Camera Settings Research Articles

Beyond PRNU: Learning Robust Device-Specific Fingerprint for Source Camera Identification.

Source-camera identification tools assist image forensics investigators to associate an image with a camera. The Photo Response Non-Uniformity (PRNU) noise pattern caused by sensor imperfections has been proven to be an effective way to identify the source camera. However, the PRNU is susceptible to camera settings, scene details, image processing operations (e.g., simple low-pass filtering or JPEG compression), and counter-forensic attacks. A forensic investigator unaware of malicious counter-forensic attacks or incidental image manipulation is at risk of being misled. The spatial synchronization requirement during the matching of two PRNUs also represents a major limitation of the PRNU. To address the PRNU’s fragility issue, in recent years, deep learning-based data-driven approaches have been developed to identify source-camera models. However, the source information learned by existing deep learning models is not able to distinguish individual cameras of the same model. In light of the vulnerabilities of the PRNU fingerprint and data-driven techniques, in this paper, we bring to light the existence of a new robust data-driven device-specific fingerprint in digital images that is capable of identifying individual cameras of the same model in practical forensic scenarios. We discover that the new device fingerprint is location-independent, stochastic, and globally available, which resolves the spatial synchronization issue. Unlike the PRNU, which resides in the high-frequency band, the new device fingerprint is extracted from the low- and mid-frequency bands, which resolves the fragility issue that the PRNU is unable to contend with. Our experiments on various datasets also demonstrate that the new fingerprint is highly resilient to image manipulations such as rotation, gamma correction, and aggressive JPEG compression.

Identifying the Digital Camera from Natural Images Using Residual Noise and the Jensen–Shannon Divergence

Regarding the problem of digital camera identification, many methods have been proposed, and for several of them, their effectiveness has been verified on the basis of disputed flat images. However, in real cases the disputed images are natural images, rather than flat images. In that case, several of the already proposed methods are not effective. Hence, in this paper, a method is proposed for the digital camera identification from natural images based on the statistical comparison between the residual noise in the natural disputed images and the fingerprint defined for the eligible digital cameras. In the reported case studies, the HDR database provided by the Communications and Signal Processing Laboratory of University of Florence is used to select a set of eligible digital cameras, and from this image database, for each digital camera, a set of disputed flat images, a set of disputed natural images, and a set of flat reference images were selected. Thus, the fingerprint of each digital camera was calculated from the probability density function (PDF) of the photo-response nonuniformity (PRNU) extracted from its reference images. Therefore, in order to identify the source digital camera of a natural disputed image, the Jensen–Shannon divergence (JSD) was implemented to statistically compare the PRNU-based fingerprint of each eligible source camera against the noise residual of that disputed image. The proposed method has a similar effectiveness to methods based on the peak-to-correlation energy or the Kullback–Leibler divergence when the disputed images are flat images and the PRNU is considered, but it is significantly more effective than those methods when the disputed images are natural images.

Energy-Constrained UAV Flight Scheduling for IoT Data Collection With 60 GHz Communication

In recent years, the data from Internet of Things (IoT) devices is growing at a rapid pace, and the data collection issues have attracted more and more attention. Distinct from existing solutions which usually adopted traditional wireless technologies achieving low-bandwidth data connections towards IoT devices, this paper adopts the 60 GHz communication technology which is with increasing maturity, providing high-bandwidth wireless transmission for data-intensive IoT devices, such as high-definition (HD) cameras. However, 60 GHz links are subject to line-of-sight short communication range, therefore, we propose to use unmanned aerial vehicles (UAVs) to fly over data-intensive IoT devices and achieve short-range high-throughput 60 GHz transmission in this paper. Moreover, for a set of HD cameras deployed in the linear scenario, multiple UAV flights are assigned to collect data by 60 GHz communication. To this end, we investigate the UAV flight scheduling (UFS) problem which aims to minimize the number of UAV flights while satisfying the data requirements of all ground cameras (GCs) with limitations of UAV’s energy and data storage. We prove that the UFS problem is NP-hard and design efficient algorithms with constant theoretical approximation ratios. Specifically, we first study a special case of the UFS problem where all the cameras are on the same direction of the UAV ground station, and propose two algorithms NF_SUFS and FF_SUFS, whose approximation ratios are both proved to be 2 by theoretical analysis. Then, we extend the algorithms to a more general case with the cameras on both directions of the ground station along the road, and put forward the FF_UFS algorithm that achieves an approximation ratio of 3. Finally, we conduct experiments to validate the effectiveness and efficiency of our algorithms.

