Camera Model Research Articles

Perspective Camera Model With Refraction Correction for Optical Velocimetry Measurements in Complex Geometries.

Camera calibration is among the most challenging aspects of the investigation of fluid flows around complex transparent geometries, due to the optical distortions caused by the refraction of the lines-of-sight at the solid/fluid interfaces. This work presents a camera model which exploits the pinhole-camera approximation and represents the refraction of the lines-of-sight directly via Snell's law. The model is based on the computation of the optical ray distortion in the 3D scene and dewarping of the object points to be projected. The present procedure is shown to offer a faster convergence rate and greater robustness than other similar methods available in the literature. Issues inherent to estimation of the refractive extrinsic and intrinsic parameters are discussed and feasible calibration approaches are proposed. The effects of image noise, volume size of the control point grid and number of cameras on the calibration procedure are analyzed. Finally, an application of the camera model to the 3D optical velocimetry measurements of thermal convection inside a polymethylmethacrylate (PMMA) cylinder immersed in water is presented. A specific calibration procedure is designed for such a challenging experiment where the cylinder interior is not physically accessible and its effectiveness is demonstrated by providing velocity field reconstructions.

Global Horizontal Irradiance Modeling for All Sky Conditions Using an Image-Pixel Approach

Ground images with a sky camera have become common to evaluate cloud coverage, aerosols, and energy collection. In parallel, the growth of solar energy has led to an impulse to evaluate and forecast the solar potential in a site before investments, which has increased the importance of solar power measurements. Facing that scenario, this work presents a novel sky camera model that allows to measure the global horizontal irradiance (GHI). Initially, images from a fisheye camera were stored and a pixel-based approach model was created for cloud segmentation. A total of 813 k vectors of features were used as input to the support vector machine for classification (SVC), which yielded a success rate of about 98.6% in accuracy. The Sun’s position was also segmented and an artificial neural network (ANN) regression model for GHI with 17 input features was created based on segmentation of the Sun, clouds, and sky. The training/validation stage of the ANN used 89,964 samples and the test stage reached about 97.4% in Pearson’s correlation. The RMSE was 72.3 W/m2 for GHI and the normalized RMSE, nRMSE, revealed 12.9% for GHI. That nRMSE value was comparable to or lower than other studies, despite the high fluctuations in the observed GHI.

Calibration Venus: An Interactive Camera Calibration Method Based on Search Algorithm and Pose Decomposition

Cameras are widely used in many scenes such as robot positioning and unmanned driving, in which the camera calibration is a major task in this field. The interactive camera calibration method based on a plane board is becoming popular due to its stability and handleability. However, most methods choose suggestions subjectively from a fixed pose dataset, which is error-prone and limited for different camera models. In addition, these methods do not provide clear guidelines on how to place the board in the specified pose. This paper proposes a new interactive calibration method, named ‘Calibration Venus’, including two main parts: pose search and pose decomposition. First, a pose search algorithm based on simulated annealing (SA) algorithm is proposed to select the optimal pose in the entire pose space. Second, an intuitive and easy-to-use user guidance method is designed to decompose the optimal pose into four sub-poses: translation, each rotation along X-, Y-, Z-axes. Thereby the users could follow the guide step by step to accurately complete the placement of the calibration board. Experimental results evaluated on simulated and real datasets show that the proposed method can reduce the difficulty of calibration, and improve the accuracy of calibration, as well as provide better guidance.

Distorted pinhole camera modeling and calibration.

Camera modeling and calibration are essential tasks in modern optics. Conventionally, the pinhole model is adopted with a further extension for lens distortion. However, pinhole and distortion models are mutually dependent; thus, the standard approach induces systematic camera calibration errors. This research presents a unifying distorted pinhole camera model that includes a telecentric, distortion-free pinhole, and radial lens distortion as particular cases. An iterative calibration method based on the derived distorted pinhole model is proposed, and experimental evaluation by calibrating a camera with high radial distortion is performed. The calibration results are compared with the standard and fisheye models using a well-known commercial camera calibrator software. The proposed method outperforms the standard model and achieves accuracy comparable to the fisheye model, and the proposed approach is a versatile and accurate tool for diverse optical metrology applications.

