Hydrogel loaded with cerium-manganese nanoparticles and nerve growth factor enhances spinal cord injury repair by modulating immune microenvironment and promoting neuronal regeneration.

Background: Spinal cord injury (SCI) treatment remains a formidable challenge, as current therapeutic approaches provide only marginal relief and fail to reverse the underlying tissue damage. This study aims to develop a novel composite material combining enzymatic nanoparticles and nerve growth factor (NGF) to modulate the immune microenvironment and enhance SCI repair.

Methods: CeMn nanoparticles (NP) and CeMn NP-polyethylene glycol (PEG) nanozymes were synthesized via sol-gel reaction and DSPE-mPEG modification.

Weakly Supervised Visual Saliency Prediction.

The success of current deep saliency models heavily depends on large amounts of annotated human fixation data to fit the highly non-linear mapping between the stimuli and visual saliency. Such fully supervised data-driven approaches are annotation-intensive and often fail to consider the underlying mechanisms of visual attention. In contrast, in this paper, we introduce a model based on various cognitive theories of visual saliency, which learns visual attention patterns in a weakly supervised manner.

Collision-Free Video Synopsis Incorporating Object Speed and Size Changes.

This paper presents a new surveillance video synopsis method which performs much better than previous approaches in terms of both compression ratio and artifact. Previously, a surveillance video was usually compressed by shifting the moving objects of that video forward along the time axis, which inevitably yielded serious collision and chronological disorder artifacts between the shifted objects. The main observation of this paper is that these artifacts can be alleviated by changing the speed or size of the objects, since with varied speed and size the objects can move more flexibly to avoid collision points or to keep chronological relationships.

Multi-View Video Synopsis via Simultaneous Object-Shifting and View-Switching Optimization.

We present a method for synopsizing multiple videos captured by a set of surveillance cameras with some overlapped field-of-views. Currently, object-based approaches that directly shift objects along the time axis are already able to compute compact synopsis results for multiple surveillance videos. The challenge is how to present the multiple synopsis results in a more compact and understandable way.

Video Saliency Prediction using Spatiotemporal Residual Attentive Networks.

This paper proposes a novel residual attentive learning network architecture for predicting dynamic eye-fixation maps. The proposed model emphasizes two essential issues, i.e, effective spatiotemporal feature integration and multi-scale saliency learning.

Effective Video Stabilization via Joint Trajectory Smoothing and Frame Warping.

Video stabilization is usually composed of three stages: feature trajectory extraction, trajectory smoothing, and frame warping. Most previous approaches view them as three separate stages. This paper proposes a method combining the last two stages, namely the trajectory smoothing and frame warping stages, into a single optimization framework.

Multiobject Tracking by Submodular Optimization.

In this paper, we propose a new multiobject visual tracking algorithm by submodular optimization. The proposed algorithm is composed of two main stages. At the first stage, a new selecting strategy of tracklets is proposed to cope with occlusion problem.

Better Dense Trajectories by Motion in Videos.

Currently, the most widely used point trajectories generation methods estimate the trajectories from the dense optical flow, by using a consistency check strategy to detect the occluded regions. However, these methods will miss some important trajectories, thus resulting in breaking smooth areas without any structure especially around the motion boundaries (MBs). We suggest exploring MBs in video to generate more accurate dense point trajectories.

Dynamic Video Stitching via Shakiness Removing.

Stitching videos captured by hand-held mobile cameras can essentially enhance entertainment experience of ordinary users. However, such videos usually contain heavy shakiness and large parallax, which are challenging to stitch. In this paper, we propose a novel approach of video stitching and stabilization for videos captured by mobile devices.

Animating Wall-Bounded Turbulent Smoke via Filament-Mesh Particle-Particle Method.

Turbulent vortices in smoke flows are crucial for a visually interesting appearance. Unfortunately, it is challenging to efficiently simulate these appealing effects in the framework of vortex filament methods. The vortex filaments in grids scheme allows to efficiently generate turbulent smoke with macroscopic vortical structures, but suffers from the projection-related dissipation, and thus the small-scale vortical structures under grid resolution are hard to capture.

Homography Propagation and Optimization for Wide-Baseline Street Image Interpolation.

