Estimating the 6-DoF pose of a camera from a single image relative to a 3D point-set is an important task for many computer vision applications. Perspective-n-point solvers are routinely used for ...camera pose estimation, but are contingent on the provision of good quality 2D-3D correspondences. However, finding cross-modality correspondences between 2D image points and a 3D point-set is non-trivial, particularly when only geometric information is known. Existing approaches to the simultaneous pose and correspondence problem use local optimisation, and are therefore unlikely to find the optimal solution without a good pose initialisation, or introduce restrictive assumptions. Since a large proportion of outliers and many local optima are common for this problem, we instead propose a robust and globally-optimal inlier set maximisation approach that jointly estimates the optimal camera pose and correspondences. Our approach employs branch-and-bound to search the 6D space of camera poses, guaranteeing global optimality without requiring a pose prior. The geometry of SE(3) is used to find novel upper and lower bounds on the number of inliers and local optimisation is integrated to accelerate convergence. The algorithm outperforms existing approaches on challenging synthetic and real datasets, reliably finding the global optimum, with a GPU implementation greatly reducing runtime.
Smartphone-based thermal imaging (SBTI) has been reported in the literature to be an easy-to-use, contactless, cost-friendly alternative to standard imaging modalities in identifying flap ...perforators, monitoring flap perfusion, and detecting flap failure. Our systematic review and meta-analysis aimed to evaluate SBTI's accuracy in perforator identification and secondarily evaluate SBTI's utility in flap perfusion monitoring as well as ability to predict flap compromise, failure, and survival.
Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, a systematic review was performed using PubMed from inception to 2021. Articles were uploaded into Covidence and, following duplicate deletion, were initially screened for use of SBTI in flap procedures through title and abstract screening followed by full-text review. The following data points, if provided, were extracted from each included study: study design, number of patients, patient demographics, perforator number and location, flap number and location, room temperature, cooling method, imaging distance, time from cloth removal, primary (SBTI's accuracy in perforator identification), and secondary outcomes (prediction of flap compromise/failure/survival and cost analysis). Meta-analysis was performed using RevMan v.5.
The initial search yielded 153 articles. Eleven applicable studies with a total of 430 flaps from 416 patients were ultimately included. The SBTI device assessed in all included studies was FLIR ONE. Four studies assessed the SBTI's perforated detection ability and were included in meta-analysis. Smartphone-based thermal imaging correctly identified 378 (93.3%; n = 405) perforators, and computed tomography angiography (CTA) correctly identified 402 (99.2%; n = 402), although in one study SBTI found additional perforators not detected on CTA. A random-effects model was used (I2 = 65%), and no significant difference in perforator detection ability was found between SBTI and CTA (P = 0.27).
This systematic review and meta-analysis supports SBTI as user- and cost-friendly ($229.99), contactless imaging modality with perforator detection ability comparable to current criterion-standard CTA. Postoperatively, SBTI outperformed Doppler ultrasound in early detection of microvascular changes causing flap compromise, allowing for prompt tissue salvage. With a minimal learning curve, SBTI seems to be a promising method of postoperative flap perfusion monitoring able to be used by all hospital ranks. Smartphone-based thermal imaging could thus increase flap monitoring frequency and lower complication rates, although further study is warranted.
