The rendezvous and formation problem is a significant part for the unmanned aerial vehicle(UAV) autonomous aerial refueling(AAR) technique. It can be divided into two major phases: the long-range guidance phase a...
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The rendezvous and formation problem is a significant part for the unmanned aerial vehicle(UAV) autonomous aerial refueling(AAR) technique. It can be divided into two major phases: the long-range guidance phase and the formation phase. In this paper, an iterative computation guidance law(ICGL) is proposed to compute a series of state variables to get the solution of a control variable for a UAV conducting rendezvous with a tanker in AAR. The proposed method can make the control variable converge to zero when the tanker and the UAV receiver come to a formation flight eventually. For the long-range guidance phase, the ICGL divides it into two sub-phases: the correction sub-phase and the guidance sub-phase. The two sub-phases share the same iterative process. As for the formation phase, a velocity coordinate system is created by which control accelerations are designed to make the speed of the UAV consistent with that of the *** simulation results demonstrate that the proposed ICGL is effective and robust against wind disturbance.
Surveillance systems have become increasingly ubiquitous, which has led to a requirement to detect anomalies for efficiently preventing terrorism and reducing crimes. The increasing number of surveillance networks has...
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Surveillance systems have become increasingly ubiquitous, which has led to a requirement to detect anomalies for efficiently preventing terrorism and reducing crimes. The increasing number of surveillance networks has imposed major technical challenges on intelligent anomaly detection because of the inconsistent appearance of pedestrians owing to posture deformation and clutter in video
As a classic topic in computer graphics, the semantic part segmentation of 3D data is helpful for3D part-level editing and modeling. Single-views point cloud is the raw format of 3D data. Giving each point a semantic ...
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As a classic topic in computer graphics, the semantic part segmentation of 3D data is helpful for3D part-level editing and modeling. Single-views point cloud is the raw format of 3D data. Giving each point a semantic annotation in single-view point cloud, i.e., single-view point cloud semantic part segmentation, is meaningful and challenging.
During short-range air combat involving unmanned aircraft vehicle(UAV) swarms, UAVs must make accurate maneuver decisions based on information from both enemy and friendly UAVs. This dual requirement of competition an...
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During short-range air combat involving unmanned aircraft vehicle(UAV) swarms, UAVs must make accurate maneuver decisions based on information from both enemy and friendly UAVs. This dual requirement of competition and cooperation presents a significant challenge in the field of unmanned air combat. In this paper, a method based on multi-agent reinforcement learning(MARL) is proposed to address this issue. An actor network containing three subnetworks that can handle different types of situational information is designed. Hence, the results from simpler one-on-one scenarios are leveraged to enhance the complex swarm air combat training process. Separate state spaces for local and global information are designed for the actor and critic networks. A detailed reward function is proposed to encourage *** prevent lazy participants in air combat, a reward assignment operation is applied to distribute these dense rewards. Simulation testing and ablation experiments demonstrate that both the transfer operation and reward assignment operation can effectively deal with the swarm air combat scenario, and reflect the effectiveness of the proposed method.
As a traditional and fundamental research area in computer graphics,3D shape segmentation has provided large support to many other 3D processing technologies.3D shape co-segmentation,which together with model sets,co-...
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As a traditional and fundamental research area in computer graphics,3D shape segmentation has provided large support to many other 3D processing technologies.3D shape co-segmentation,which together with model sets,co-segments multiple 3D shape parts among the same category,has been attracting significant attention to fully utilize the common features of model sets.
Video portrait segmentation(VPS), aiming at segmenting prominent foreground portraits from video frames, has received much attention in recent years. However, the simplicity of existing VPS datasets leads to a limitat...
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Video portrait segmentation(VPS), aiming at segmenting prominent foreground portraits from video frames, has received much attention in recent years. However, the simplicity of existing VPS datasets leads to a limitation on extensive research of the task. In this work, we propose a new intricate large-scale multi-scene video portrait segmentation dataset MVPS consisting of 101 video clips in 7 scenario categories,in which 10843 sampled frames are finely annotated at the pixel level. The dataset has diverse scenes and complicated background environments, which is the most complex dataset in VPS to our best *** the observation of a large number of videos with portraits during dataset construction, we find that due to the joint structure of the human body, the motion of portraits is part-associated, which leads to the different parts being relatively independent in motion. That is, the motion of different parts of the portraits is imbalanced. Towards this imbalance, an intuitive and reasonable idea is that different motion states in portraits can be better exploited by decoupling the portraits into parts. To achieve this, we propose a part-decoupling network(PDNet) for VPS. Specifically, an inter-frame part-discriminated attention(IPDA)module is proposed which unsupervisedly segments portrait into parts and utilizes different attentiveness on discriminative features specified to each different part. In this way, appropriate attention can be imposed on portrait parts with imbalanced motion to extract part-discriminated correlations, so that the portraits can be segmented more accurately. Experimental results demonstrate that our method achieves leading performance with the comparison to state-of-the-art methods.
