Limited View Angle Research Articles

Direct voxel classification from x-ray projections for 3D pore detection applied to laser sintered parts

X-ray computed tomography (XCT) is a validated and frequently used tool to verify part geometry and to perform a non-destructive inspection of additive manufacturing parts. However, the acquisition of a large number of x-ray projections generates long inspection times. This conflicts with a high throughput of the production process and hinders the integration of XCT as an in-line quality control procedure for low-end parts. In this paper, we propose a method to obtain three-dimensional (3D) information of the internal pores from a limited view or limited angle scan. The method combines a forward projection model of a cone-beam x-ray system and a deep learning neural network to directly classify each individual voxel, based on the x-ray projections in order to avoid the reconstruction and segmentation step. Accompanying reconstruction artifacts for limited view and limited angle XCT scans are thereby reduced, while preserving 3D information of the pores, defects or inclusions inside the material. The method is validated on real x-ray projections of polymer laser sintered industrial parts and shows a significant reduction in the required x-ray projections, hence acquisition time.

In-plane ESPI with unlimited angle of view for multi-object rotation angle determination

The in-plane electronic speckle pattern interferometry (ESPI), implemented in a Michelson stellar interferometer-like configuration, offers high sensitivity and dynamic measurement. However, its limited angle of view (AOV) remains a major challenge for the rotation angle determination of multiple objects. In this Letter, we analyze the main factors that influence the AOV of the in-plane ESPI and propose an “image transmitting” approach to enlarge the AOV. With the aid of a folded dual-telescope imaging system, we develop an AOV-unlimited interferometer that can determine multi-object rotation angles in real time. The practicability of the interferometer is demonstrated by the application in real-time measuring of the rotation angles of the disks within a 2D granular system.

Collaborative obstacle avoidance algorithm and simulation of swarm robots with limited viewing angle

In this paper, the problem of collaborative obstacle avoidance of swarm robots with limited view angles is studied. In the case of limited view angles, the control model of swarm robots system is designed. The swarm robots can safely avoid a single static obstacle when the obstacle is within the robot’s visual angle. The main research result of this paper is to design a collaborative obstacle avoidance control algorithm for swarm robots, and subdivide the overall control algorithm into three sub algorithms: formation, obstacle avoidance and navigation. The condition of limited view angle is added to the collaborative obstacle avoidance control algorithm of swarm robots, and a control model of collaborative obstacle avoidance of swarm robots with limited view angle is constructed. Then based on the Simulink platform, the simulation is carried out in the case of static obstacle to verify the correctness and stability of the algorithm.

Approximate Evaluation of the Resolution in Near Field Remote Sensing

In linear inverse scattering, the performance of the imaging system is sometimes evaluated in terms of its resolution, i.e., its capability to reconstruct a point-like scatterer. However, there is still a lack of analytical studies on the achievable resolution. To address this, we consider the point spread function (PSF) evaluation of the scattered near field for the single frequency and multi-view/multi-static case in homogeneous medium. Instead of numerically computing the PSF, we propose and discuss an approximate closed form under series expansions according to the angular ranges of both source and receiver location. In order to assess the effectiveness of the proposed approximation, we consider two cases including both full and limited view angles for the incident field and observation ranges. In addition, we provide a localization application to show the usefulness of the theoretical discussion. Numerical results confirmed the analytical investigations.

A new hybrid algorithm integrating genetic algorithm with Tabu search to solve imbalanced k‐coverage problem in directional sensor networks

AbstractThe target coverage problem is considered as one of the major issues in directional sensor networks (DSNs), which is caused by the nature of these networks, including their limited angle of view. Due to the fault tolerance characteristic of some coverage applications, the target coverage is required to be performed using multiple sensors. This challenge is discussed in the literature under the title of k‐coverage problem. Under certain conditions, the number of sensors may suffer some changes due to various factors such as power depletion of the sensors, sensors' malfunctioning, and harshness of the environment. This can result in unavailability of adequate sensors for providing k‐coverage for all targets. The network suffering from such problem is referred to as under‐provisioned network. This paper was aimed at studying such networks by adopting the network conditions to the real environments. To solve this problem, the present paper proposes a hybrid model integrating the genetic algorithm (GA) and Tabu search (TS). The proposed algorithm generally aimed to identify a subset of sensors with appropriate working directions in order to provide a balanced coverage for all the targets available in the network. In order to evaluate the performance of the algorithm several experiments were conducted and the results have been compared with greedy and learning automat‐abased algorithms. . The results of the experiments show the superiority of the algorithm.