Coupling tomographic and thermographic measurements for in-situ thermo-mechanical tests

In order to determine the thermo-mechanical properties of a complex 3D woven ceramic composite material, an experiment at high and inhomogeneous temperature and its dedicated full-field measurement procedure is developed. 3D tomographic images of the tested sample are captured at different stages of loading in a synchrotron beamline, and an infrared camera captures a side view of the sample as it rotates in the x-ray beam. A pin-hole projective model of the thermographic camera allows one to map the thermal field measured under numerous orientations onto a 3D mesh of the sample built from an initial tomographic image or a model. The projective model has to be calibrated, and an original procedure is proposed thanks to an integrated digital image correlation algorithm based on the ‘silhouette’ of the sample (as only the protruding edges outlining the sample shape can be seen clearly). This procedure is illustrated with an experimental case study.

PSIX-37 Considerations for using game cameras for on-farm behavior of beef cattle

Abstract On-farm studies provide an opportunity to build relationships with clientele while answering applied science questions. Further, game trail cameras are a low-cost technology that may be an alternative to visually observing livestock for behavior data. An on-farm study involving eight county Extension agents and producers investigated game cameras to capture cattle visitation to mineral feeders. Three different models of Browning game trail cameras (Prometheus Group LLC, Birmingham, AL) each with timelapse feature were utilized. A three-page document provided background and methodology for the study. Details for placement of the trail camera were provided to provide consistency across farms. Cameras were pre-programmed to settings for image capture. A video for camera setup was also provided in the event that cameras reset to default settings due to battery failure. Timelapse feature was utilized providing image capture from sunrise to sunset at 2 min intervals. Several observations were noted to improve data capture in the future. Differences existed in battery utilization between camera models with one model only capturing images for approximately 2 of the 7 d periods while others had more than 80% battery life remaining after 7 d. Cameras were to be oriented facing south to avoid sun glare. Sun glare occurred at sunrise on most cameras and was particularly bad when dew was present on the camera lens. Mineral feeders need to be secured in place as livestock may move feeders out of the frame of the images captured. Still photos are insufficient in assessing behavior and short video clips are expected to improve interpretation especially when multiple animals visit the feeder at the same time. Utilization of human observation was not useful unless observation was performed at sunrise. Game trail cameras have the potential to be a cost-effective tool in capturing on-farm data of cattle behavior.

Non-central refractive camera calibration using co-planarity constraints for a photogrammetric system with an optical sphere cover

Optical sphere covers are widely used in photogrammetric systems to protect camera systems from external conditions. The spherical refraction can cause severe non-linear distortion, resulting in imprecise and unreliable measurements. In particular the cameras with a relatively thick optical sphere cover (e.g. underground or underwater cameras). However, the conventional perspective camera model are not suitable for such spherical refraction calibration problem. This study presents a novel non-central refractive calibration technique for cameras with an optical sphere cover. Considering the general situation in which the camera optical axis could not pass through the spherical center, a universal spherical refraction geometric model was established with coplanarity constraints. The model introduces modified perspective center and enables the spherical refraction can be integrated into the equivalent collinearity equations, which make it possible to describe explicitly the correspondences between the image points and object coordinates. Moreover, a calibration technique is proposed to estimate relative orientation parameters. With no need to gain explicit knowledge of object space coordinates, the spherical refraction calibration can be performed with known refractive index as well as the radius of sphere cover. Numerical and experimental results demonstrated the proposed spherical refraction calibration model to be both effective and robust, which can achieve high levels of calibration accuracy.

Image Forgery Detection and Localization via a Reliability Fusion Map

Moving away from hand-crafted feature extraction, the use of data-driven convolution neural network (CNN)-based algorithms facilitates the realization of end-to-end automated forgery detection in multimedia forensics. On the basis of fingerprints acquired by images from different camera models, the goal of this paper is to design an effective detector capable of completing image forgery detection and localization. Specifically, relying on the designed constant high-pass filter, we first establish a well-performing CNN architecture to adaptively and automatically extract characteristics, and design a reliability fusion map (RFM) to improve localization resolution, and tamper detection accuracy. The extensive results from our empirical experiments demonstrate the effectiveness of our proposed RFM-based detector, and its better performance than other competing approaches.

Vision-based relative pose determination of cooperative spacecraft in neutral buoyancy environment

Neutral buoyancy systems simulate the microgravity environment by taking advantage of buoyancy forces of water to offset the gravity of test bodies. Functional verification of space robots in neutral buoyancy system is of great importance for ground tests. The relative pose determination of a spacecraft plays an essential role in the on-orbit operation of space robots. In order to meet the requirement of on-orbit operation ground verification for space robots, this paper develops a vision-based system for determining the relative pose of cooperative spacecraft in neutral buoyancy environment. Cooperative markers and underwater binocular vision system are designed for the pose determination, and a cooperative spacecraft model is built. A detection and recognition method based on the topological characteristic is proposed for the cooperative marker. An underwater imaging model of binocular camera is established, and its refraction parameters are calibrated. The marker points are measured with an underwater binocular 3D measurement algorithm. Furthermore, the pose of cooperative spacecraft is determined using axisymmetric plane feature points. Additionally, the stable and reliable pose and velocity are obtained after the data are further processed with a Kalman filter. Finally, the experiments are carried out and the experimental results show that the proposed system can achieve a stable and reliable high-precision relative pose determination for cooperative spacecraft in neutral buoyancy environment.

