Text-driven 3D scene editing has attracted considerable interest due to its convenience and user-friendliness. However, methods that rely on implicit 3D representations, such as Neural Radiance Fields (NeRF), while effective in rendering complex scenes, are hindered by slow processing speeds and limited control over specific regions of the scene. Moreover, existing approaches, including Instruct-NeRF2NeRF and GaussianEditor, which utilize multi-view editing strategies, frequently produce inconsistent results across different views when executing text instructions. This inconsistency can adversely affect the overall performance of the model, complicating the task of balancing the consistency of editing results with editing efficiency. To address these challenges, we propose a novel method termed Single-View to 3D Object Editing via Gaussian Splatting (SVGS), which is a single-view text-driven editing technique based on 3D Gaussian Splatting (3DGS). Specifically, in response to text instructions, we introduce a single-view editing strategy grounded in multi-view diffusion models, which reconstructs 3D scenes by leveraging only those views that yield consistent editing results. Additionally, we employ sparse 3D Gaussian Splatting as the 3D representation, which significantly enhances editing efficiency. We conducted a comparative analysis of SVGS against existing baseline methods across various scene settings, and the results indicate that SVGS outperforms its counterparts in both editing capability and processing speed, representing a significant advancement in 3D editing technology. For further details, please visit our project page at: https://amateurc.github.io/svgs.github.io/ .

The automatic design of a 3D tooth model plays a crucial role in dental digitization. However, current approaches face challenges in compositional 3D tooth generation because both the layouts and shapes of missing teeth need to be optimized. In addition, collision conflicts are often omitted in 3D Gaussian-based compositional 3D generation, where objects may intersect with each other due to the absence of explicit geometric information on the object surfaces. Motivated by graph generation through diffusion models and collision detection using 3D Gaussians, we propose an approach named DM-CFO for compositional tooth generation, where the layout of missing teeth is progressively restored during the denoising phase under both text and graph constraints. Then, the Gaussian parameters of each layout-guided tooth and the entire jaw are alternately updated using score distillation sampling (SDS). Furthermore, a regularization term based on the distances between the 3D Gaussians of neighboring teeth and the anchor tooth is introduced to penalize tooth intersections. Experimental results on three tooth-design datasets demonstrate that our approach significantly improves the multiview consistency and realism of the generated teeth compared with existing methods.

The illegal use of facial forgery models, such as Generative Adversarial Networks (GAN) synthesized contents, has been on the rise, thereby posing great threats to personal reputation and national security. To mitigate these threats, recent studies have proposed the use of adversarial watermarks as countermeasures against GAN, effectively disrupting their outputs. However, the majority of these adversarial watermarks exhibit very limited defense ranges, providing defense against only a single GAN forgery model. Although some universal adversarial watermarks have demonstrated impressive results, they lack the defense scalability as a new-emerging forgery model appears. To address the tough issue, we propose a scalable approach even when the original forgery models are unknown. Specifically, a watermark expansion scheme, which mainly involves inheriting, defense and constraint steps, is introduced. On the one hand, the proposed method can effectively inherit the defense range of the prior well-trained adversarial watermark; on the other hand, it can defend against a new forgery model. Extensive experimental results validate the efficacy of the proposed method, exhibiting superior performance and reduced computational time compared to the state-of-the-arts.

Vision-based road lane detection is a critical technology for autonomous driving, enabling vehicles to navigate safely and efficiently under constrained conditions such as accurately identifying the lane markings and tracking the vehicle's position. However, despite exceeding 90% detection recall in large scale datasets, existing lane detection methods often fail in some real- world scenarios such as adverse weather, intensive shadows, complex road types, and challenging lighting conditions. This study highlights these gaps and proposes potential research areas to address them. To this end, the YOLO-based lane detection algorithm is utilized as a case study for determining potential perception problems under complex traffic situations.