Wide-baseline street image interpolation is useful but very challenging. Existing approaches either rely on heavyweight 3D reconstruction or computationally intensive deep networks. We present a lightweight and efficient method which uses simple homography computing and refining operators to estimate piecewise smooth homographies between input views.

Object Movements Synopsis via Part Assembling and Stitching.

Video synopsis aims at removing video's less important information, while preserving its key content for fast browsing, retrieving, or efficient storing. Previous video synopsis methods, including frame-based and object-based approaches that remove valueless whole frames or combine objects from time shots, cannot handle videos with redundancies existing in the movements of video object. In this paper, we present a novel part-based object movements synopsis method, which can effectively compress the redundant information of a moving video object and represent the synopsized object seamlessly.

Vortex Filaments in Grids for Scalable, Fine Smoke Simulation.

Vortex modeling can produce attractive visual effects of dynamic fluids, which are widely applicable for dynamic media, computer games, special effects, and virtual reality systems. However, it is challenging to effectively simulate intensive and fine detailed fluids such as smoke with fast increasing vortex filaments and smoke particles. The authors propose a novel vortex filaments in grids scheme in which the uniform grids dynamically bridge the vortex filaments and smoke particles for scalable, fine smoke simulation with macroscopic vortex structures.

Compact video synopsis via global spatiotemporal optimization.

Video synopsis aims at providing condensed representations of video data sets that can be easily captured from digital cameras nowadays, especially for daily surveillance videos. Previous work in video synopsis usually moves active objects along the time axis, which inevitably causes collisions among the moving objects if compressed much. In this paper, we propose a novel approach for compact video synopsis using a unified spatiotemporal optimization.

Mesh-guided optimized retexturing for image and video.

This paper presents an approach of replacing textures of specified regions in the input image and video using stretch-based mesh optimization.. The retexturing results have the similar distortion and shading effects conforming to the underlying geometry and lighting conditions.

Layer-based representation of polyhedrons for point containment tests.

This paper presents the layer-based representation of polyhedrons and its use for point-in-polyhedron tests. In the representation, the facets and edges of a polyhedron are sequentially arranged, and so, the binary search algorithm is efficiently used to speed up inclusion tests. In comparison with conventional representation for polyhedrons, the layer-based representation we propose greatly reduces the storage requirement because it represents much information implicitly, though it still has a storage complexity O(n).

Principal square root of 3-subdivision-based biorthogonal wavelets.

A new efficient biorthogonal wavelet analysis based on the principal square root of subdivision is proposed in the paper by using the lifting scheme. Since the principal square root of subdivision is of the slowest topological refinement among the traditional triangular subdivisions, the multiresolution analysis based on the principal square root of subdivision is more balanced than the existing wavelet analyses on triangular meshes, and accordingly offers more levels of detail for processing polygonal models. In order to optimize the multiresolution analysis process, the new wavelets, no matter whether they are interior or on boundaries, are orthogonalized with the local scaling functions based on a discrete inner product with subdivision masks.

Interactive cutting simulation with adaptive refinements using digital logic design analogy.

A major requirement for surgical simulation is to allow virtual tissue cutting. This paper presents a scalable and adaptive cutting technique based on a mass-spring mesh. By the analogy of digital logic design, an arbitrary incision is modeled systematically by translating the cutting process into a state diagram.

A haptic needle manipulation simulator for Chinese acupuncture.

Chinese acupuncture is a traditional medical treatment in Chinese history. Recent evidence shows that this treatment is effective. However, acupuncture students can only practice on either real patients or mannequin.

An efficient and scalable deformable model for virtual reality-based medical applications.

Modeling of tissue deformation is of great importance to virtual reality (VR)-based medical simulations. Considerable effort has been dedicated to the development of interactively deformable virtual tissues. In this paper, an efficient and scalable deformable model is presented for virtual-reality-based medical applications.

Interactive deformation of soft tissues with haptic feedback for medical learning.

An effective deformable model based on a successive force propagation process is proposed. It avoids the laborious stiffness matrix formulation and is scalable simply by controlling the penetration depth. Mechanical tests are performed to evaluate its feasibility for modeling real tissues.