework to project old photos into the space of modern high-resolution photos, achieving all of these effects in a unified framework. A unique challenge with this approach is retaining the identity and pose of the subject in the original photo, while discarding the many artifacts frequently seen in low-quality antique photos. Our comparisons to current state-of-the-art restoration filters show significant improvements and compelling results for a variety of important historical people. Please go to <u>time-travel</u>l<u>-rephotography.github.io</u> for many more results.

Research of Distorted Vehicle Magnetic Signatures Recognitions, for Length Estimation in Real Traffic Conditions.

Reliable cost-effective traffic monitoring stations are a key component of intelligent transportation systems (ITS). While modern surveillance camera systems provide a high amount of data, due to high installation price or invasion of drivers’ personal privacy, they are not the right technology. Therefore, in this paper we introduce a traffic flow parameterization system, using a built-in pavement sensing hub of a pair of AMR (anisotropic magneto resistance) magnetic field and MEMS (micro-electromechanical system) accelerometer sensors. In comparison with inductive loops, AMR magnetic sensors are significantly cheaper, have lower installation price and cause less intrusion to the road. The developed system uses magnetic signature to estimate vehicle speed and length. While speed is obtained from the cross-correlation method, a novel vehicle length estimation algorithm based on characterization of the derivative of magnetic signature is presented. The influence of signature filtering, derivative step and threshold parameter on estimated length is investigated. Further, accelerometer sensors are employed to detect when the wheel of vehicle passes directly over the sensor, which cause distorted magnetic signatures. Results show that even distorted signatures can be used for speed estimation, but it must be treated with a more robust method. The database during the real-word traffic and hazard environmental condition was collected over a 0.5-year period and used for method validation.

NEW ACID-BASIC INDICATORS: RESEARCH USING A SMARTPHONE

Modern cameras, desktop scanners, smartphones allow not only registering an image, but also determining its color characteristics. That allows us to quickly, objectively and automatically determine the color characteristics of colored samples in acid-base titration, because there is a significant error at visually determining the pH range of the color transition. In analytical chemistry the characteristics of acid-base indicators are very important, in particular their pH transition interval. But the disadvantage of most indicators is the wide range of color transition: from 1 to 3 pH units. The aim of this work is to find new acid-base indicators that change color in an alkaline environment and have a narrow pH range of the color transition. We have developed the apparatus and technique of convenient and highprecision simultaneous determination of the pH of the medium and the color of the acidbase indicators. In acid-base titration the PH measurements were performed with a combined glass electrode AD1131 by рН-meter AD1000. The color transition was determined with help of a smartphone with the subsequent processing of the results by computer software. The color characteristics were measured for each channel of the RGB model in the range from 0 to 255. Our apparatus is small and mobile, and allows us simultaneously to measure the pH of the medium and accurately to determine the color characteristics. As a result, we can construct graphical dependencies of color on pH for each channel of the RGB model. We found the N-arylsulfonyl-2-aroylamido-1,4-benzo(naphto)quinone monoimines and 2,5-dibenzoylamido-1,4-benzoquinone are good acid-base indicators. They “work” in the pH range from 8.82 to 11.35 and have a very narrow color transition interval from 0.10 to 0.61. Solutions of these compounds in an alkaline medium have bright intense colors due to formation of mesomeric ions. That allows using of these indicators in the titration of weak acids with strong bases and vice versa by the method of neutralization.

On-Chip Optical Anodic Stripping with Closed Bipolar Cells and Cathodic Electrochemiluminescence Reporting.

The aim of this work was to develop a simple, accessible, and point-of-use sensor to measure heavy metal ions in water in low-resource areas that cannot accommodate expensive or technical solutions. This report describes a new bipolar electrochemical sensor platform that reimagines conventional anodic stripping voltammetry in a wireless bipolar format with an optical electrochemiluminescent readout that can be quantified with any simple optical sensor like that found on most modern cell phone cameras. We call this technique as optical anodic stripping. Using a new nonlithographic fabrication process, devices could be produced rapidly and simply at <$1/sensor. The sensing scheme was developed, characterized, and optimized using electrochemical and optical methods. Quantitation of Pb2+ in both lab and natural water samples was rapid (2-3 min), accurate, precise, and highly linear in the 25-1000 ppb range and was shown to be sufficiently selective in the presence of other common heavy metal ions such as Cu2+, Cd2+, and Zn2+.