BodyTrak

In this paper, we present BodyTrak, an intelligent sensing technology that can estimate full body poses on a wristband. It only requires one miniature RGB camera to capture the body silhouettes, which are learned by a customized deep learning model to estimate the 3D positions of 14 joints on arms, legs, torso, and head. We conducted a user study with 9 participants in which each participant performed 12 daily activities such as walking, sitting, or exercising, in varying scenarios (wearing different clothes, outdoors/indoors) with a different number of camera settings on the wrist. The results show that our system can infer the full body pose (3D positions of 14 joints) with an average error of 6.9 cm using only one miniature RGB camera (11.5mm x 9.5mm) on the wrist pointing towards the body. Based on the results, we disscuss the possible application, challenges, and limitations to deploy our system in real-world scenarios.

Technology Focus: Reservoir Surveillance (September 2022)

When it comes to reservoir surveillance, subsurface professionals have a broad array of tools and techniques to understand what is happening in the reservoir. To ensure valuable information is gathered with surveillance, it’s important to first ask “what problem are we trying to solve?” Depending on the problem’s definition and scale, the appropriate surveillance technology will change significantly. Zooming far out, particularly if little data is available for a reservoir, we may be interested in tools and techniques to validate and refine the geological concept for a basin, along with the most likely deposition patterns for sediments. Zooming in to the level of interactions between wells, we need to use a different toolbox that often includes finer-resolution geoscience mapping and engineering methods such as analyzing production, pressure, and fluid-property trends from individual wells. But what’s really exciting is that many new approaches are being developed to understand reservoir behavior and well performance at incredibly high resolution—at the fracture scale or even the pore scale. In this feature, we will examine surveillance technologies that can be applied at the broad scale, midscale, and fine scale. The papers highlighted in this month’s feature apply forward stratigraphic modeling to improve geological models, classical reservoir engineering techniques to understand interwell connectivity in order to optimize waterflooding operations, and exciting new fiber-optic technology to characterize the performance of individual hydraulic fractures in unconventionals. I hope these articles help you to think about “framing” the problem you are trying to solve, like a photographer carefully selecting the right camera settings and lens for a specific shot, in order to select the right tools and methods to add value through surveillance. Recommended additional reading at OnePetro: www.onepetro.org. SPE 202319 - Utilizing Surface Microseismic Monitoring To Improve Understanding of Natural Fractures in Sichuan Shale Gas Play by Cui Jing, Sichuan Changning Natural Gas Development Company, et al. SPE 202837 - Modified Technique To Model Volatile Oil Reservoirs: Implications for Modern Software Programs by Mohamed Ibrahim, Shell Egypt, et al. SPE 201543 - Production Optimization Using a 24/7 Distributed Fiber-Optic DFO Sensing-Based Multiphase Inflow Profiling Capability by Teymur Sadigov, BP, et al.

A Method to Extract Rail Flatness Profile and Analysis of Different Noise Sources

This article addresses the problem of the rail flatness assessment that occurs at its final production stage. Flatness control must be measured during the total length of each rail to avoid problems during this rail usage. Only the flat rails can be used in high-performance environments such as high-speed trains. Rails quality assessment must be done to avoid highway security problems or any other type of circumstances that may prevent the normal operation of the systems where these rails will be installed. The rail that does not meet the minimum flatness requirement for the high-performance usage, can be used in standard railways, but if the flatness imperfections are greater than certain tolerances, it must go through a process of restraightening or been discarded. To obtain the rail points where flatness will be measured, we propose a method based on a set of cameras distributed in a range along a section of the rail. The goal is to reduce the impact of the rail vibrations, and then, obtain a synthetic shape that calculates the original rail shape. This shape can be used to measure rail flatness using any other process. The obtained results indicate that this method is robust and accurate.