Object localization in 3D from 2D images is an important computer vision problem that enables modern robotic vision systems to interact properly with the objects present in the real world. Owing to its significance, t...
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Object localization in 3D from 2D images is an important computer vision problem that enables modern robotic vision systems to interact properly with the objects present in the real world. Owing to its significance, techniques for recovering the 6-DOF pose and dimensions of objects from images has received increasing attention in recent years [1–3]. However, to the best of our knowledge,the existing approaches all focus on object-level inference instead of analyzing object parts. Most of
The returning of homing pigeons to their lofts from remote and unfamiliar locations with great accuracy remains a mystery. Pigeon-inspired optimization(PIO), which is a novel mono-objective continuous optimization alg...
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The returning of homing pigeons to their lofts from remote and unfamiliar locations with great accuracy remains a mystery. Pigeon-inspired optimization(PIO), which is a novel mono-objective continuous optimization algorithm, is inspired by the hidden mechanism behind the remarkable navigation capacity of homing pigeons. Since their development, PIO and its variants have been widely applied to various fields ranging from combinatorial optimization to multi-objective optimization in many areas, such as aerospace,medicine, and energy. This study aims to review the modifications of PIO from four aspects of improvement measures, namely, component replacement, operation addition, structure adjustment, and application expansion. It also summarizes the problems of existing research and plots the course of future effort.
Despite the long history of medical simulations, suffering from the patient-specific heterogeneous heart physiological structure and complex intravascular procedures, it is still challenging for patient-specific percu...
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Despite the long history of medical simulations, suffering from the patient-specific heterogeneous heart physiological structure and complex intravascular procedures, it is still challenging for patient-specific percutaneous coronary intervention(PCI) surgery simulation. In this paper, we advocate a haptics-equiped interactive prototype system towards PCI surgeons training and patient-specific surgery rehearsing, which can afford trainees the opportunity to approximately experience the entire PCI procedures and customized emergency cases that might occur in common clinical settings. The full simulation covers tissue deformation, catheter and wire simulation, X-ray simulation, haptic feedback, and 3D realistic rendering, which in all give rise to the integrated physical, visual, haptic, and procedural realism. Our system can accommodate various comprehensive operations involved in PCI-related procedures, including feeding wires, releasing stents, injecting contrast medium, simulating X-ray, bleeding, etc. Moreover, our system framework is fully built upon CUDA, and thus can achieve real-time performance even on a common desktop. The high-fidelity, real-time efficiency and stableness of our system show great potentials for its practical applications in clinical training fields.
Automatic image completion can provide convenient editing of consumer *** image completion methods find the best patch locally and then copy it to the hole region for texture *** the best patch is fixed in size,it is ...
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Automatic image completion can provide convenient editing of consumer *** image completion methods find the best patch locally and then copy it to the hole region for texture *** the best patch is fixed in size,it is hard to adapt these methods efciently either to various patterns or to content ***,salient image structures can be estimated and propagated to guide the texture inpainting process for more plausible *** paper presents a novel image completion method using structure guided texture *** do not require any interactions to achieve automatic two-stage image *** the structure completion stage,the salient structures around the hole region are detected,and then the missing structure curves are completed with Euler *** textures along the structure curves are completed through belief *** the texture inpainting stage,we propose multi-scale patches and global optimization to inpaint the remaining texture in the hole regions guided by the completed ***,with defined patch sizes,the hole region is divided into lattice patches,making it possible for multiple patch sizes to render multiscale descriptions of this image.A multi-scale graph is then built for the hole region and formulated as a posterior probability ***,using a simulated annealing based Markov chain Monte Carlo method,an inference algorithm is designed to find a global optimization solution for the posterior probability *** experiments show that our method can automatically complete the hole region and preserve plausible structure shapes of existing ones in various *** texture inpainting results are more convincing with guidance from the completed structures,and our method can guarantee and accelerate convergence of the global optimization.
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