The Horror Writing of Strange Tales from Liaozhai

Strange Tales from Liaozhai is a representative work of the Zhiguai Chuanqi of Chinese classical literature, and the writing of horror stories is one of its most essential parts, which contains adequate meaning, showing its rich details and vivid writing characteristics, which also reflects a certain extent of the author, Pu Songling's unique aesthetic and value orientation. This paper analyzes the setting of the time and space environment of the original text, the use of the third-person limited angle of view in the narrative, and the selection of horror images, respectively, from the scene of the incident, the subject of experience and the object of experience in the horror story. This paper analyzes the original texts of Strange Tales from Liaozhai by summarizing writing techniques of horror stories in work to explore the process of creating the sense of horror in Strange Tales from Liaozhai and how the author uses the original source of human fear to create a horror story. It reveals the alienation and abnormality of time and space, the immersive sense of subjective, limited perspective, and the deadly threat behind horrific imagery. While appreciating the artistic beauty and realizing the diversity of stories types of this work, its implicit humanistic concern is not ignored.

Complex Valued Long Short Term Memory Based Architecture for Frequency Domain Photoacoustic Imaging

Frequency domain photoacoustic (FDPA) imaging has great potential in a clinical setting compared to time-domain photoacoustic imaging due to its reduced cost and small form factors. However FDPA system struggles with lower signal to noise ratio, necessitating the need for advanced image reconstruction methods. Most of the image reconstruction approaches in FDPA imaging are based on analytical or model based schemes. Very less emphasis has been placed on developing deep learning based approaches for FDPA imaging. In this work, a image translation network was developed with the ability to directly map from sinogram data (complex-valued) to the initial pressure rise distribution (real-valued) for FD-PA imaging. This architecture was based on a Long Short Term Memory (LSTM) backbone (with adjoined real and imaginary parts of the complex sinogram data as input) followed by a fully connected layer, which is then passed through a convolution and transposed convolution layer pair. The result of the FDPA-LSTM architecture was compared with direct translational networks based on ResNet, UNet and AUTOMAP and found to have an improvement of about 15% in terms of PSNR and 10% in terms of SSIM with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$150^{\circ }$</tex-math></inline-formula> data acquisition limited-view angle. Further, the FDPA-LSTM was also compared with post-processing UNet architecture on backprojection and Tikhonov regularized reconstruction. A 20% improvement in terms of PSNR with backprojection and post-processing UNet was observed. Further FDPA-LSTM had similar performance as Tikhonov and post-processing UNet (with 75 times acceleration). The developed scheme will indeed be very useful for achieving accelerated and accurate frequency domain photoacoustic imaging.

Markerless Radio Frequency Indoor Monitoring for Telemedicine: Gait Analysis, Indoor Positioning, Fall Detection, Tremor Analysis, Vital Signs and Sleep Monitoring.

Quantitative indoor monitoring, in a low-invasive and accurate way, is still an unmet need in clinical practice. Indoor environments are more challenging than outdoor environments, and are where patients experience difficulty in performing activities of daily living (ADLs). In line with the recent trends of telemedicine, there is an ongoing positive impulse in moving medical assistance and management from hospitals to home settings. Different technologies have been proposed for indoor monitoring over the past decades, with different degrees of invasiveness, complexity, and capabilities in full-body monitoring. The major classes of devices proposed are inertial-based sensors (IMU), vision-based devices, and geomagnetic and radiofrequency (RF) based sensors. In recent years, among all available technologies, there has been an increasing interest in using RF-based technology because it can provide a more accurate and reliable method of tracking patients' movements compared to other methods, such as camera-based systems or wearable sensors. Indeed, RF technology compared to the other two techniques has higher compliance, low energy consumption, does not need to be worn, is less susceptible to noise, is not affected by lighting or other physical obstacles, has a high temporal resolution without a limited angle of view, and fewer privacy issues. The aim of the present narrative review was to describe the potential applications of RF-based indoor monitoring techniques and highlight their differences compared to other monitoring technologies.

Approaching closed spherical, full-view detection for photoacoustic tomography.