Falcon Neuro: an event-based sensor on the International Space Station

We report on the Falcon neuro event-based sensor (EBS) instrument that is designed to acquire data from lightning and sprite phenomena and is currently operating on the International Space Station. The instrument consists of two independent, identical EBS cameras pointing in two fixed directions, toward the nominal forward direction of flight and toward the nominal Nadir direction. The payload employs stock DAVIS 240C focal plane arrays along with custom-built control and readout electronics to remotely interface with the cameras. To predict the sensor’s ability to effectively record sprites and lightning, we explore temporal response characteristics of the DAVIS 240C and use lab measurements along with reported limitations to model the expected response to a characteristic sprite illumination time-series. These simulations indicate that with appropriate camera settings the instrument will be capable of capturing these transient luminous events when they occur. Finally, we include initial results from the instrument, representing the first reported EBS recordings successfully collected aboard a space-based platform and demonstrating proof of concept that a neuromorphic camera is capable of operating in the space environment.

Predicting real-time fire heat release rate by flame images and deep learning

The heat release rate (HRR) is the most critical parameter in characterizing the fire behavior and thermal effects of a burning item. However, traditional fire calorimetry methods are not applicable due to the lack of equipment in most fire scenarios. This work explores the real-time fire heat release rate prediction by using fire scene images and deep learning algorithms. A big database of 112 fire tests from the NIST Fire Calorimetry Database is formed, and 69,662 fire scene images labeled by their transient heat release rate are adopted to train the deep learning model. The fire tests conducted in the lab environment and the real fire events are used to validate and demonstrate the reliability of the trained model. Results show that regardless of the fire sources, background, light conditions, and camera settings, the proposed AI-image fire calorimetry method can well identify the transient fire heat release rate using only fire scene images. This work demonstrates that the deep learning algorithms can provide an alternative method to measure the fire HRR when traditional calorimetric methods cannot be used, which shows great potential in smart firefighting applications.

Auto calibration of multi‐camera system for human pose estimation

The multi-camera calibration is an essential step for many spatially aware applications, such as robotic navigation, augmented reality, and 3D human pose estimation. Traditional calibration methods use off-the-shelf checkerboards or triangles as the known world coordinate system and their corresponding corners are set as control points, which heavily depends on specific calibration patterns and is not suitable for calibration pattern-denied environments. In this paper, an automatic calibration method is proposed to calibrate the multi-camera system without the aid of a known calibration pattern. The key idea of the proposed method is that the authors consider the human body, which is always available, as the counterpart of the calibration pattern. The authors’ approach starts with binocular camera calibration, in which the extrinsic and intrinsic parameters are calculated in order and followed by a joint optimisation. With the results of each pair of binocular camera calibration, the multi-camera system calibration is carried out in three steps: (i) parameters initialisation, (ii) extrinsic parameters optimisation, and (iii) jointly optimising intrinsic and extrinsic parameters. Since the authors’ approach does not require additional calibration patterns except for one visible person, it is flexible and easy to be implemented. Real experiments are conducted in different scenes, camera angles, and camera settings. Human pose estimation with the multi-camera system is additionally performed for exhaustive experiments. The experimental results demonstrate that the authors’ method shows superior performance than the traditional method with the aid of a specific calibration pattern.