The main challenge faced by video‐based real‐world anomaly detection systems is the accurate learning of unusual events that are irregular, complicated, diverse, and heterogeneous in nature. Several techniques utilizing deep learning have been created to detect anomalies, yet their effectiveness on real‐world data is often limited due to the insufficient incorporation of motion patterns. To address these problems and enhance the traditional functionality of anomaly detection systems for surveillance video data, we propose a weakly supervised graph neural‐network‐assisted video anomaly detection framework called AD‐Graph. To identify temporal information from a series of frames, we extract 3D visual and motion features and represent these in a language‐based knowledge graph format. Next, a robust clustering strategy is applied to group together meaningful neighbourhoods of the graph with similar vertices. Furthermore, spectral filters are applied to these graphs, and spectral graph theory is used to generate graph signals and detect anomalous events. Extensive experimental results over two challenging datasets, UCF‐Crime and ShanghaiTech, show improvements of 0.35% and 0.78% against a state‐of‐the‐art model.

The rapid development of the Industrial Internet of Things (IIoT) and artificial intelligence (AI) brings new security threats by exposing secret and private data. Thus, information security has become a major concern in the communication environment of IIoT and AI, where security and privacy must be ensured for the messages between a sender and the intended recipient. In this article, we propose a method called Harris hawks optimization-integer wavelet transform (HHO-IWT) for covert communication and secure data in the IIoT environment based on digital image steganography. The method embeds secret data in the cover images using a metaheuristic optimization algorithm called HHO to efficiently select image pixels that can be used to hide bits of secret data within integer wavelet transforms. The HHO-based pixel selection operation uses an objective function evaluation depending on the following two phases: exploitation and exploration. The objective function is employed to determine an optimal encoding vector to transform secret data into an encoded form generated by the HHO algorithm. Several experiments are conducted to validate the performance of the proposed method with respect to visual quality, payload capacity, and security against attacks. The obtained results reveal that the HHO-IWT method achieves higher levels of security than the state-of-the-art methods and that it resists various forms of steganalysis. Thus, utilizing this approach can keep unauthorized individuals away from the transmitted information and solve some security challenges in the IIoT.

Vehicle detection and tracking play an important role in autonomous vehicles and intelligent transportation systems. Adverse weather conditions such as the presence of heavy snow, fog, rain, dust or sandstorm situations are dangerous restrictions on camera’s function by reducing visibility, affecting driving safety. Indeed, these restrictions impact the performance of detection and tracking algorithms utilized in the traffic surveillance systems and autonomous driving applications. In this article, we start by proposing a visibility enhancement scheme consisting of three stages: illumination enhancement, reflection component enhancement, and linear weighted fusion to improve the performance. Then, we introduce a robust vehicle detection and tracking approach using a multi-scale deep convolution neural network. The conventional Gaussian mixture probability hypothesis density filter based tracker is utilized jointly with hierarchical data associations (HDA), which splits into detection-to-track and track-to-track associations. Herein, the cost matrix of each phase is solved using the Hungarian algorithm to compensate for the lost tracks caused by missed detection. Only detection information (i.e., bounding boxes with detection scores) is used in HDA without visual features information for rapid execution. We have also introduced a novel benchmarking dataset designed for research in applications of autonomous vehicles under adverse weather conditions called DAWN. It consists of real-world images collected with different types of adverse weather conditions. The proposed method is tested on DAWN, KITTI, and MS-COCO datasets and compared with 21 vehicle detectors. Experimental results have validated effectiveness of the proposed method which outperforms state-of-the-art vehicle detection and tracking approaches under adverse weather conditions.

