ABSTRACT Sand painting is a visually distinctive art form characterized by granular textures and diverse expressive techniques, where sand grains are manipulated through various hand movements such as waving, seeping, sweeping, and stroking. However, traditional stylized methods often fail to capture the fine textures and diverse techniques unique to sand painting. In this paper, we propose a Feature‐Oriented Sand Painting (FOSP) inspired by real sand painting techniques, aiming to produce sand paintings with authentic sandy textures and diverse techniques. Our FOSP comprises three main modules: overall sand waving generation, extraction and drawing of sand reduction regions, and simulation of sand grain accumulation with multiple techniques. The first module generates fine sand‐grain textures, whereas the latter two focus on contour rendering to enrich detail expression. Experiments validate that our FOSP can generate high‐quality sand paintings rapidly, outperforming existing methods in sand painting generation tasks.

The sensor streams influence and correlate with each other, and the hidden correlation can be used to identify and explain abnormal problems. This paper proposed one kind of anomaly detection service based on lag-correlation analysis. It first constructs correlation graph model based on lag-correlation analysis of multiple sensor streams. Then sensor streams groups are constructed according to the correlation degree in the graph model, and are encapsulated as corresponding services to realize the anomaly detection within and out of stream groups in real time. Experimental results on a real industrial sensor data set show that the proposed method is effective in anomaly detection tasks in multiple sensor stream data.

Abstract We propose a comprehensive soccer match video analysis pipeline tailored for broadcast footage, which encompasses three pivotal stages: soccer field localization, player tracking, and soccer ball detection. Firstly, we introduce sports camera calibration to seamlessly map soccer field images from match videos onto a standardized two‐dimensional soccer field template. This addresses the challenge of consistent analysis across video frames amid continuous camera angle changes. Secondly, given challenges such as occlusions, high‐speed movements, and dynamic camera perspectives, obtaining accurate position data for players and the soccer ball is non‐trivial. To mitigate this, we curate a large‐scale, high‐precision soccer ball detection dataset and devise a robust detection model, which achieved the of 80.9%. Additionally, we develop a high‐speed, efficient, and lightweight tracking model to ensure precise player tracking. Through the integration of these modules, our pipeline focuses on real‐time analysis of the current camera lens content during matches, facilitating rapid and accurate computation and analysis while offering intuitive visualizations.

Abstract Debris flow is a highly destructive natural disaster, necessitating accurate simulation and prediction. Existing simulation methods tend to be overly simplified, neglecting the three‐dimensional complexity and multiphase fluid interactions, and they also lack comprehensive consideration of soil conditions. We propose a novel two‐particle debris flow simulation method based on smoothed particle hydrodynamics (SPH) for enhanced accuracy. Our method employs a sophisticated two‐particle model coupling debris flow dynamics with SPH to simulate fluid‐solid interaction effectively, which considers various soil factors, dividing terrain into variable and fixed areas, incorporating soil impact factors for realistic simulation. By dynamically updating positions and reconstructing surfaces, and employing GPU and hash lookup acceleration methods, we achieve accurate simulation with significantly efficiency. Experimental results validate the effectiveness of our method across different conditions, making it valuable for debris flow risk assessment in natural disaster management.

In this study, the authors focus on the task of leaf segmentation under different imaging conditions (e.g. backgrounds and shadows). A new method ‐ robust random walk (RW) is proposed to propagate the prior of user's specified pixels. Specifically, they first employ RWs to take the relationship of pairwise pixels into consideration. A superpixel‐consistent constraint is added to make the edges of segmentation smooth. Owing to the effect of illumination, some parts of a leaf surface are brighter than others and it may further harm the subsequent label propagation. To address this problem, they learn a common subspace by taking into account the illumination of local and non‐local pixels. By doing so, it has good adaptability to process noise interfering and non‐uniform illumination. In addition, since RW only considers the pairwise relationship of pixels, it will be sensitive to the specified and connected pixels. Thus, they further employ a log‐likelihood ratio to predict the probability of a pixel belonging to the background and use it to guide the label propagation. Based on the proposed method, they can obtain a smoothed and robust leaf segmentation. Experimental results on unconstrained leaf images demonstrate the efficiency of their algorithm.

