Search results

The purpose of this paper is to present an image based method for estimating the 3D motion of rigid particles from high‐speed video footage (HSV). The computed motion can be used as either a means to generate quantitative feedback for a process or to validate the accuracy of discrete element method (DEM) simulation models.

Experiments consist of a diamond impacting an angled plate and video is captured at 4,000 frames per second. Simple image analysis is used to track the particle in each frame and to extract its 2D silhouette boundary. Using an approximate 3D model of the particle generated from a multi‐camera setup, a pose estimation scheme based on silhouette consistency is used in conjunction with a rigid body model to compute the 3D motion.

Under reasonable conditions, the method can reliably estimate the linear and angular motion of the particle to within 1 per cent of their true values.

As an example application, we demonstrate how the method can be used to validate DEM simulations of simple impact experiments captured with HSV, providing valuable insight towards further development. In particular, we investigate the effects of shape representation through sphere‐clumping and the applicability of different contact models.

The novelty of our method is its ability to accurately compute the motion associated with a real world interaction, such as an impact, which provides numerical ground truth at an individual particle level. While similar schemes have been attempted with ideal particles (e.g. spheres), the resulting models do not naturally extend to realistic particle shapes. Since our method can track real particles, real‐world processes can be better quantified.

360-degree video is recorded with omnidirectional or multi-camera systems that capture all directions at the same time in a spherical view. With immersive technologies gaining momentum and reducing educational cost, it has attracted the interest of the academic community. However, little is known about using 360-degree video in teacher education. The purpose of this study is to conduct a systematic scoping review through a systematic process based on 15 included studies to determine the characteristics, impacts, strengths and weaknesses of the 360-degree video applied to teacher education.

This study combines scoping and systematic review based on the PRISMA paradigm.

This paper explores that 360-degree videos are applicable to teacher education, specifically with their positive effects on pre-service teachers’ immersion, noticing, reflection and interpersonal competence. However, as for learners’ reactions, physical discomfort is reported, like motion sickness.

First, some recently published studies on the subjects were partially accessible, which precluded the authors from adding their findings to this study. Second, the sample of articles is constrained to the search and selection strategies described in the methods section, which increases the possibility that pertinent research may be omitted. Furthermore, this study’s summary of the selected research may be inadequate. Third, only English-language publications were included in this study. Future researchers can expand on this topic by gathering additional relevant empirical data from publications in other languages.

Practically, findings in this study reveal the positive effects of 360-degree video in teacher education. The results may help researchers and preservice teachers better understand 360-degree video and use it more frequently in teaching. Instructional video technologies have been found to have a nearly medium effect on learning effectiveness in educational practice from a broader perspective.

The findings in this study can shed light on future educational technology research on instructional video technologies and technology-enhanced teacher education.

The purpose of this paper is to examine the interaction effect of innovative product category and presentation order on consumer consumer’s purchasing intention and the mediating role of perceived novelty and risk perception.

The authors examined the hypotheses in three experiment studies. In Study 1, the authors primed innovative product category and presentation order on consumer consumer’s purchasing intention. In Study 2, the authors measured the mediating role of perceived novelty and risk perception. In Study 3, they validated the moderating effect of picture and text consistency on the improvement of purchase preference.

The results reveal that RNP/INP and presentation order (from whole to part/from part to whole) could enhance consumers’ purchase intention and verify the mediating role of perceived novelty and risk perception, based on which a complete internal mechanism model is constructed. The third experiment shows the moderating effect of picture and text consistency on the improvement of purchase preference by matching the category and presentation order of innovative products.

Prior literature on the thinking mode of holistic and partial processing has been mostly applied to the cognitive field of reading and text labeling. In this study, using the holistic (local) processing thinking model and anchoring theory, eye movement experiments and situational experiments, the audience’s analysis framework of information processing mechanism is constructed. The unique phenomenon of product category and overall (local) presentation order coexisting in innovative product advertisement is considered comprehensively.

The calibration is a key but cumbersome process for 3D body scanning using multiple depth cameras. The purpose of this paper is to simplify the calibration process by introducing a new method to calibrate the extrinsic parameters of multiple depth cameras simultaneously.

An improved method is introduced to enhance the accuracy based on the virtual checkerboards. Laplace coordinates are employed for a point-to-point adjustment to increase the accuracy of scanned data. A system with eight depth cameras is developed for full-body scanning, and the performance of this system is verified by actual results.

The agreement of measurements between scanned human bodies and the real subjects demonstrates the accuracy of the proposed method. The entire calibration process is automatic.

A complete algorithm for a full human body scanning system is introduced in this paper. This is the first publically study on the refinement and the point-by-point adjustment based on the virtual checkerboards toward the scanning accuracy enhancement.

Monitoring of the quality of precast concrete (PC) components is crucial for the success of prefabricated construction projects. Currently, quality monitoring of PC components during the construction phase is predominantly done manually, resulting in low efficiency and hindering the progress of intelligent construction. This paper presents an intelligent inspection method for assessing the appearance quality of PC components, utilizing an enhanced you look only once (YOLO) model and multi-source data. The aim of this research is to achieve automated management of the appearance quality of precast components in the prefabricated construction process through digital means.