SOS Implementation With Camera Facilities

The system defines the task SOS (Save Our Souls) means it is a system which will manage the emergency situations and provide solution using android application. The goal of the project is to develop an android application that lets its users to send notification through mail in case of an emergency or a panic situation. The users can send emails on the press of single button. The phone number, mail id and camera set from within the application. The notification will be send with person’s image. The app can capture the human face in real time and send to the emergency contacts. This is very helpful in tracking the whereabouts of the person. Women’s safety is a big concern which has been the most important topic till date. As ladies ought to travel late night generally, it’s necessary to remain alert and safe. Most of the females these days carry their Smartphone with them, so it is necessary to have at least one the personal safety apps installed. Here we introduce an app which ensures the safety of people. This helps to identify and call on resources to help the one out of dangerous situations. This is a common app for using all kind of people especially women for their safety. In this application user need to feed five contacts these are the people who will receive notifications via email in case of an emergency or any dangerous situations. In case they are in dangerous situation or attacked by any person, user will click one button and the message will pass on fiver users’ mail id and the attacker face captured and send to emergency contact’s mail ID. The system designs the android application as emergency app in mobile phones

EyeNeRF

A unique challenge in creating high-quality animatable and relightable 3D avatars of real people is modeling human eyes, particularly in conjunction with the surrounding periocular face region. The challenge of synthesizing eyes is multifold as it requires 1) appropriate representations for the various components of the eye and the periocular region for coherent viewpoint synthesis, capable of representing diffuse, refractive and highly reflective surfaces, 2) disentangling skin and eye appearance from environmental illumination such that it may be rendered under novel lighting conditions, and 3) capturing eyeball motion and the deformation of the surrounding skin to enable re-gazing. These challenges have traditionally necessitated the use of expensive and cumbersome capture setups to obtain high-quality results, and even then, modeling of the full eye region holistically has remained elusive. We present a novel geometry and appearance representation that enables high-fidelity capture and photorealistic animation, view synthesis and relighting of the eye region using only a sparse set of lights and cameras. Our hybrid representation combines an explicit parametric surface model for the eyeball surface with implicit deformable volumetric representations for the periocular region and the interior of the eye. This novel hybrid model has been designed specifically to address the various parts of that exceptionally challenging facial area - the explicit eyeball surface allows modeling refraction and high frequency specular reflection at the cornea, whereas the implicit representation is well suited to model lower frequency skin reflection via spherical harmonics and can represent non-surface structures such as hair (i.e. eyebrows) or highly diffuse volumetric bodies (i.e. sclera), both of which are a challenge for explicit surface models. Tightly integrating the two representations in a joint framework allows controlled photoreal image synthesis and joint optimization of both the geometry parameters of the eyeball and the implicit neural network in continuous 3D space. We show that for high-resolution close-ups of the human eye, our model can synthesize high-fidelity animated gaze from novel views under unseen illumination conditions, allowing to generate visually rich eye imagery.

ФОТОГРАФІЧНИЙ ТВІР ЯК ОБ’ЄКТ ПРАВОВОЇ ОХОРОНИ

The article analyzes a photographic work as an object of legal protection. The author researches the peculiarities of legal protection of photographs, characterizes their legal nature, analyzes the peculiarities of the exercise of copyright in photographs. It is established that a photographic work can exist in various forms, but as of today photography is created and exists mainly in digital form, which determines the peculiarities of the exercise and protection of rights to it. It is highlighted that the national copyright law does not contain a definition of a photographic work, a photograph as well as a work made by means similar to a photograph, which is a shortcoming of legal regulation. It is proved that the object of legal protection is a photographic work, not a photograph, which may have all the features of the object of copyright. In addition, it is substantiated that the legal regime of a photographic work is also not defined in the legislation of Ukraine, which is a shortcoming of legal regulation. It is established that the Law of Ukraine «On Copyright and Related Rights» practically does not pay attention to the characteristics of a photographic work, only mentioning it among the objects of copyright. Moreover, it is proved that the features of a photographic work as an object of copyright are its creative character (as a result of creative activity), original character (expressed in a creative way of expressing the author’s idea – framing, lighting, focus, camera settings, etc.) and objective form of expression as a result of transferring the work from the author’s consciousness in the form of a creative idea to an independent material object, mainly in digital form). It is established that the creative and original nature of photography are often identified as features of the object of copyright. In turn, it is justified that a photograph and other works of art depicting an individual may be publicly shown, reproduced, distributed only with the consent of this person, and in case of his death – with the consent of persons specified by the law. It is proved that such an institution as the freedom of panorama, which characterizes the ability to photograph architectural objects that are in public places, is practically not regulated at the level of law. Consequently, it is established that the right to photograph the relevant architectural object as an object of copyright belongs to the personal non-property rights of the architect, but this approach of the legislator seems questionable, as the photography is a way to capture and reproduce the object of architectural activity as an object of copyright, which is a way to use property rights as a copyright. The imperfection of the application of the so-called «take down notice» procedure, provided for in Art. 52-1 of the Law of Ukraine «On Copyright and Related Rights», is stated, as this out-of-court procedure for protection of copyright in the digital environment does not apply to photographic works, which is a shortcoming of legal regulation.

