Predicting human motion from a historical pose sequence is at the core of many applications in computer vision. Current state-of-the-art methods concentrate on learning contexts space, however, high dimensionality and complex nature invoke inherent difficulties extracting such contexts. In this paper, we instead advocate to model joint trajectory as smooth, vectorial, gives sufficient information model. Moreover, most existing consider only dependencies between skeletal connected joints,...

Multi-frame human pose estimation has long been an appealing and fundamental issue in visual perception. Owing to the frequent rapid motion occlusion videos, this task is extremely challenging. Current state-of-the-art methods seek model spatiotemporal features by equally fusing each frame local sequence, which weakens target information. In addition, existing approaches usually emphasize more on deep while ignoring detailed information implied shallow feature maps, resulting dropping of...

Motion prediction is a classic problem in computer vision, which aims at forecasting future motion given the observed pose sequence. Various deep learning models have been proposed, achieving state-of-the-art performance on prediction. However, existing methods typically focus modeling temporal dynamics space. Unfortunately, complicated and high dimensionality nature of human brings inherent challenges for dynamic context capturing. Therefore, we move away from conventional based...

Extrapolating future human motion based on the historical pose sequence is foundation of various intelligent applications. Numerous deep learning-based algorithms have been designed to address this task, achieving state-of-the-art performance different benchmark datasets. However, most existing methods employ three-dimensional coordinates joints demonstrate dynamic contexts implicitly. Unfortunately, it remains challenging in capturing information from sequence. In paper, we advocate...

In this study, an improved quantum‐behaved particle swarm optimisation based pulse‐coupled neural network (IQPSO‐PCNN) is proposed in the non‐subsampled shearlet transform (NSST) domain for medical image fusion. First, NSST tool used to decompose source into low‐frequency and high‐frequency subbands. Then, subbands, fusion rules of two different functions are presented, which simultaneously addresses key issues energy preservation detail extraction. For unlike conventional PCNN‐based...

Medical image segmentation has long been a compelling and fundamental problem in the realm of neuroscience. This is an extremely challenging task due to intensely interfering irrelevant background information segment target. State-of-the-art methods fail consider simultaneously addressing both long-range short-range dependencies, commonly emphasize semantic characterization capability while ignoring geometric detail implied shallow feature maps resulting dropping crucial features. To tackle...

Developing a high-quality, real-time, dense visual SLAM system poses significant challenge in the field of computer vision. NeRF introduces neural implicit representation, marking notable advancement research. However, existing methods suffer from long runtimes and face challenges when modeling complex structures scenes. In this paper, we propose method that enables high-quality real-time reconstruction even on desktop PC. Firstly, novel scene encoding geometry appearance information as...

Magnetic Resonance Imaging (MRI) requires a trade-off between resolution, signal-to-noise ratio, and scan time, making high-resolution (HR) acquisition challenging. Therefore, super-resolution for MR image is feasible solution. However, most existing methods face challenges in accurately learning continuous volumetric representation from low-resolution or require HR supervision. To solve these challenges, we propose novel method based on two-factor representation. Specifically, factorize...

Cell-to-cell communication must occur through molecular transport in the intercellular fluid space. Nanoparticles, such as exosomes, diffuse or move more slowly fluids than small molecules. To find a microfluidic technology for real-time exosome experiments on between living cells, we use culture dish's quaternary ultra-slow microcirculation flow field to accumulate nanoparticles specific area. Taking stem cell-tumor cell interaction an example, microcirculatory controls exosomes interfere...

In the field of computer vision and graphics, high-quality reconstruction human body in static scenes has been achieved recent years by a single multilayer perceptron (MLP) number approaches. However, MLPs have capacity limitations, requiring substantial training time computational resources for dynamic scene reconstruction, quality is significantly constrained. We propose method effectively processing complex spatiotemporal signals three-dimensional (3D) modeling. The proposed uses temporal...

Human motion prediction aims at capturing the hidden temporal correlations between historical and future poses. Various graph convolution networks have been presented for encoding spatial dependencies joints. Empirically, crucial shortcoming of these methods is that they fail to extract enough spatially relevant information. In this paper, we propose an adaptive multi-order context fusion architecture consists two components. A novel message propagation module encodes interaction joints,...

Human motion prediction is fundamental for many applications in computer vision. Current methods typically handle with seqential models, which ignore the fact that joint movement driven by forces. In this paper, we provide a novel mechanical view to decompose force into magnitude and direction, contributes modeling temporal evolution of joints. Moreover, existing graph convolution-based merely utilize deep-level features, difficult capture complex spatial dependencies contexts. We introduce...

Medical image segmentation techniques based on convolution neural networks indulge in feature extraction triggering redundancy of parameters and unsatisfactory target localization, which outcomes less accurate results to assist doctors diagnosis.In this paper, we propose a multi-level semantic-rich encoding-decoding network, consists Pooling-Conv-Former (PCFormer) module Cbam-Dilated-Transformer (CDT) module.In the PCFormer module, it is used tackle issue parameter explosion conservative...

Motion prediction is a classic problem in computer vision, which aims at forecasting future motion given the observed pose sequence. Various deep learning models have been proposed, achieving state-of-the-art performance on prediction. However, existing methods typically focus modeling temporal dynamics space. Unfortunately, complicated and high dimensionality nature of human brings inherent challenges for dynamic context capturing. Therefore, we move away from conventional based...