394 research outputs found
Mineralization of Biomaterials for Bone Tissue Engineering
Mineralized biomaterials have been demonstrated to enhance bone regeneration compared to their non-mineralized analogs. As non-mineralized scaffolds do not perform as well as mineralized scaffolds in terms of their mechanical and surface properties, osteoconductivity and osteoinductivity, mineralization strategies are promising methods in the development of functional biomimetic bone scaffolds. In particular, the mineralization of three-dimensional (3D) scaffolds has become a promising approach for guided bone regeneration. In this paper, we review the major approaches used for mineralizing tissue engineering constructs. The resulting scaffolds provide minerals chemically similar to the inorganic component of natural bone, carbonated apatite, Ca5(PO4,CO3)3(OH). In addition, we discuss the characterization techniques that are used to characterize the mineralized scaffolds, such as the degree of mineralization, surface characteristics, mechanical properties of the scaffolds, and the chemical composition of the deposited minerals. In vitro cell culture studies show that the mineralized scaffolds are highly osteoinductive. We also summarize, based on literature examples, the applications of 3D mineralized constructs, as well as the rationale behind their use. The mineralized scaffolds have improved bone regeneration in animal models due to the enhanced mechanical properties and cell recruitment capability making them a preferable option for bone tissue engineering over non-mineralized scaffolds
SigFormer: Sparse Signal-Guided Transformer for Multi-Modal Human Action Segmentation
Multi-modal human action segmentation is a critical and challenging task with
a wide range of applications. Nowadays, the majority of approaches concentrate
on the fusion of dense signals (i.e., RGB, optical flow, and depth maps).
However, the potential contributions of sparse IoT sensor signals, which can be
crucial for achieving accurate recognition, have not been fully explored. To
make up for this, we introduce a Sparse signalguided Transformer (SigFormer) to
combine both dense and sparse signals. We employ mask attention to fuse
localized features by constraining cross-attention within the regions where
sparse signals are valid. However, since sparse signals are discrete, they lack
sufficient information about the temporal action boundaries. Therefore, in
SigFormer, we propose to emphasize the boundary information at two stages to
alleviate this problem. In the first feature extraction stage, we introduce an
intermediate bottleneck module to jointly learn both category and boundary
features of each dense modality through the inner loss functions. After the
fusion of dense modalities and sparse signals, we then devise a two-branch
architecture that explicitly models the interrelationship between action
category and temporal boundary. Experimental results demonstrate that SigFormer
outperforms the state-of-the-art approaches on a multi-modal action
segmentation dataset from real industrial environments, reaching an outstanding
F1 score of 0.958. The codes and pre-trained models have been available at
https://github.com/LIUQI-creat/SigFormer
Part-level Action Parsing via a Pose-guided Coarse-to-Fine Framework
Action recognition from videos, i.e., classifying a video into one of the
pre-defined action types, has been a popular topic in the communities of
artificial intelligence, multimedia, and signal processing. However, existing
methods usually consider an input video as a whole and learn models, e.g.,
Convolutional Neural Networks (CNNs), with coarse video-level class labels.
These methods can only output an action class for the video, but cannot provide
fine-grained and explainable cues to answer why the video shows a specific
action. Therefore, researchers start to focus on a new task, Part-level Action
Parsing (PAP), which aims to not only predict the video-level action but also
recognize the frame-level fine-grained actions or interactions of body parts
for each person in the video. To this end, we propose a coarse-to-fine
framework for this challenging task. In particular, our framework first
predicts the video-level class of the input video, then localizes the body
parts and predicts the part-level action. Moreover, to balance the accuracy and
computation in part-level action parsing, we propose to recognize the
part-level actions by segment-level features. Furthermore, to overcome the
ambiguity of body parts, we propose a pose-guided positional embedding method
to accurately localize body parts. Through comprehensive experiments on a
large-scale dataset, i.e., Kinetics-TPS, our framework achieves
state-of-the-art performance and outperforms existing methods over a 31.10% ROC
score.Comment: Accepted by IEEE ISCAS 2022, 5 pages, 2 figures. arXiv admin note:
text overlap with arXiv:2110.0336
Parsing is All You Need for Accurate Gait Recognition in the Wild
Binary silhouettes and keypoint-based skeletons have dominated human gait
recognition studies for decades since they are easy to extract from video
frames. Despite their success in gait recognition for in-the-lab environments,
they usually fail in real-world scenarios due to their low information entropy
for gait representations. To achieve accurate gait recognition in the wild,
this paper presents a novel gait representation, named Gait Parsing Sequence
(GPS). GPSs are sequences of fine-grained human segmentation, i.e., human
parsing, extracted from video frames, so they have much higher information
entropy to encode the shapes and dynamics of fine-grained human parts during
walking. Moreover, to effectively explore the capability of the GPS
representation, we propose a novel human parsing-based gait recognition
framework, named ParsingGait. ParsingGait contains a Convolutional Neural
Network (CNN)-based backbone and two light-weighted heads. The first head
extracts global semantic features from GPSs, while the other one learns mutual
information of part-level features through Graph Convolutional Networks to
model the detailed dynamics of human walking. Furthermore, due to the lack of
suitable datasets, we build the first parsing-based dataset for gait
recognition in the wild, named Gait3D-Parsing, by extending the large-scale and
challenging Gait3D dataset. Based on Gait3D-Parsing, we comprehensively
evaluate our method and existing gait recognition methods. The experimental
results show a significant improvement in accuracy brought by the GPS
representation and the superiority of ParsingGait. The code and dataset are
available at https://gait3d.github.io/gait3d-parsing-hp .Comment: 16 pages, 14 figures, ACM MM 2023 accepted, project page:
https://gait3d.github.io/gait3d-parsing-h
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