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Given the importance of augmented reality in recent years and its wide spread in many fields of media industries, practical fields such as medicine, education, and others. This study present a methodology in order to insert virtual static object into a real video environment for improve the reality with a virtual information that was not actually present. There are several problems needs to be overcome during the process of merging the virtual object such as geometry of shapes, registration, and illumination. The aim of this study is to make the virtual static objects look like it actually exists in the same real video environment in terms of dimensions, sizes and consistency of shapes. The following steps of the proposed method are: Firstly convert original video file into frames. Secondly separate the background from foreground by rebuilding the background model, then obtain the foreground objects by subtract original frames of the video from background. Thirdly apply the background segmentation. Then extracting the features for every one of the segments and extracting the features of virtual static object. Fifthly selecting a suitable segment (area) for insert the virtual object.

Nowadays, automated object detection and tracking is needed for video observation, robotic control, and vehicle driver aid systems. Object detection and tracking is an essential, complex activity in PC system vision due to the challenges in tracking. Continual contortion of objects at that time of motion and background scatter causes inadequate tracking. Non-static objects can be efficiently identified by deviation between the frames and clustering techniques, yet background system need to be upgraded consistently, and it is also vulnerable to camera jitter and lighting variants. In order to overcome these issues, an effective cluster-based frame differencing and W4+ method is proposed. These methods are helping to efficiently recognize the non-static object, in which a single technique cannot provide an efficient result. The proposed refined W4+ incorporated with CBFD method is used to detect the efficient moving object detection during the image analysis process. In addition to that, the fuzzy morphological filter processing to reduce the noise drastically. The objects with various sizes, contours, and lighting variants are available in the dataset, which makes the assessment of data a difficult task. The speculative outcomes and efficiency analysis on real video series datasets illustrate the efficiency of our strategies in contrast with existing strategies.

The k -nearest neighbor searching is a classical problem that has been seriously studied, due to its many important applications. The paper proposes an efficient algorithm to search the k -nearest neighbors for static objects. Since locations of static objects are known in advance and not changed, most of existing solutions build a kd -tree as a preprocessing and search the k - nearest neighbors by using it. We propose a completely different preprocessing with kd -trees. The core idea of this paper is to build in advance the k -nearest neighbors of each static object as a preprocessing. If a querying point q is given, the nearest object p of q is firstly searched and then the k -nearest neighbors of q are found by using the k -nearest neighbors of p . It is to use the feature that two objects may share many neighbors if they are spatially close to each other. In order to measure the performance of the proposed algorithm, we have a number of experiments. The results of experiments showed that the proposed algorithm is 2–3 times quicker than the method using kd -tree in the Point Cloud Library(PCL).

Chirality is a manifestation of the asymmetry inherent in nature. It has been defined as the symmetry breaking of the parity of static objects, and the definition was extended to dynamic motion such that true and false chiralities were distinguished. Recently, rotating, yet not propagating, atomic motions were predicted and observed in two-dimensional materials, and they were referred to as ‘chiral phonons’. A natural development would be the discovery of truly chiral phonons that propagate while rotating in three-dimensional materials. Here we used circularly polarized Raman scattering and first-principles calculations to identify truly chiral phonons in chiral bulk crystals. This approach enabled us to determine the chirality of a crystal in a non-contact and non-destructive manner. In addition, we demonstrated that the law of the conservation of pseudo-angular momentum holds between circularly polarized photons and chiral phonons. These findings are expected to help develop ways for transferring the pseudo-angular momentum from photons to electron spins via propagating chiral phonons in opto-phononic-spintronic devices.The notion of chirality in dynamical systems with broken spatial symmetry but preserved time inversion symmetry has led to the concept of truly chiral phonons. These have now been observed in bulk HgS using circularly polarized Raman spectroscopy.

Visual simultaneous localization and mapping (SLAM), based on point features, achieves high localization accuracy and map construction. They primarily perform simultaneous localization and mapping based on static features. Despite their efficiency and high precision, they are prone to instability and even failure in complex environments. In a dynamic environment, it is easy to keep track of failures and even failures in work. The dynamic object elimination method, based on semantic segmentation, often recognizes dynamic objects and static objects without distinction. If there are many semantic segmentation objects or the distribution of segmentation objects is uneven in the camera view, this may result in feature offset and deficiency for map matching and motion tracking, which will lead to problems, such as reduced system accuracy, tracking failure, and track loss. To address these issues, we propose a novel point-line SLAM system based on dynamic environments. The method we propose obtains the prior dynamic region features by detecting and segmenting the dynamic region. It realizes the separation of dynamic and static objects by proposing a geometric constraint method for matching line segments, combined with the epipolar constraint method of feature points. Additionally, a dynamic feature tracking method based on Bayesian theory is proposed to eliminate the dynamic noise of points and lines and improve the robustness and accuracy of the SLAM system. We have performed extensive experiments on the KITTI and HPatches datasets to verify these claims. The experimental results show that our proposed method has excellent performance in dynamic and complex scenes.

