Dynamic shock waves induced by high-pulsed current discharge in liquid have wide potential applications in industrial and civil fields, such as rock fragmentation, electrohydraulic cleaning, and oil ...stimulation. Normally, the interelectrode gap distance is small due to the high electrical breakdown field of the liquid. Most of the electrical energy is consumed by the external circuit due to the low impedance of the plasma channel, and the energy transfer efficiency from the electrical energy to the mechanical energy is very low. In this paper, the optimal interelectrode distance and the effect of the plasma channel length on the intensity of the shock wave are investigated. The voltage across the liquid gap, main discharge current through the gap and the pressure wave, as well as the time-resolved images of the plasma channel development and the bubble formation are observed and presented. Results show that the optimal interelectrode distance can generate the strongest intensity of the shock waves. The plasma channel length can be extended by the plasma channel length regulation (PCLR) method, and the deposited energy into the plasma channel is increased. Besides, the energy transfer efficiency can be improved effectively by the PCLR method, and the requirement of the capacitor and switch can be reduced to obtain the same shock wave intensities. Based on the PCLR method, the induced shock wave was applied to increase the permeability of the specimen. It shows that the energy transfer efficiency can be improved effectively based on the presented method, and the shock wave can increase the permeability effectively.
In this paper a binary feature based Loop Closure Detection (LCD) method is proposed, which for the first time achieves higher precision-recall (PR) performance compared with state-of-the-art SIFT ...feature based approaches. The proposed system originates from our previous work Multi-Index hashing for Loop closure Detection (MILD), which employs Multi-Index Hashing (MIH) 1 for Approximate Nearest Neighbor (ANN) search of binary features. As the accuracy of MILD is limited by repeating textures and inaccurate image similarity measurement, burstiness handling is introduced to solve this problem and achieves considerable accuracy improvement. Additionally, a comprehensive theoretical analysis on MIH used in MILD is conducted to further explore the potentials of hashing methods for ANN search of binary features from probabilistic perspective. This analysis provides more freedom on best parameter choosing in MIH for different application scenarios. Experiments on popular public datasets show that the proposed approach achieved the highest accuracy compared with state-of-the-art while running at 30Hz for databases containing thousands of images.
ZnS thin films were deposited from four different zinc salts on glass substrates by chemical bath deposition method. Different anions of zinc salts affect the deposition mechanism and growth rate, ...which influence the properties of the films significantly. The ZnS thin film deposited from ZnSO4 is smoother, thicker, more homogeneous and compact, nearly stoichiometric, comparing with the films deposited from Zn(CH3COO)2 and Zn(NO3)2, and ZnCl2. The scratch test of bonding force between ZnS film and substrate shows that the ZnS film deposited from ZnSO4 has the most excellent adhesion with the substrate. The presence of SO42− promotes heterogeneous ZnS thin film growth via ions by ions deposition, and the films deposited from Zn(CH3COO)2 and Zn(NO3)2 are formed via clusters by clusters deposition. XRD and HRTEM results show that cubic ZnS films are obtained after single deposition, and the grain size of ZnS thin film deposited from ZnSO4 for 2.5h is 10nm. The average transmission of all films is greater than 85% in the wavelength ranging from 600 to 1100nm, and the transmission of films deposited from ZnSO4 or Zn(NO3)2 for 1.5, 2 and 2.5h is greater than 85% in the wavelength varying from 340 to 600nm, which can enhance the blue response. The band gaps of all ZnS thin films are in the range of 3.88–3.99eV. After annealing treatment, the mechanical and optical properties of the ZnS thin film deposited from ZnSO4 are improved significantly.
Recently, 3D scenes parsing with deep learning approaches has been a heating topic. However, current methods with fully-supervised models require manually annotated point-wise supervision which is ...extremely user-unfriendly and time-consuming to obtain. As such, training 3D scene parsing models with sparse supervision is an intriguing alternative. We term this task as data-efficient 3D scene parsing and propose an effective two-stage framework named VIBUS to resolve it by exploiting the enormous unlabeled points. In the first stage, we perform self-supervised representation learning on unlabeled points with the proposed Viewpoint Bottleneck loss function. The loss function is derived from an information bottleneck objective imposed on scenes under different viewpoints, making the process of representation learning free of degradation and sampling. In the second stage, pseudo labels are harvested from the sparse labels based on uncertainty-spectrum modeling. By combining data-driven uncertainty measures and 3D mesh spectrum measures (derived from normal directions and geodesic distances), a robust local affinity metric is obtained. Finite gamma/beta mixture models are used to decompose category-wise distributions of these measures, leading to automatic selection of thresholds. We evaluate VIBUS on the public benchmark ScanNet and achieve state-of-the-art results on both validation set and online test server. Ablation studies show that both Viewpoint Bottleneck and uncertainty-spectrum modeling bring significant improvements. Codes and models are publicly available at https://github.com/AIR-DISCOVER/VIBUS.