The paper begins by establishing an improved YOLO model and an image dataset for evaluating appearance quality. Through object detection in the images, a preliminary and efficient assessment of the precast components' appearance quality is achieved. Moreover, the detection results are mapped onto the point cloud for high-precision quality inspection. In the case of precast components with quality defects, precise quality inspection is conducted by combining the three-dimensional model data obtained from forward design conversion with the captured point cloud data through registration. Additionally, the paper proposes a framework for an automated inspection platform dedicated to assessing appearance quality in prefabricated buildings, encompassing the platform's hardware network.

The improved YOLO model achieved a best mean average precision of 85.02% on the VOC2007 dataset, surpassing the performance of most similar models. After targeted training, the model exhibits excellent recognition capabilities for the four common appearance quality defects. When mapped onto the point cloud, the accuracy of quality inspection based on point cloud data and forward design is within 0.1 mm. The appearance quality inspection platform enables feedback and optimization of quality issues.

The proposed method in this study enables high-precision, visualized and automated detection of the appearance quality of PC components. It effectively meets the demand for quality inspection of precast components on construction sites of prefabricated buildings, providing technological support for the development of intelligent construction. The design of the appearance quality inspection platform's logic and framework facilitates the integration of the method, laying the foundation for efficient quality management in the future.

The purpose of this paper is to propose a novel method of coaxial optical precision alignment based on surface roughness and reflectiveness matching.

The micro-assembly experiment system set-up was constructed according to the principle of the coaxial optical alignment. The coaxial optical alignment error is theoretically analyzed and calculated. When the prism orthogonal alignment mechanism produces the error of 0.001°, the theoretical deviation was less than 0.87 μm and the actual experimental micro-assembly platform assembly accuracy exceeded 3 μm. A peg-in-hole precise assembly of punching pin micro-assembly experiment was done in order to validate feasibility of this method.

The results indicate that coaxial optical precision alignment could be used for the assembly of complex micro-heterogeneous system which is integrated by similar devices, such as 3D complex micro-structures, silicon micro-electro-mechanical system (MEMS) devices and non-silicon MEMS devices with flat structure.

The paper provides certain methodological guidelines for MEMS for high precision automatic assembly of complex 3D micro-structures.

The growth of police operated surveillance cameras has out-paced the ability of humans to monitor them effectively. Computer vision is a possible solution. An ongoing research project on the application of computer vision within a municipal police department is described. The paper aims to discuss these issues.

Following the demystification of computer vision technology, its potential for police agencies is developed within a focus on computer vision as a solution for two common surveillance camera tasks (live monitoring of multiple surveillance cameras and summarizing archived video files). Three unaddressed research questions (can specialized computer vision applications for law enforcement be developed at this time, how will computer vision be utilized within existing public safety camera monitoring rooms, and what are the system-wide impacts of a computer vision capability on local criminal justice systems) are considered.

Despite computer vision becoming accessible to law enforcement agencies the impact of computer vision has not been discussed or adequately researched. There is little knowledge of computer vision or its potential in the field.

This paper introduces and discusses computer vision from a law enforcement perspective and will be valuable to police personnel tasked with monitoring large camera networks and considering computer vision as a system upgrade.

The purpose of this paper is to provide a comprehensive review of a non-contact full-field optical measurement technique known as digital image correlation (DIC).

The approach of this review paper is to introduce the research pertaining to DIC. It comprehensively covers crucial facets including its principles, historical development, core challenges, current research status and practical applications. Additionally, it delves into unresolved issues and outlines future research objectives.

The findings of this review encompass essential aspects of DIC, including core issues like the subpixel registration algorithm, camera calibration, measurement of surface deformation in 3D complex structures and applications in ultra-high-temperature settings. Additionally, the review presents the prevailing strategies for addressing these challenges, the most recent advancements in DIC applications across quasi-static, dynamic, ultra-high-temperature, large-scale and micro-scale engineering domains, along with key directions for future research endeavors.

This review holds a substantial value as it furnishes a comprehensive and in-depth introduction to DIC, while also spotlighting its prospective applications.

Presently, 6 Degree of Freedom (6DOF) visual pose measurement methods enjoy popularity in the industrial sector. However, challenges persist in accurately measuring the visual pose of blank and rough metal casts. Therefore, this paper introduces a 6DOF pose measurement method utilizing stereo vision, and aims to the 6DOF pose measurement of blank and rough metal casts.

This paper studies the 6DOF pose measurement of metal casts from three aspects: sample enhancement of industrial objects, optimization of detector and attention mechanism. Virtual reality technology is used for sample enhancement of metal casts, which solves the problem of large-scale sample sampling in industrial application. The method also includes a novel deep learning detector that uses multiple key points on the object surface as regression objects to detect industrial objects with rotation characteristics. By introducing a mixed paths attention module, the detection accuracy of the detector and the convergence speed of the training are improved.

The experimental results show that the proposed method has a better detection effect for metal casts with smaller size scaling and rotation characteristics.

A method for 6DOF pose measurement of industrial objects is proposed, which realizes the pose measurement and grasping of metal blanks and rough machined casts by industrial robots.