As composite spaces that integrate nature and culture, gardens are no longer regarded as merely static objects of visual appreciation in the context of urbanization, but have become essential venues for public cultural tourism and leisure. Consequently, the behavioral characteristics of tourists in gardens have attracted increasing academic attention. Space syntax, as a tool for analyzing the influence of spatial organization on human behavior, quantifies spatial configuration characteristics and can reveal how garden spatial configuration affects tourists’ movement paths and spatial preferences, thereby enabling a systematic examination of the impact of space syntax–based garden spatial configuration on tourist behavior. adheres to the Following by PRISMA 2020 guidelines, this study conducted a literature search for the period 2015−2015 in four databases, namely Web of Science, Scopus, JSTOR, and ScienceDirect Based on explicit inclusion and exclusion criteria, 16 high-quality empirical studies were ultimately selected. Results indicate that indicators such as integration, connectivity, and depth, demonstrate significant explanatory in predicting tourist path selection, stay locations, and spatial preferences. Furthermore, the influence of spatial structure on visitor behavior is not a singular direct effect. Visitor perceptions, particularly aesthetic preferences, cultural cognition, and sense of security, play a crucial mediating role between spatial structure and behavior. Based on these findings, this study proposes the “Structure–Perception–Behavior (SPB)’‘ framework. Its cross-scale methodological insights provide a theoretical foundation and practical pathway for subsequent landscape space optimization design and visitor behavior guidance.

Agricultural and ecological modelers commonly use maps as input for spatially explicit simulations. While real world maps are often used, they are limited by being static objects, therefore making it difficult to assess how patterns within the landscape contribute to ecological processes. Agricultural landscape generators (ALG) are a useful tool for simulating maps in a more flexible way. They can increase robustness of models that rely on landscape maps as input, they allow modelers to give spatial representation to non-spatial models, and they are a useful tool for recreating spatial patterns in agricultural-dominated landscapes. A limitation of previous ALGs is that they have rarely been designed for general use (non-open source software, not written in R, and designed for specific projects). Furthermore, they are typically either extremely general and thus oversimplified or have a high specificity for particular use cases. ALGR bridges this gap by providing a general-purpose, dynamic landscape generator that balances structural realism with adaptability. ALGR generates agricultural landscapes with a three-step approach: first, outlining potential space, second, field placement inside of that space, and third, enrichment of the landscape with information. This stepwise approach ensures that ALGR generates landscapes with realistic spatial patterns while remaining adaptable to diverse regions and applications. It is the first ALG that is specifically designed to allow a simple integration within the R programming environment and the r-spatial package environment. ALGR is designed as a general-purpose generator, which is simple to use and facilitates an easy integration in modelling workflow. We present several examples of workflows using ALGR , to demonstrate its usefulness. Our examples include: 1) simulating different land use shares, 2) parameter tuning of ALGR to recreate real world landscape, patterns 3) spatially distributing crop portfolios, and 4) using real-world maps as a basis for field placement.

In our work, we are particularly interested in studying the shadows cast by static objects in outdoor environments, during daytime. To assess the accuracy of a shadow detection algorithm, we need ground truth information. The collection of such information is a very tedious task because it is a process that requires manual annotation. To overcome this severe limitation, we propose in this paper a methodology to automatically render ground truth using a virtual environment. To increase the degree of realism and usefulness of the simulated environment, we incorporate in the scenario the precise longitude, latitude and elevation of the actual location of the object, as well as the sun’s position for a given time and day. To evaluate our method, we consider a qualitative and a quantitative comparison. In the quantitative one, we analyze the shadow cast by a real object in a particular geographical location and its corresponding rendered model. To evaluate qualitatively the methodology, we use some ground truth images obtained both manually and automatically.

The Fermi-polaron problem of a mobile impurity interacting with fermionic medium emerges in various contexts, ranging from the foundations of Landau’s Fermi-liquid theory to electron-exciton interaction in semiconductors, to unusual properties of high-temperature superconductors. While classically the medium provides only a dissipative environment to the impurity, the quantum picture of polaronic dressing is more intricate and arises from the interplay of few- and many-body aspects of the problem. The conventional expectation for the dynamics of Fermi polarons is that it is dissipative in character, and any excess energy is rapidly emitted away from the impurity as particle-hole excitations. Here we report a strikingly different type of polaron dynamics in a one-dimensional system of the impurity interacting repulsively with the fermions. When the total momentum of the system equals the Fermi momentum, there emerges a sharp collective mode corresponding to long-lived oscillations of the polaronic cloud surrounding the impurity. This mode can be observed experimentally with ultracold atoms using Ramsey interferometry and radio-frequency spectroscopy.