.Significance: Photoacoustic tomography (PAT) is a widely explored imaging modality and has excellent potential for clinical applications. On the acoustic detection side, limited-view angle and limited-bandwidth are common key issues in PAT systems that result in unwanted artifacts. While analytical and simulation studies of limited-view artifacts are fairly extensive, experimental setups capable of comparing limited-view to an ideal full-view case are lacking.Aims: A custom ring-shaped detector array was assembled and mounted to a 6-axis robot, then rotated and translated to achieve up to steradian view angle coverage of an imaged object.Approach: Minimization of negativity artifacts and phantom imaging were used to optimize the system, followed by demonstrative imaging of a star contrast phantom, a synthetic breast tumor specimen phantom, and a vascular phantom.Results: Optimization of the angular/rotation scans found effective detectors were needed for high-quality images, while 15-mm steps were used to increase the field of view as required depending on the size of the imaged object. Example phantoms were clearly imaged with all discerning features visible and minimal artifacts.Conclusions: A near full-view closed spherical system has been developed, paving the way for future work demonstrating experimentally the significant advantages of using a full-view PAT setup.

Iterative tomography of pipes during operation

The pipe wall thickness was estimated based on three-dimensional images of the pipe recovered from several X-ray projections, which were made in a limited angle of view. Since the effects of scattered radiation and beam hardening are up to 50 % of the main radiation, ignoring them leads to blur of the image and inaccuracy in determining dimensions. To restore pipe images from projections, a volume and/or shell representation of the pipe is used, as well as iterative Bayesian methods. Using these methods, the error in estimating the pipe wall thickness from the projection data can be equal to or less than 300 μm. It has been shown that standard X-ray projections on the film or imaging plates used to obtain data can be used to restore pipe wall thickness profiles in factory conditions.

Adaptive Finite-Time Tracking Control of Nonholonomic Multirobot Formation Systems With Limited Field-of-View Sensors.

This article studies the vision-based tracking control problem for a nonholonomic multirobot formation system with uncertain dynamic models and visibility constraints. A fixed onboard vision sensor that provides the relative distance and bearing angle is subject to limited range and angle of view due to limited sensing capability. The constraint resulting from collision avoidance is also taken into account for safe operations of the formation system. Furthermore, the preselected specifications on transient and steady-state performance are provided by considering the time-varying and asymmetric constraint requirements on formation tracking errors for each robot. To address the constraint problems, we incorporate a novel barrier Lyapunov function into controller design and analysis. Based on the recursive adaptive backstepping procedure and neural-network approximation, we develop a vision-based formation tracking control protocol such that formation tracking errors can converge into a small neighborhood of the origin in finite time while meeting the requirements of visibility and performance constraints. The proposed protocol is decentralized in the sense that the control action on each robot only depends on the local relative information, without the need for explicit network communication. Moreover, the control protocol could extend to an unconstrained multirobot system. Both simulation and experimental results show the effectiveness of the control protocol.

Fixed-Time Formation Control of Unicycle-Type Mobile Robots With Visibility and Performance Constraints

This article presents a fixed-time leader-follower formation control protocol for a swarm of unicycle-type mobile robots with visibility and performance constraints. Under a vision-based formation control framework, mobile robots calculate their control signals with only the local relative distance and bearing angle provided by onboard stereo or RGB-D cameras. Since the sensing capability of the fixed onboard cameras is subject to limited range and angle of view, the field-of-view constraints are considered to ensure that each follower can always detect its leader and meanwhile avoid collision with the leader. The time-varying and asymmetric performance constraints on formation tracking errors are applied to guarantee the transient and stead-state performance. Furthermore, the proposed control protocol can be extended to address the control design problem without constraint requirements. Based on the fixed-time control design and Lyapunov analysis, formation tracking errors are shown to converge to a small neighborhood of zero in fixed settling time. Simulation and experiment studies are performed to illustrate the effectiveness of the formation control protocol.

Adaptive Leader–Follower Formation Control of Underactuated Surface Vehicles With Guaranteed Performance

This article studies the formation tracking control problem for a group of underactuated surface vehicles with guaranteed transient properties, including connectivity maintenance, collision avoidance, and tracking performance specifications. The formation is within the leader–follower control framework, in which every follower is controlled to track its leader and maintain a desired relative distance and bearing angle with respect to its leader such that the prescribed formation geometry is achieved based on local sensing capability. The onboard sensor systems are of limited range and angle of view, thus defining a cone of detectable region for every follower. Each follower can detect its leader, if and only if the relative distance and bearing angle keep always inside the predefined detectable region such that the connectivity between the follower and its leader is maintained over time. In addition to the consideration of connectivity maintenance, no collision between the follower and its leader is also considered. A transverse function control approach is introduced to overcome the difficulties caused by the off-diagonal system matrix and underactuation. The barrier Lyapunov function and adaptive backstepping procedure are incorporated into the formation control design to achieve the boundedness of the closed-loop systems with guaranteed transient performance. Collision avoidance and connectivity maintenance between every follower and its leader are also proven mathematically. Simulation studies are performed to show the effectiveness of the proposed control design technique.