Plant leaf recognition is a computer vision task used to automatically recognize plant species. It is very challenging since rich plant leaf morphological variations, such as sizes, textures, shapes, venation, and so on. Most existing plant leaf methods typically normalize all plant leaf images to the same size and recognize them at one scale, resulting in unsatisfactory performances. In this letter, a multiscale fusion convolutional neural network (MSF-CNN) is proposed for plant leaf recognition at multiple scales. First, an input image is down-sampled into multiples low resolution images with a list of bilinear interpolation operations. Then, these input images with different scales are step-by-step fed into the MSF-CNN architecture to learn discriminative features at different depths. At this stage, the feature fusion between two different scales is realized by a concatenation operation, which concatenates feature maps learned on different scale images from a channel view. Along with the depth of the MSF-CNN, multiscale images are progressively handled and the corresponding features are fused. Third, the last layer of the MSF-CNN aggregates all discriminative information to obtain the final feature for predicting the plant species of the input image. Experiments show the proposed MSF-CNN method is superior to multiple state-of-the art plant leaf recognition methods on the MalayaKew Leaf dataset and the LeafSnap Plant Leaf dataset.

In this paper, a new surface tension formulation is presented for Smoothed Particle Hydrodynamics in fluid simulation, especially small-scale detailed fluid animation. The surface tension formulation is decomposed into three processes: (1) mesh smoothing exploited a Lagrangian operator in a volume-preserved way, (2) surface tension computation between the original mesh and smoothed mesh, (3) surface tension transfer from mesh vertices onto their neighbor particles. Experimental results show that the proposed surface tension formulation is effective and efficient for realistic simulations.

ABSTRACT This paper presents a novel approach for simulating the dynamic coupling between a tree and raindrops based on physical deformation and fluid simulation. By the approach, tree animation in the rain can be simulated in a two‐resolution way: branch motion and leaf motion. The branch is represented by the Euler–Bernoulli beam model, and the leaf petiole is represented by the three‐prism elastic model. Interaction coupling liquid motion on the hydrophilic surface with a flexible petiole is well implemented by a special design. To simplify the computation process, instead of the computation‐intensive three‐dimensional Navier–Stokes equations, shallow water equations are used to simulate the water dynamics together with the whole leaf deformation. Simulation has been also made to various phenomena incurred from the interactive coupling. These include, among others, part of impacting raindrops splashing into the air with the remaining flowing along the slant of the leaf and merging into larger ones or hanging on the blade boundary, with the leaf rebounding and vibrating after the drops fall off the leaf. A level‐of‐detail approach is exploited to accelerate rendering in views of different distances. The experimental results illustrate that the approach can be applied to efficiently generate realistic details of the interactive coupling between a tree and raindrops. Copyright © 2012 John Wiley & Sons, Ltd.

This paper proposes a novel approach to animation realistic interactions between tree branches and raindrops in a physically-based way. A new elastic model using a three-prism structures is presented to flexibly bend and twist tree branches naturally in the first time. Various distinct forms of interactions when or after raindrops hitting on tree branches can be well simulated using a new efficient technique specially designed for liquid motion on non-rigid objects with hydrophilic surfaces. Experimental results indicate that our approach can be used to simulate the interactions between tree branches and raindrops efficiently and realistically.

We present a new approach for quickly designing 3D models of botanical trees using an iterative addition of new nodes to the tree branch structure. This process is guided by the proximity of points marking volume density data captured from photographs. Numerical parameters provide the user controls that are consistent with the characteristics of trees in landscaping and make it possible to generate a wide variety of tree styles. Meanwhile we synthesize visually believable motions for the generated tree models affected by a wind field. Our system enables the simulation of tree animation, by introducing physically-based transformation matrix calculations for hierarchical branch patterns. The system also supports the tree-shaping modes in which many branches and leaves are generated by interactively designed their distribution density. Experimental results show that our approach can design a variety of reasonably natural-looking trees and their motions.