Extracting stable skeletons from noisy images is a challenging problem since the skeletonization method is prone to be affected by inner and border noise. Although many methods have been proposed in the past for increasing the antinoise ability of skeletonization methods, most of them either only overcome border noise or, at the cost of lost topology, degrade the effects of two noises. In this paper, we propose a skeleton extraction framework to enhance the robustness of the existing skeletonization method against both inner and border noise. In our approach, we first use the different scales of Gaussian filters to smooth the input image and obtain multiple representations. Then, binarization and skeletonization were performed to produce a series of binary images and a series of skeletal images. Next, we use our measure on these binary and skeletal images to find the most suitable skeleton. Since our measure considers both the skeleton image changes and binary image changes caused by using a filter, the selected skeleton is sufficiently robust and has all the necessary skeletal branches. The inner noise experiment and border noise experiment are conducted for comparison. From the perspective of the measure of the rate of variation in the skeleton, the proposed framework can reduce the inner noise by approximately 92% and the border noise by approximately 40%. In addition, the experiment on static hand gesture recognition has demonstrated that the introduction of our framework can increase up to 11% mean recognition accuracy.

With the development of Micro-Electro-Mechanical System, wearable sensor-based human activity recognition systems have important applications in various fields such as health management, motion analysis, military and industry. In this paper, we propose a time-frequency features extraction method based on wavelet transform, which extracts 5 time-frequency features, namely wavelet entropy, wavelet energy, wavelet waveform length, wavelet coefficient variance and wavelet coefficient standard deviation. The experimental results are evaluated on the publicly available benchmark WISDM dataset including accelerometer data. Our model achieves 99.2%, 99.1% and 95.6% test accuracy on Subspace KNN, Bagged tree and Gaussian SVM respectively.

In this paper, we have proposed a novel fast iterative parallel thinning algorithm which combines the trait of the well-known Zhang-Suen algorithm (ZS) and of the one-pass thinning algorithm (OPTA). In the thinning procedure of the proposed algorithm, all the pixels are checked by logical criterion conditions and template match. The usage of the logical criterion conditions is for the purpose of reducing the amounts of the match template to accelerate the speed of the processing and the applying of the template match is to avoid the excessive erosion of some certain pattern which happened in ZS algorithm. Proposed algorithm, ZS algorithm and OPTA algorithm have implemented and applied to some simple binary image for comparison. The test results confirmed that the proposed algorithm has better performance in terms of visual effect and lower number of iterations. Meanwhile, from the perspective of robust to the edge noise, the proposed algorithm also has a good performance which is better than OPTA algorithm but is not over the ZS algorithm.

Gait impairment is a common motor manifestation of Parkinson’s disease (PD), which is also frequently accompanied by other motor abnormalities such as bradykinesia, rigidity, postural instability, and movement asymmetry. These motor impairments are closely associated with reduced mobility and increased fall risk. Although wearable plantar insole sensing provides a promising basis for objective gait assessment, existing studies have mainly focused on conventional time- or frequency-domain descriptors, whereas the nonlinear complexity of gait, laterality-related imbalance, and deviation from normal gait patterns remain insufficiently characterized in an integrated manner. To address this gap, this paper proposes FID-Gait, which is a three-domain fusion framework for PD identification using instrumented insole data. The framework combines automated gait-cycle segmentation with multidomain feature modeling, including a fractal domain for nonlinear gait complexity, a plantar-loading–phase imbalance (PLPI) domain for loading asymmetry and temporal disturbance, and a covariance-adjusted deviation (CAD) domain for measuring deviation from normal gait patterns. Experiments on the PhysioNet Gait in Parkinson’s Disease dataset showed that FID-Gait achieved strong discriminative performance under multiple evaluation protocols. At the gait-cycle level, the selected MLP classifier achieved an accuracy of 99.11% and an F1-score of 99.47%. At the subject level, the selected AdaBoost classifier achieved the highest accuracy of 90.22% and the best F1-score reached 93.02%. Five-fold cross-validation further supported the robustness of the proposed representation, and leave-one-subject-out evaluation provided preliminary evidence of subject-independent generalization. Overall, FID-Gait provides an effective and interpretable framework for PD gait characterization and identification in offline experimental settings.

Autonomous driving is one of the hottest topics in recent years, receiving extensive attention from the automotive industry and research institutes. In order to ensure the safety of drivers and pedestrians, it is necessary to predict possible hazards during driving based on the information collected by sensors. The two most important sensors used on the vehicles are Lidar and cameras. They are used to measure the distance and detect objects on the front, respectively. However, Lidar is very expensive, which limits its use in autonomous driving. This paper presents a new framework, which uses the mono-camera to realize both object detection and relative distance estimation. The framework is tested on the benchmark of the KITTI dataset, its performance depends on the algorithms used in the framework.