Negativity artifacts in back-projection based photoacoustic tomography

Photoacoustic tomography (PAT) has become a fast-evolving biomedical imaging modality in recent years. In PAT, image reconstruction is a critical step to produce high-quality photoacoustic images from raw photoacoustic signals. To date, algorithms based on back projection are the most widely used image reconstruction techniques due to their simplicity and computational efficiency. However, images reconstructed by back projection contain negative values, especially at the edge of photoacoustic sources, which have no physical meaning and are essentially undesired artifacts. In this work, we study the formation mechanism, fundamental causes and removal strategies of the negativity artifacts in back-projection-based PAT. Our results show that limited detector bandwidth and limited view angle are two fundamental causes of negativity artifacts. When the bandwidth of a detector is limited, back-projection signals will be distorted as a result of the loss of frequency contents and negativity artifacts thus appear. When the view angle of the detector is limited, photoacoustic signals propagating in three-dimensional space will be partially lost, resulting in negativity artifacts. Post-processing strategies, such as envelope detection and forced zeroing can be used to remove the negativity artifacts but may cause problems. Since negativity artifacts are a common image quality degradation factor in PAT, understanding their characteristics may expedite the development of novel artifact-removal techniques and artifact-free image reconstruction algorithms, which are of importance to correct image interpretation and quantitative imaging.

Integrating Intervehicular Communications, Vehicle Localization, and a Digital Map for Cooperative Adaptive Cruise Control with Target Detection Loss

Adaptive Cruise Control (ACC) is an Advanced Driver Assistance System (ADAS) that enables vehicle following with desired inter-vehicular distances. Cooperative Adaptive Cruise Control (CACC) is upgraded ACC that utilizes additional inter-vehicular wireless communication to share vehicle states such as acceleration to enable shorter gap following. Both ACC and CACC rely on range sensors such as radar to obtain the actual inter-vehicular distance for gap-keeping control. The range sensor may lose detection of the target, the preceding vehicle, on curvy roads or steep hills due to limited angle of view. Unfavourable weather conditions, target selection failure, or hardware issue may also result in target detection loss. During target detection loss, the vehicle following system usually falls back to Cruise Control (CC) wherein the follower vehicle maintains a constant speed. In this work, we propose an alternative way to obtain the inter-vehicular distance during target detection loss to continue vehicle following. The proposed algorithm integrates inter-vehicular communication, accurate vehicle localization, and a digital map with lane center information to approximate the inter-vehicular distance. In-lab robot following experiments demonstrated that the proposed algorithm provided desirable inter-vehicular distance approximation. Although the algorithm is intended for vehicle following application, it can also be used for other scenarios that demand vehicles' relative distance approximation. The work also showcases our in-lab development effort of robotic emulation of traffic for connected and automated vehicles.

Reconstructing Normal Section Profiles of 3-D Revolving Structures via Pose-Unconstrained Multiline Structured-Light Vision

Reconstructing normal section profiles is an effective approach at monitoring the shape of a 3-D revolving-symmetry structure. The existing reconstruction methods typically require a sensor to work in a constrained pose and thus suffer from poor flexibility and limited view-angle. This article proposes a pose-unconstrained normal section profile reconstruction framework for 3-D revolving-symmetry structures, which estimates the normal section profile using multiple 3-D general section profiles acquired by a multiline structured light vision sensor. First, we establish a model to estimate the revolving axis and calculate the normal section profile using corresponding points. Then, the model is embedded into an iterative algorithm to optimize the corresponding points and calculate the accurate normal section profile. Simulations show that our algorithm is applicable to 3-D revolving structures of various shapes and is robust against noise and local surface defects. Real experiments were conducted on reconstructing the normal section profile of a 3-D wheel. The results demonstrate that our algorithm reaches the mean precision of 0.065 mm and repeatability with the STD of 0.007 mm. It is also robust to pose and position of the sensor.