The capabilities of drones are increasing every day, as is the ease with which civilians can buy and fly them. Most drones are equipped with a camera that is used by a point-of-view operator and, at ...the same time, can be used for image capture. The use of drones creates a threat to privacy whereby anyone who can fly a drone can take pictures without permission. This study aims to create a 2-axis tracker system that can recognize a drone and locate the position of the drone camera so that a laser beam can track and dazzle the drone camera. The depth-sensing camera is used to localize the part of the target corresponding to the drone’s camera and is created using the YOLOv5 algorithm as a deep-learning detector model. The drone’s camera range and position are challenging to detect due to its small size. Our adaptive detection method combines drone detection and drone camera detection. The depth-sensing camera provides input in the form of a three-coordinate axis from the target. If only the drone is detected, a predictive algorithm can determine the camera’s position for illumination with the laser. Alternatively, if the drone camera is detected, the laser can follow the target’s movement more quickly. In this study, a green (520 nm) laser module with adjustable power is used to investigate factors that affect the dazzling range. The computer vision detection algorithm can detect and localize the position of the drone camera up to 500 cm with a confidence level of more than 65%. If the target is in the center of the field of view, the accuracy of the target position can reach 98%. The tracker can follow the drone’s movement from 2 m/s to 4 m/s with a maximum error of 1.9 cm from the center point of the drone camera for close range. For long range, the maximum error is 6.2 cm. A laser power of 23.5 mW at 500 cm distance is found to be sufficient to dazzle and track drone cameras.
•Drone and drone camera detection based on depth-sensing camera and YOLOv5 algorithm.•The 2-axis real-time tracking system recognizes a drone and locates the position of the drone camera.•Vision disruption based on laser dazzling.•The adaptive detection method combines drone detection and drone camera detection.
The problem of optimizing the choice of parameters for installing a video camera, such as the location and viewing angles, tilt and pan to increase the information content of the generated video ...signal, is considered. The relevance of the paper is due to the lack of methods and programs for automating the process of choosing these parameters. The problem is solved when the pixel density is reached, which is necessary for solving the task of observation. It is based on the proposed model for representing view areas, surveillance and camera locations as discrete sets in accordance with the observation task being solved, which determines the required minimum pixel density as well as selected criteria and restrictions. It gives the opportunity to solve the problem programmatically, unlike existing solutions that use empirical approaches. The main and additional criteria as well as limitations are formulated according to which it is possible to optimize the position of the camera relative to the required surveillance area — the observation task to be solved, the minimum required camera resolution and the maximum information content of the generated image. Algorithms for calculating estimates of the near, far and side boundaries of the view area as well as view angles, pan and tilt are formulated. The adequacy of the proposed model to real areas of observation, review and location of cameras is substantiated. An example of solving an optimization problem is given, which confirms the correctness of using the proposed method. The results obtained make it possible to automate the design process and minimize the influence of the human factor when choosing the location and installation parameters of cameras in the process of designing surveillance systems. The results of the work can be used in the development of algorithms and programs for computer-aided design of surveillance systems.
For moving cameras, the video content changes significantly, which leads to inaccurate prediction in traditional inter prediction and results in limited compression efficiency. To solve these ...problems, first, we propose a camera pose-based background modeling (CP-BM) framework that uses the camera motion and the textures of reconstructed frames to model the background of the current frame. Compared with the reconstructed frames, the predicted background frame generated by CP-BM is more geometrically similar to the current frame in position and is more strongly correlated with it at the pixel level; thus, it can serve as a higher-quality reference for inter prediction, and the compression efficiency can be improved. Second, to compensate the motion of the background pixels, we construct a pixel-level motion vector field that can accurately describe various complex motions with only a small overhead. Our method is more general than other motion models because it has more degrees of freedom, and when the degrees of freedom are decreased, it encompasses other motion models as special cases. Third, we propose an optical flow-based depth estimation (OF-DE) method to synchronize the depth information at the codec, which is used to build the motion vector field. Finally, we integrate the overall scheme into the High Efficiency Video Coding (HEVC) and Versatile Video Coding (VVC) reference software HM-16.7 and VTM-10.0. Experimental results demonstrate that in HM-16.7, for in-vehicle video sequences, our solution has an average Bjøntegaard delta bit rate (BD-rate) gain of 8.02% and reduces the encoding time by 20.9% due to the superiority of our scheme in motion estimation. Moreover, in VTM-10.0 with affine motion compensation (MC) turned off and turned on, our method has average BD-rate gains of 5.68% and 0.56%, respectively.