Assessing occupancy and activity of two invasive carnivores in two Caribbean islands: implications for insular ecosystems

AbstractThe introduction of exotic species is one of the major causes of the decline of global biodiversity. Tropical insular ecosystems, including many biodiversity hotspots, are particularly threatened by biological invasions. Two wild carnivores have been introduced in the Caribbean, the northern raccoon Procyon lotor and the small Indian mongoose Urva auropunctata. Understanding the spatial distribution and activity patterns of both species is crucial for conservation purposes. Here we used camera trap data to model single‐season occupancy and detection of these two species on two Caribbean islands, Guadeloupe and Martinique. Our survey highlighted the broad distribution of both species on these islands, with the exception of the northern raccoon population in Martinique which appears very limited. Moreover, spatio‐temporal co‐occurrence with other bird and mammal species revealed that the northern raccoon and the small Indian mongoose face few or no competitors. Finally, our models show that the occupancy of both species was not influenced by any variable tested (i.e. elevation, precipitation, temperature and land cover) and that the probability to detect small Indian mongooses was influenced by land cover and camera model. These results highlight the potential of both the northern raccoon and the small Indian mongoose to have a significant impact on the native ecosystems in these hotspots of biodiversity and demonstrate the necessity to develop conservation actions towards control and limitation of these invasive carnivores.

The African golden cat Caracal aurata in Tanzania: first record and vulnerability assessment

AbstractWe report on the first population found in Tanzania of the Vulnerable African golden cat Caracal aurata, extending its documented range c. 200 km to the south and south-east. This is one of the least-known and truly forest-dependent felines in Africa, ranging across the Guinea–Congolian forest block. We recorded the new population in Minziro Nature Forest Reserve, north-west Tanzania, during a 3-month survey in 2018. We deployed 70 camera traps on a regular grid and obtained 33 detection events of the golden cat at 26% of sites, with a minimum of 10 individuals across 257 km2. We estimated occupancy and detection probability and modelled these in relation to the distance of sampling sites to the forest edge, which coincides with both the Reserve boundary and proximity to human settlements surrounding the Reserve. Mean estimated occupancy was 0.41 ± SE 0.12 (mean detectability = 0.13 ± SE 0.05), with occupancy increasing significantly with distance from the forest edge. Detectability did not vary significantly with distance from the forest edge, but was higher for camera models that had a shorter trigger time. Our findings add to the scant data available for this species. It appears threatened by human activity, which we recorded both outside and within the Reserve, and the presence of the species indicates Minziro Forest is an important site for its conservation.

Automatic fundus image quality assessment on a continuous scale

Fundus photography is commonly used for screening, diagnosis, and monitoring of various diseases affecting the eye. In addition, it has shown promise in the diagnosis of brain diseases and evaluation of cardiovascular risk factors. Good image quality is important if diagnosis is to be accurate and timely. Here, we propose a method that automatically grades image quality on a continuous scale which is more flexible than binary quality classification. The method utilizes random forest regression models trained on image features discovered automatically by combining basic image filters using simulated annealing as well as features extracted with the discrete Fourier transform. The method was developed and tested on images from two different fundus camera models. The quality of those images was rated on a continuous scale from 0.0 to 1.0 by five experts. In addition, the method was tested on DRIMDB, a publicly available dataset with binary quality ratings. On the DRIMDB dataset the method achieves an accuracy of 0.981, sensitivity of 0.993 and specificity of 0.958 which is consistent with the state of the art. When evaluating image quality on a continuous scale the method outperforms human raters.

