To reduce the cost of virtual try-on, a method of image deformation by body part size is proposed for the traditional two-dimensional virtual try-on method, which is challenging to represent the personalized characteristics of the body size of the fitting subject. On the basis of the input information of the user's body size, the method can generate a fitting effect that shows the user's characteristics with the corresponding clothes. The image segmentation algorithm is used to snap out the garment from the background garment image, and then the size and position of the garment are adjusted according to the dressing position of the standard mannequin image and fit the mannequin image. The final mesh with dense vertices is generated using surface subdivision. The experimental results show that this method can show good results in user personalized try-on.

A surface slicing planning method based on the K-means clustering algorithm and improved fruit fly optimization algorithm (FOA) is proposed to address the efficiency problem in the surface processing of special shaped workpieces. The NURBS surface reconstruction is performed on the complex surface of the selected workpiece, and the K-means clustering algorithm with the curvature-distance factor is used to subdivide the surface, and the FOA with the hopping strategy is used to plan the optimal connection path for each subdivision with different starting points. This improves the local search capability of the FOA by improving the mixing of different classes that occurs when the surface is subdivided. Finally, the proposed method is demonstrated to be effective for surface slicing by simulating the slicing plan on the constructed heterogeneous workpiece surface, and the shortest connectivity paths are obtained for different starting points.

To obtain the data of noncontact measurement of the human body, the depth camera is used to collect the human body, and the obtained initial data are transformed into the required point cloud data for processing through coordinate transformation, and then the collected three-dimensional point cloud data are preprocessed. The preprocessing includes point cloud downsampling, point cloud filtering, plane segmentation, outlier removal, point cloud surface estimation, and so forth. A new solution for point cloud filtering is proposed, which combines sliding least squares and unification and radius filtering. Compared with the traditional filtering, the effect is smoother, and finally the complete outline of the human body is obtained, and then the human body is measured. The results show that the human body data measured by this scheme is within the range of the relevant standard measurement accuracy.

To identify whether the actual work trajectory of workers in the factory meets the predetermined work trajectory requirements, we proposed an efficient and accurate work trajectory similarity matching method. We comprehensively considered the similarity between the actual work track and the predetermined track from the two characteristics of track angle and track distance. Among them, the similarity of track rotation angle is calculated using the improved longest common subsequence algorithm, and the similarity of track distance is calculated using the improved dynamic time warping (DTW) algorithm. Then the results of the similarity calculation of these two features are weighted. Finally, the weighted results are used to evaluate the similarity between the actual work track and the predetermined track, so as to judge whether the actual work track meets the requirements of the predetermined track. Experimental data show that the trajectory similarity matching algorithm in this paper has higher accuracy and efficiency than traditional DTW and other algorithms, and has higher ability to resist the interference of trajectory point evacuation than traditional DTW and other algorithms.

Epilepsy is one of the most common neurological disorders, and there exists a subset of patients with refractory epilepsy that require surgical removal of the epileptogenic foci (EF) area. Studies have shown that high-frequency oscillations (HFOs) in epileptic electroencephalogram signals can be used as an essential biomarker for locating EF. This paper proposes a new method for rapid localization of EF based on the automatic detection of HFOs by waveform feature templates (WFTs). First, the initial screening of HFOs based on Hilbert transform and subsequent rescreening with short-time energy and short-time Fourier transform is performed, and the two screening results are used as the template data set of HFOs. Then, a coarse-grained and fine-grained screening method for detecting HFOs using autocorrelation coefficients and interrelation coefficients as WFT detectors, respectively. Compared with the Hilbert transform detector and other HFOs detector methods proposed at abroad in recent years, the experimental simulations showed that the automatic detector based on WFT could detect HFOs more rapidly, accurately, and efficiently. Our proposed WFT detector has the advantages of high specificity, high sensitivity, and high accuracy in locating EF and has a high clinical utility.

Epilepsy is a disease caused by abnormal discharges in the central nervous system. Automatic detection and accurate identification of epileptic seizures based on electroencephalography (EEG) are significant in the clinical diagnosis and treatment of epilepsy. In this paper, we first decompose the patient's EEG signal into multiple intrinsic modal functions (IMFs) using empirical modal decomposition, then compute the mean, standard deviation, fluctuation index, and sample entropy of IMF1, and finally classify them using a fusion algorithm of support vector machine and K-nearest neighbor optimized by particle swarm algorithm. The results of validation using the epileptic EEG data set from Bonn University show that the auto-detection and fast recognition method proposed in this paper can achieve a high seizure accuracy recognition rate (≥95%) with only a small number of training samples, which has a good clinical application value.