Optimum LED coverage utilization in OCC for effective communication with mobile robot

Optical camera communication (OCC) is a promising technology for camera-mounted mobile robots (MRs), where it can use its camera to communicate with light-emitting diode (LED) infrastructures. Due to the limited angle of view of current commercial cameras used in MRs, the cameras cannot detect LED everywhere inside an area illuminated by an LED; hence, much of the illumination surface remains unutilized. Herein, we propose an automatic camera inflection algorithm to successfully detect LED anywhere inside an LED cell (the total coverage area illuminated by LED). The inflection mechanism is a supervised learning approach. The technique utilizes the coordinate information (i.e., X and Y coordinates) of an LED cell, which is further employed to track the position of the LED. The coordinate data is automatically updated when the MR travels from one position to another. Furthermore, we perform simulations that include a discussion on tilt angle variation with increasing distance traveled by the MR and a performance analysis that demonstrates the importance and feasibility of the developed algorithm. Finally, a testbed prototype for OCC is implemented to demonstrate the proof of angle measurement theory and bit-error-rate performance of the proposed system.

Constructing big panorama from video sequence based on deep local feature

Constructing high-quality panorama is a fundamental task in both computer vision and computer graphics communities, thus, leading to many image stitching approaches for panorama construction. However, panorama constructed by traditional image stitching has a limited angle of view and has an expensively computational cost. To defend the issues, in this paper, we proposed to use video sequence as input for constructing big panorama, resulting in a high-quality panoramic image, the presented method is built on the stabilized video and robust feature tracking method. Specifically, the input video sequences are captured by moving hand cameras which can be any type of consumer-level camera. To mitigate the affections from rolling shutters in videos, a novel video stabilization method is introduced to filter the unstable camera's path, then resulting in a stabilized video for panorama construction. Additionally, a deep local feature-based feature tracking method is proposed to produce feature correspondences between consecutive video frames for camera motion estimation used in both video stabilization and image stitching. Finally, a comprehensive experiment conducted on the benchmarking datasets is presented to demonstrate the effectiveness of the proposed method.

Intelligent video analysis: A Pedestrian trajectory extraction method for the whole indoor space without blind areas

Pedestrian trajectory extraction is an important part of intelligent monitoring, which is of great significance to many fields such as statistics on pedestrian flow and density, population behavior analysis, abnormal behavior detection, etc. However, it is quite challenging to extract pedestrian trajectory without blind areas in the whole space due to the limited view angle of ordinary cameras. So far, no efficient method has been proposed to deal with this problem. In this paper, we propose a pedestrian trajectory extraction method based on a single fisheye camera, which can realize no blind areas pedestrian trajectory extraction in the whole interior space. First, the fisheye camera with a perspective of 180∘ is adopted in our work which can realize the entire space monitoring without blind areas and avoid object matching among multiple cameras. Then, the deep convolutional neural network, the Kalman Filter algorithm, and the Hungarian algorithm are combined for pedestrian head detection and tracking. In order to calculate the coordinates of the trajectory points according to the obtained head position, we propose a novel pedestrian height estimation method for fisheye cameras. Finally, the pedestrian trajectory points are calculated based on the detected head position and the estimated height. The performance of the proposed pedestrian trajectory extraction method has been evaluated by a variety of experiments. The experimental results show that the trajectories of multiple pedestrians can be extracted simultaneously through the method proposed in this paper, and the average error of the trajectory points is less than 5.07 pixels in the 512×512 images.

An Enhanced SMART-RECON Algorithm for Time-Resolved C-Arm Cone-Beam CT Imaging.

Temporal resolution in time-resolved cone-beam CT (TR-CBCT) imaging is often limited by the time needed to acquire a complete data set for image reconstruction. With the recent developments of performing nearly limited-view artifact-free reconstruction from data in a limited-view angle range and a prior image, temporal resolution of TR-CBCT imaging can be improved. One such an example is the use of Simultaneous Multiple Artifacts Reduction in Tomographic RECONstruction (SMART-RECON) [1] technique. However, with SMART-RECON, one can only improve temporal resolution up to 1 frame per second (fps) which is an improvement of 4.5 times over that of the conventional FBP reconstruction. In this paper, a new technique referred to as enhanced SMART-RECON (eSMART-RECON) was introduced to enhance the temporal performance of SMART-RECON in a multi-sweep CBCT data acquisition protocol. Both numerical simulation studies with ground truth and in vivo human subject studies using C-arm CBCT acquisition systems were conducted to demonstrate the following key results: for a multi-sweep CBCT acquisition protocol, eSMART-RECON enables 4-7.5 fps temporal resolution for TR-CBCT which is 4-7.5 times better than that offered by the original SMART-RECON, and 18-34 times better than that offered by the conventional FBP reconstruction.