Pose Guided Image Generation from Misaligned Sources via Residual Flow Based Correction

Generating new images with desired properties (e.g. new view/poses) from source images has been enthusiastically pursued recently, due to its wide range of potential applications. One way to ensure high-quality generation is to use multiple sources with complementary information such as different views of the same object. However, as source images are often misaligned due to the large disparities among the camera settings, strong assumptions have been made in the past with respect to the camera(s) or/and the object in interest, limiting the application of such techniques. Therefore, we propose a new general approach which models multiple types of variations among sources, such as view angles, poses, facial expressions, in a unified framework, so that it can be employed on datasets of vastly different nature. We verify our approach on a variety of data including humans bodies, faces, city scenes and 3D objects. Both the qualitative and quantitative results demonstrate the better performance of our method than the state of the art.

In-Cabin Monitoring System for Autonomous Vehicles.

In this paper, we have demonstrated a robust in-cabin monitoring system (IMS) for safety, security, surveillance, and monitoring, including privacy concerns for personal and shared autonomous vehicles (AVs). It consists of a set of monitoring cameras and an onboard device (OBD) equipped with artificial intelligence (AI). Hereafter, this combination of a camera and an OBD is referred to as the AI camera. We have investigated the issues for mobility services in higher levels of autonomous driving, what needs to be monitored, how to monitor, etc. Our proposed IMS is an on-device AI system that indigenously has improved the privacy of the users. Furthermore, we have enlisted the essential actions to be considered in an IMS and developed an appropriate database (DB). Our DB consists of multifaced scenarios important for monitoring the in-cabin of the higher-level AVs. Moreover, we have compared popular AI models applied for object and occupant recognition. In addition, our DB is available on request to support the research on the development of seamless monitoring of the in-cabin higher levels of autonomous driving for the assurance of safety and security.

The Effect of Bit Depth on High-Temperature Digital Image Correlation Measurements

Digital Image Correlation (DIC) is a camera-based method of measuring displacement and strain. High-temperature DIC is challenging due to light emitted from the sample which can saturate the image. This effect can be mitigated using optical bandpass filters, but the maximum sample temperature range of DIC remains dependent on the camera and camera settings. Among camera settings, bit depth, also referred to as color depth or number of bits, has received insufficient attention in high-temperature DIC literature. In this work, the effect of bit depth on DIC measurements is investigated both analytically and experimentally. It is shown that if image noise is sufficiently low, then increasing bit depth reduces DIC random error. A new metric, the effective number of bits, is presented to determine the appropriate number of bits for DIC images. Using increased bit depth, reduced exposure time, and low-noise images, the maximum sample temperature for DIC measurements was shown to increase without negatively impacting random error.