NRSfPP: non-rigid structure-from-perspective projection

A state-of-the-art algorithm for perspective projection reconstruction of non-rigid surfaces from single-view and realistic videos is proposed. It overcomes the limitations arising from the usage of orthographic camera model and also the complexity and non-linearity issues of perspective projection equation. Unlike traditional non-rigid structure-from-motion (NRSfM) methods, which have been studied only on synthetic datasets and controlled lab environments that require some prior constraints (such as manually segmented objects, limited rotations and occlusions, and full-length trajectories); the proposed method can be used in realistic video sequences. In addition, contrary to previous methods that use multiple cameras with different relative viewing angles, only a single-view video is required to reconstruct the 3D structures. By only using the 2D frames of incoming video stream, the proposed method extracts the projective depth coefficients of each point in each input frame, rotation matrix, translation vector, varying camera parameters (such as focal lengths for each input frame), and finally reconstructs the 3D deformable shape. Due to the high number of unknowns, the problem has been divided into two parts of projective depth coefficients extraction and 3D shape reconstruction. Perspective reconstruction of non-rigid surfaces has been extended to be certainly converged which leads to a significant increase in execution frequency of the iterated algorithm that has been presented for projective depth coefficients extraction. As such, it produces promising results for perspective projection reconstruction of non-rigid surfaces from single-view and realistic videos. The accuracy and robustness of the proposed method is demonstrated quantitatively on synthetic data and qualitatively on real image sequences. The experimental results show that NRSfPP provides the state-of-the-art results and resolves the failures of previous approaches by eliminating all of the predefined situations and constraints of applying perspective projection as the camera model.

Using Sea Wave Simulations to Interpret the Sunglint Reflection Variation With Different Spatial Resolutions

Ocean surface sunglint reflection is very important for detecting ocean surface roughness, oil spills, oceanic internal waves, and so on. Although statistical sunglint models (i.e., Cox–Munk model) have been used successfully on coarse-resolution spaceborne optical images for several decades, it is still a challenge to apply it to high spatial resolution images due to scale effects of the optical remote sensing. In this study, a sea wave model and the Pinhole camera model were employed to simulate the sunglint reflection images with different spatial resolutions and viewing angles. Coefficient of variation (CV) of sunglint reflection collected from various images indicates that there is uncertainty in sunglint reflection in images with different spatial resolutions. This study presents the applicability of sunglint statistical models in images with different spatial resolutions and viewing angles.

Early entrants attract better customer evaluations: evidence from the digital camera industry

PurposeIn addition to pioneering, empirical work on entry order increasingly addresses fast followers and laggards and the potential advantages they are able to capture. There is also a growing consensus in the academia, that current measures of firm performance used in the entry order literature to study these advantages are inadequate. This study analyzes the relationship between entry order and customer evaluations, which, depicting the performance of the firm's products in the market, are used as a proxy for firm performance.Design/methodology/approachThe study is set in the digital camera industry, analyzing entries into each new technology level, in terms of the sensor resolution of compact and bridge cameras. The complete dataset consisted of 1,816 digital camera models introduced between January 1996 and December 2017. The data are analyzed using hierarchical multiple linear regression.FindingsThe study finds evidence of early-mover advantage for the compact product category. In the compact camera consumer market, both first-movers and fast followers outperform late movers. Furthermore, the difference in performance in comparison to laggards is greater for first-movers than for fast followers. However, in the bridge category which consists of a more heterogeneous set of products, no significant entry-order effects are detected.Originality/valueThe results clearly indicate that there exists an early mover advantage. Furthermore, the results are not consistent across different product categories within an industry; hence, caution needs to be exercised when analyzing industry dynamics and entry order effects. Finally, our novel conceptualization of firm performance measured as online customer evaluation add new opportunities to investigate firm success

Remote Estimation of Target Height from Unmanned Aerial Vehicle (UAV) Images

This paper focuses on how the height of a target can be swiftly estimated using images acquired by a digital camera installed into moving platforms, such as unmanned aerial vehicles (UAVs). A pinhole camera model after distortion compensation was considered for this purpose since it does not need extensive processing nor vanishing lines. The pinhole model has been extensively employed for similar purposes in past studies but mainly focusing on fixed camera installations. This study analyzes how to tailor the pinhole model for gimballed cameras mounted into UAVs, considering camera parameters and flight parameters. Moreover, it indicates a solution that foresees correcting only a few needed pixels to limit the processing overload. Finally, an extensive analysis was conducted to define the uncertainty associated with the height estimation. The results of this analysis highlighted interesting relationships between UAV-to-target relative distance, camera pose, and height uncertainty that allow practical exploitations of the proposed approach. The model was tested with real data in both controlled and uncontrolled environments, the results confirmed the suitability of the proposed method and outcomes of the uncertainty analysis. Finally, this research can open consumer UAVs to innovative applications for urban surveillance.

DoF-Dependent and Equal-Partition Based Lens Distortion Modeling and Calibration Method for Close-Range Photogrammetry.