Aiming at automatic, convenient and non-instrusive motion capture, this paper presents a new generation markerless motion capture technique, the FlyCap system, to capture surface motions of moving ...characters using multiple autonomous flying cameras (autonomous unmanned aerial vehicles(UAVs) each integrated with an RGBD video camera). During data capture, three cooperative flying cameras automatically track and follow the moving target who performs large-scale motions in a wide space. We propose a novel non-rigid surface registration method to track and fuse the depth of the three flying cameras for surface motion tracking of the moving target, and simultaneously calculate the pose of each flying camera. We leverage the using of visual-odometry information provided by the UAV platform, and formulate the surface tracking problem in a non-linear objective function that can be linearized and effectively minimized through a Gaussian-Newton method. Quantitative and qualitative experimental results demonstrate the plausible surface and motion reconstruction results.
The advance in multimedia technologies have resulted in an explosive growth of pictures in personal computers and in cloud. Typically many pictures taken in the same occasion are similar. The cost to ...store and transmit them can be very significant. Thus it is important to find an efficient method to store these pictures. This paper proposed a compression scheme for similar images. Our approach is to arrange all the similar images into tree structure then apply video coding technique along each branch. To maximize the inter-image correlation between adjacent photos, we consider the minimum spanning tree (MST) subjecting to a maximum depth limit to ensure fast access to all images. This structure is encoded by the latest video coding technique High Efficiency Video Coding (HEVC), which is reported to has advantage in high definition video/image compression. It also supports deleting, adding and modifying images. Experiments show that the proposed method saved 75% space comparing to JPEG format.
We introduce a novel approach to learn geometries such as depth and surface normal from images while incorporating geometric context. The difficulty of reliably capturing geometric context in ...existing methods impedes their ability to accurately enforce the consistency between the different geometric properties, thereby leading to a bottleneck of geometric estimation quality. We therefore propose the Adaptive Surface Normal (ASN) constraint, a simple yet efficient method. Our approach extracts geometric context that encodes the geometric variations present in the input image and correlates depth estimation with geometric constraints. By dynamically determining reliable local geometry from randomly sampled candidates, we establish a surface normal constraint, where the validity of these candidates is evaluated using the geometric context. Furthermore, our normal estimation leverages the geometric context to prioritize regions that exhibit significant geometric variations, which makes the predicted normals accurately capture intricate and detailed geometric information. Through the integration of geometric context, our method unifies depth and surface normal estimations within a cohesive framework, which enables the generation of high-quality 3D geometry from images. We validate the superiority of our approach over state-of-the-art methods through extensive evaluations and comparisons on diverse indoor and outdoor datasets, showcasing its efficiency and robustness.
In this study, we tackle the challenging fine-grained edge detection task, which refers to predicting specific edges caused by reflectance, illumination, normal, and depth changes, respectively. ...Prior methods exploit multi-scale convolutional networks, which are limited in three aspects: (1) Convolutions are local operators while identifying the cause of edge formation requires looking at far away pixels. (2) Priors specific to edge cause are fixed in prediction heads. (3) Using separate networks for generic and fine-grained edge detection, and the constraint between them may be violated. To address these three issues, we propose a two-stage transformer-based network sequentially predicting generic edges and fine-grained edges, which has a global receptive field thanks to the attention mechanism. The prior knowledge of edge causes is formulated as four learnable cause tokens in a cause-aware decoder design. Furthermore, to encourage the consistency between generic edges and fine-grained edges, an edge aggregation and alignment loss is exploited. We evaluate our method on the public benchmark BSDS-RIND and several newly derived benchmarks, and achieve new state-of-the-art results. Our code, data, and models are publicly available at https://github.com/Daniellli/ECT.git.
•The first transformer-based unified architecture tackling fine-grained and generic edge detection.•Embedding learned cause tokens in a decoder for dynamic kernel generation.•A new loss function to ensure the consistency between edge maps from unified network.•Several new benchmarks for the evaluation of fine-grained edge detection.•New SOTA results on a public benchmark and newly benchmarks.
For humans, understanding the relationships between objects using visual signals is intuitive. For artificial intelligence, however, this task remains challenging. Researchers have made significant ...progress studying semantic relationship detection, such as human-object interaction detection and visual relationship detection. We take the study of visual relationships a step further from semantic to geometric. In specific, we predict relative occlusion and relative distance relationships. However, detecting these relationships from a single image is challenging. Enforcing focused attention to task-specific regions plays a critical role in successfully detecting these relationships. In this work, <xref rid="deqn1" ref-type="disp-formula">(1) we propose a novel three-decoder architecture as the infrastructure for focused attention; 2) we use the generalized intersection box prediction task to effectively guide our model to focus on occlusion-specific regions; 3) our model achieves a new state-of-the-art performance on distance-aware relationship detection. Specifically, our model increases the distance F1-score from 33.8% to 38.6% and boosts the occlusion F1-score from 34.4% to 41.2%. Our code is publicly available.
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