An Efficient Multiresolution Network for Vehicle Reidentification

In general, vehicle images have varying resolutions due to vehicles’ movements and different camera settings. However, most existing vehicle re-identification models are single-resolution deep networks trained with pre-uniformly resizing vehicle images, which underestimates adverse effects of varying resolutions and leads to unsatisfactory performance. A straightforward solution for dealing with varying resolutions is to train multiple vehicle re-identification models. Each model is independently trained with images of a specific resolution. However, this straightforward solution requires significant overhead and ignores intrinsic associations among different resolution images. For that, an efficient multi-resolution network (EMRN) is proposed for vehicle re-identification in this paper. Firstly, EMRN embeds a newly-designed multi-resolution feature dimension uniform module (MR-FDUM) behind a traditional backbone network (i.e., ResNet-50). As a result, the whole model can extract fixed dimensional features from different resolution images so that it can be trained with one loss function of fixed dimensional parameters rather than training multiple models. Secondly, a multi-resolution image randomly feeding strategy is designed to train EMRN, making each mini-batch data of a random resolution during the training process. Consequently, EMRN can implicitly learn collaborative multi-resolution features via only a unitary deep network. Experiments on three large scale datasets, i.e., VeRi776, VehicleID, and VRIC, demonstrate that EMRN is superior to state-of-the-art vehicle re-identification methods.

Archival Silence in the Collections of Dietrich Schulz-Köhn

Archival Silence in the Collections of Dietrich Schulz-Köhn

Distribution and Morphology of Lava Tube Systems on the Western Flank of Alba Mons, Mars

AbstractIntegrated analyses of Thermal Emission Imaging System (THEMIS) IR, Context Camera (CTX), and Mars Orbiter Laser Altimeter (MOLA) data sets have been used to characterize the western flank of the Martian volcano Alba Mons, which hosts a prominent population of lava tube systems. Identification and mapping of lava tube systems is based on both morphologic and topographic analyses, including the presence of chains of collapse depressions and elongate, sinuous ridges. Lava tubes and adjacent tabular lava flows with lengths of 100+ km form an extensive lava flow field. Analyses of topographic data sets, including slope maps, suggest continuity of the radial flow field pattern across the full western flank. Concurrent surface activity across the western flank is consistent with age constraints from geologic mapping and crater size‐frequency distributions that indicate Early Amazonian ages. The mapped population of 331 lava tube systems in the western flank geologic map quadrangle has a mean length of 36.2 km and a total length of ∼12,000 km. Individual lava tube systems extend up to ∼400 km. Orientation and slope data for lava tube systems show small deviations compared to regional values in 50‐km grid cells defined by the MOLA Digital Elevation Model, suggesting a strong coupling of lava tubes to the current slopes of Alba Mons. Mapping of lava tube systems documents segments both showing collapse and with no collapse, indicating the potential for extensive subsurface cavities that would be important astrobiological targets.

TIMo-A Dataset for Indoor Building Monitoring with a Time-of-Flight Camera.

We present TIMo (Time-of-flight Indoor Monitoring), a dataset for video-based monitoring of indoor spaces captured using a time-of-flight (ToF) camera. The resulting depth videos feature people performing a set of different predefined actions, for which we provide detailed annotations. Person detection for people counting and anomaly detection are the two targeted applications. Most existing surveillance video datasets provide either grayscale or RGB videos. Depth information, on the other hand, is still a rarity in this class of datasets in spite of being popular and much more common in other research fields within computer vision. Our dataset addresses this gap in the landscape of surveillance video datasets. The recordings took place at two different locations with the ToF camera set up either in a top-down or a tilted perspective on the scene. Moreover, we provide experimental evaluation results from baseline algorithms.

Measurement of Large-Sized-Pipe Diameter Based on Stereo Vision

To address the lack of fast and high-precision in situ measurement of large-sized pipes in current industrial applications, a pipe-diameter-measurement method based on stereo vision is designed in this paper. By using multiple sets of binocular cameras to perform 3D reconstruction and integration of multiple laser markers projected on measured cross sections of pipes, the pipe diameter can be estimated. In this method, a measurement algorithm is adopted to enable automatic matching of feature points through affine distance transformation, and an optimized point-cloud-registration algorithm with normal-vector constraints is used to ensure measurement robustness. To verify the feasibility of the method, an experimental system was built under laboratory conditions, and three types of pipes with outer diameters from 285 mm to 325 mm were measured. The experimental results show that the relative error is within ±0.570% and the maximum repeatability standard deviation is 0.551 mm. The experimental results basically meet industrial standards, and the proposed method therefore has good application prospects.