Lens distortion is closely related to the spatial position of depth of field (DoF), especially in close-range photography. The accurate characterization and precise calibration of DoF-dependent distortion are very important to improve the accuracy of close-range vision measurements. In this paper, to meet the need of short-distance and small-focal-length photography, a DoF-dependent and equal-partition based lens distortion modeling and calibration method is proposed. Firstly, considering the direction along the optical axis, a DoF-dependent yet focusing-state-independent distortion model is proposed. By this method, manual adjustment of the focus and zoom rings is avoided, thus eliminating human errors. Secondly, considering the direction perpendicular to the optical axis, to solve the problem of insufficient distortion representations caused by using only one set of coefficients, a 2D-to-3D equal-increment partitioning method for lens distortion is proposed. Accurate characterization of DoF-dependent distortion is thus realized by fusing the distortion partitioning method and the DoF distortion model. Lastly, a calibration control field is designed. After extracting line segments within a partition, the de-coupling calibration of distortion parameters and other camera model parameters is realized. Experiment results shows that the maximum/average projection and angular reconstruction errors of equal-increment partition based DoF distortion model are 0.11 pixels/0.05 pixels and 0.013°/0.011°, respectively. This demonstrates the validity of the lens distortion model and calibration method proposed in this paper.

High-speed real-time augmented reality tracking algorithm model of camera based on mixed feature points

At this stage of augmented reality, simple feature descriptions are mainly used in camera real-time motion tracking, but this is prone to the problem of unstable camera motion tracking. Aiming at the balance between real-time performance and stability, a new method model of real-time camera motion tracking based on mixed features was proposed. By comprehensively using feature points and feature lines as scene features, feature extraction, optimization, and fusion are used to construct hybrid features, and the hybrid features are unified for real-time camera parameter estimation. An image feature optimization method based on scene structure analysis is proposed to meet the computing constraints of mobile terminals. An iterative feature line-screening method is proposed to calculate a stable feature line set, and based on the scene feature composition and feature geometry, a hybrid feature is adaptively constructed to improve the tracking stability of the camera. Based on improved SIFT feature matching target detection and tracking algorithm, a hybrid feature point detection operator detection algorithm is used to achieve rapid feature point extraction, and the speed of descriptor generation is reduced by reducing the feature descriptor vector dimension. The experimental results prove that the proposed target detection and tracking algorithm has good real-time and robustness, and improves the success rate of target detection and tracking.

Source camera identification for re-compressed images: A model perspective based on tri-transfer learning

Source Camera Identification (SCI) achieves high accuracy on matching identification, in which the training and testing sample sets are derived from the same statistical distribution. However, in practice the training and testing sets, namely, the source and test domains, may consist of digital images that are double compressed by various software and applications with different quantization tables. Unfortunately, existing methods are inadequate in performance under such circumstances, such that we aim to find an algorithm that can fill the gap between the training and testing sets. In this work, we propose an algorithm, tri-transfer Learning (TTL), which is a cross-pollination of transfer learning and tri-training. For TTL, the transfer learning module transfers the knowledge learned from the training sets to improve the identification performance on testing. Compared with other methods, TTL uses a semi-supervised approach requiring only a small number of training samples and has better performance than other methods. The tri-training module, which is a variation of the co-training, facilitates knowledge transferring by assigning pseudo-labels to unlabelled instances and adds target instances with labels to the training set in batches. Combining the two modules, our framework can gain higher efficiency and performance than other state-of-art methods on mismatched camera model identification which is supported by experiments based on the open-source Dresden Image Database.

Calibration method for the electrically tunable lens based on shape-changing polymer.

In this paper, a calibration method for the camera system with electrically tunable lens (ETL) based on shape-changing polymer (SCP) is proposed to improve the accuracy, robustness and practicality of the system. The camera model of the ETL based on SCP is proposed based on the analyses of its optical properties. The calibration strategy, including initial estimation of camera parameters and bundle adjustment is presented. To eliminate the influence of temperature on ETL in machine vision applications, a real-time temperature compensation method is proposed. The proposed method makes use of the existing calibration hardware without adding new components to the system. Both simulations and experiments are conducted to evaluate the effectiveness and accuracy of the proposed camera model and calibration method. The measurement error with the proposed calibration method is below 20 microns at high magnification, whose measurement accuracy is improved by five times than the existing method at high magnification. With the proposed calibration method for the camera system with ETL based on SCP, the calibration workload is reduced and accurate calibration at high magnification is achieved. It also benefits the development of autofocusing 3D measurement technology.