Abstract Interactive simulations of fluids flow play an important role in several computer graphics‐based applications such as computer games, computer animation, movie industry, and virtual realities. The incompressible smoothed particle hydrodynamics (ISPH) model is a promising numerical scheme for large‐scale and large‐deformation simulations, where the pressure can be determined precisely by solving pressure Poisson equation (PPE). The three main shortcomings of the ISPH scheme are oscillating pressure, particles disorders, and particles penetrations through rigid boundary. In this paper, the stable pressure is obtained from modifications in the source term of PPE, in which the divergence‐free velocity condition plus density‐invariance condition multiply by a relaxation coefficient are included. The particles disorders are solved via utilizing a shifting technique with the current treatment of source term in PPE. Additionally, the dummy boundary particles are used for the rigid boundary treatment. For getting enough pressure on the boundary, the Neumann boundary condition is satisfied during the implicit solving processes. The performance of the stabilized ISPH model is tested on various numerical simulations with largely distorted free surface including liquid sloshing problems, fluid–fluid and fluid–structure interactions, and dam‐break flows. To extend the applicability of the stabilized ISPH model, the post process including visual realism with a highly rendering scheme is coupled. The coupled scheme introduces several simulations including free falling of a rigid body, water splashes, and dam break analysis. Furthermore, the proposed ISPH‐based method enables efficient and viscous fluid simulations with large time steps, higher viscosities, and resolutions, and it is a robust scheme in long interval simulations of nonlinear free‐surface flows.

Face recognition has received significant attention because of its numerous applications in access control, law enforcement, security, surveillance, Internet communication and computer entertainment. Although significant progress has been made, the state‐of‐the‐art face recognition systems yield satisfactory performance only under controlled scenarios and they degrade significantly when confronted with real‐world scenarios. The real‐world scenarios have unconstrained conditions such as illumination and pose variations, occlusion and expressions. Thus, there remain plenty of challenges and opportunities ahead. Latterly, some researchers have begun to examine face recognition under unconstrained conditions. Instead of providing a detailed experimental evaluation, which has been already presented in the referenced works, this study serves more as a guide for readers. Thus, the goal of this study is to discuss the significant challenges involved in the adaptation of existing face recognition algorithms to build successful systems that can be employed in the real world. Then, it discusses what has been achieved so far, focusing specifically on the most successful algorithms, and overviews the successes and failures of these algorithms to the subject. It also proposes several possible future directions for face recognition. Thus, it will be a good starting point for research projects on face recognition as useful techniques can be isolated and past errors can be avoided.

Face detection is one of the most important areas of research in computer vision due to its various uses in a wide range of human face-related applications. This paper proposes a method for detecting faces in uncontrolled imaging conditions using a probabilistic framework based on Hough forests. Hough forests can be regarded as task-adapted codebooks of local appearance that allow fast supervised training and fast matching at test time, codebooks are built upon a pool of heterogeneous local appearance features, a codebook is learned for the face appearance features that models the spatial distribution and appearance of facial components. The feasibility of the proposed method has been successfully tested on two challenging and widely used databases (i.e., CMU+MIT and FDDB) and the obtained results are encouraging.

The volume and complexity of data processed by today's personal computers are increasing exponentially, placing incredible demands on the microprocessors. In the meantime, computing performance that can be achieved by increasing the clock speed of a microprocessor is reaching to physical limits thus making the architectural solutions more prominent. Due to this an important architectural feature is added to recent microprocessors, single instruction multiple data (SIMD), which is a set of instructions that can speed up an application performance by allowing basic operation to be performed on multiple data elements in parallel with fewer instructions. The SIMD computational technique was introduced in the IA-32 Intel® architecture with MMX technology and then further enhanced with Intel's introduction of streaming SIMD extensions (SSE), SSE 2 (SSE2) and SSE 3 (SSE3). Although programming using these SIMD extensions enables software to achieve higher performance, several exiting scientific applications are not affected. This paper gives an overview of SIMD multimedia extensions. The features of these extensions are introduced. Available methods for programming with multimedia instruction sets are discussed. It also reviews recent trends to use multimedia extensions to accelerate many applications such as multimedia, scientific and engineering applications, and argues for further use in other significant computationally intensive applications.