Having accurate, detailed, and up-to-date information about the location and behavior of animals in the wild would improve our ability to study and conserve ecosystems. We investigate the ability to ...automatically, accurately, and inexpensively collect such data, which could help catalyze the transformation of many fields of ecology, wildlife biology, zoology, conservation biology, and animal behavior into “big data” sciences. Motion-sensor “camera traps” enable collecting wildlife pictures inexpensively, unobtrusively, and frequently. However, extracting information from these pictures remains an expensive, time-consuming, manual task. We demonstrate that such information can be automatically extracted by deep learning, a cutting-edge type of artificial intelligence. We train deep convolutional neural networks to identify, count, and describe the behaviors of 48 species in the 3.2 million-image Snapshot Serengeti dataset. Our deep neural networks automatically identify animals with >93.8% accuracy, and we expect that number to improve rapidly in years to come. More importantly, if our system classifies only images it is confident about, our system can automate animal identification for 99.3% of the data while still performing at the same 96.6% accuracy as that of crowdsourced teams of human volunteers, saving >8.4 y (i.e., >17,000 h at 40 h/wk) of human labeling effort on this 3.2 million-image dataset. Those efficiency gains highlight the importance of using deep neural networks to automate data extraction from camera-trap images, reducing a roadblock for this widely used technology. Our results suggest that deep learning could enable the inexpensive, unobtrusive, high-volume, and even real-time collection of a wealth of information about vast numbers of animals in the wild.
Radar and camera information fusion sensing methods are used to solve the inherent shortcomings of the single sensor in severe weather. Our fusion scheme uses radar as the main hardware and camera as ...the auxiliary hardware framework. At the same time, the Mahalanobis distance is used to match the observed values of the target sequence. Data fusion based on the joint probability function method. Moreover, the algorithm was tested using actual sensor data collected from a vehicle, performing real-time environment perception. The test results show that radar and camera fusion algorithms perform better than single sensor environmental perception in severe weather, which can effectively reduce the missed detection rate of autonomous vehicle environment perception in severe weather. The fusion algorithm improves the robustness of the environment perception system and provides accurate environment perception information for the decision-making system and control system of autonomous vehicles.
The landing camera (LCAM) of Chang’e-4 lander provides a series of low (46 cm/pixel) to high (2.3 cm/pixel) resolution images, which are suitable for centimeter-scale craters. In this paper, we ...analyze the degradation of those small-sized craters to provide detailed information on the local geological evolution of the lunar surface. From the mosaicked descent image, 6316 craters were extracted and classified into four degradation levels based on their morphology on the image: fresh, slightly degraded, moderately degraded, and severely degraded. The ground terrain camera (TCAM) image and the DEM of the Yutu-2 panoramic camera (PCAM) validate the crater degradation levels from a qualitative and quantitative perspective, respectively. The results show that the smaller the size of the craters, the more easily they are degraded. The crater populations in equilibrium in the four study areas indicate that the cumulative size–frequency distribution (SFD) slope is different from previous research results, and the smaller the craters, the more difficult to reach an equilibrium state (for craters smaller than a given size, the production rate is exactly balanced by the removal rate), which may be due to secondary cratering and surface resurfacing caused by the burial of ejecta from neighboring craters.
Nikon has developed various cameras, NIKKOR lenses and accessories, and the technology associated with them, through dialogue with the market, from the film camera era Nikon I(1948)to the latest ...Z-series mirrorless digital cameras.
Portable gamma cameras suitable for intraoperative imaging are in active development and testing. These cameras utilise a range of collimation, detection, and readout architectures, each of which can ...have significant and interacting impacts on the performance of the system as a whole. In this review, we provide an analysis of intraoperative gamma camera development over the past decade. The designs and performance of 17 imaging systems are compared in depth. We discuss where recent technological developments have had the greatest impact, identify emerging technological and scientific requirements, and predict future research directions. This is a comprehensive review of the current and emerging state-of-the-art as more devices enter clinical practice.
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