Research on target tracking algorithm based on mean shift with adaptive bandwidth

The traditional mean shift algorithm used fixed kernels or symmetric kernel function, which will cause the target tracking lost or failure. The target tracking algorithm based on mean shift with adaptive bandwidth was proposed. Firstly, the signed distance constraint function was introduced to produce the anisotropic kernel function based on signed distance kernel function. This anisotropic kernel function satisfies that the value of the region function outside the target is zero, which provides accurate tracking window for the target tracking. Secondly, calculate the mean shift window center of anisotropic kernel function template, the theory basis is the sum of vector weights from the sample point in the tracking window to the center point is zero. Thirdly, anisotropic kernel function templates adaptive update implementation by similarity threshold to limit the change of the template between two sequential pictures, so as to realize real-time precise tracking. Finally, the contrast experimental results show that our algorithm has good accuracy and high real time.

Segmentation of Gastric Computerized Tomography Images under Intelligent Algorithms in Evaluation of Efficacy of Decitabine Combined with Paclitaxel in Treatment of Gastric Cancer

To analyze the evaluation of artificial intelligence algorithm combined with gastric computed tomography (CT) image in clinical chemotherapy for advanced gastric cancer, 112 patients with advanced gastric cancer were selected as the research object. Among which, 56 patients in the experimental group received paclitaxel (PTX) combined with decitabine sequential decitabine maintenance therapy. Fifty-six patients in the control group received first-line treatment with decitabine combined with cisplatin. The image segmentation algorithm based on fast interactive dictionary selection was used to process gastric CT images. Complete response (CR), partial response (PR), stable disease (SD), progressive disease (PD), response rate (RR), disease control rate (DCR), and overall survival (OS) after treatment were recorded. The true-positive rate (TPR) and coincidence ratio (CR) of the proposed algorithm for image segmentation were significantly higher than those of the mean shift algorithm and the iCoseg algorithm. The mean edge distance (MED) and edge distance variance (EDV) were significantly lower than the mean shift algorithm and the iCoseg algorithm, and the differences were considerable ( P < 0.05 ). The number of CR (5 cases), PR (13 cases), RR (18 cases), and DCR (44 cases) in the experimental group was significantly higher than that in the control group, while the number of PD (12 cases) was significantly lower than that in the control group ( P < 0.05 ). The number of patients complicated with hematological toxicity, leucopenia, thrombocytopenia, and digestive tract reaction in the experimental group was less than that in the control group ( P < 0.05 ). From the comparison of long-term efficacy, the survival rate of patients in both groups showed a decreasing trend within 24 months, but the decreasing trend of survival rate of patients in the experimental group was better than that in the control group. In short, the proposed algorithm had better segmentation performance than traditional algorithms. Compared with first-line treatment with decitabine and cisplatin, PTX in combination with decitabine sequential citabine maintenance regimens had better disease control rates, lower toxicity, and more effective improvements in patient quality of life and longer survival in patients with advanced gastric cancer.

Outlier Detection in Wellness Data using Probabilistic Mapped Mean-Shift Algorithms

In this paper, the Probabilistic Mapped Mean-Shift Algorithm is proposed to detect anomalous data in public datasets and local hospital children’s wellness clinic databases. The proposed framework consists of two main parts. First, the Probabilistic Mapping step consists of k-NN instance acquisition, data distribution calculation, and data point reposition. Truncated Gaussian Distribution (TGD) was used for controlling the boundary of the mapped points. Second, the Outlier Detection step consists of outlier score calculation and outlier selection. Experimental results show that the proposed algorithm outperformed the existing algorithms with real-world benchmark datasets and a Children’s Wellness Clinic dataset (CWD). Outlier detection accuracy obtained from the proposed algorithm based on Wellness, Stamps, Arrhythmia, Pima, and Parkinson datasets was 93%, 94%, 80%, 75%, and 72%, respectively.

Motion Capture and Intelligent Correction Method of Badminton Movement Based on Machine Vision

In recent years, badminton has become more and more popular in national fitness programs. Amateur badminton clubs have been established all over the country, and amateur badminton events at all levels have increased significantly. Due to the lack of correct medical supervision and health guidance, many people have varying degrees of injury during sports. Therefore, it is very important to study the method of badminton movement capture and intelligent correction based on machine vision to provide safe and effective exercise plan for amateur badminton enthusiasts. This article aims to study the methods of motion capture and intelligent correction of badminton. Aiming at the shortcoming of the mean shift algorithm that it is easy to lose the target when the target is occluded or the background is disturbed, this paper combines the mean shift algorithm with the Kalman filter algorithm and proposes an improvement to the combined algorithm. The improved algorithm is added to the calculation of the average speed of the target, which can be used as the target speed when the target is occluded to predict the area where the target may appear at the next moment, and it can also be used as a judgment condition for whether the target is interfered by the background. The improved algorithm combines the macroscopic motion information of the target, can overcome the problem of target loss when the target is occluded and background interference, and improves the robustness of target tracking. Using LabVIEW development environment to write the system software of the Japanese standard tracking robot, the experiment verified the rationality and correctness of the improved target tracking algorithm and motion control method, which can meet the real-time performance of moving target tracking. Experimental results show that 83% of amateur badminton players have problems with asymmetric functions and weak links. Based on machine vision technology, it can provide reliable bottom line reference for making training plans, effectively improve the quality of action, improve the efficiency of action, and promote the development of sports competitive level.

Autonomous Analysis of Infrared Images for Condition Diagnosis of HV Cable Accessories

Infrared thermography has been used as a key means for the identification of overheating defects in power cable accessories. At present, analysis of thermal imaging pictures relies on human visual inspections, which is time-consuming and laborious and requires engineering expertise. In order to realize intelligent, autonomous recognition of infrared images taken from electrical equipment, previous studies reported preliminary work in preprocessing of infrared images and in the extraction of key feature parameters, which were then used to train neural networks. However, the key features required manual selection, and previous reports showed no practical implementations. In this contribution, an autonomous diagnosis method, which is based on the Faster RCNN network and the Mean-Shift algorithm, is proposed. Firstly, the Faster RCNN network is trained to implement the autonomous identification and positioning of the objects to be diagnosed in the infrared images. Then, the Mean-Shift algorithm is used for image segmentation to extract the area of overheating. Next, the parameters determining the temperature of the overheating parts of cable accessories are calculated, based on which the diagnosis are then made by following the relevant cable condition assessment criteria. Case studies are carried out in the paper, and results show that the cable accessories and their overheating regions can be located and assessed at different camera angles and under various background conditions via the autonomous processing and diagnosis methods proposed in the paper.

A Mean Shift Algorithm for Drift Correction in Localization Microscopy

Single molecule localization microscopy (SMLM) techniques transcend the diffraction limit of visible light by localizing isolated emitters sampled stochastically. This time-lapse imaging necessitates long acquisition times, over which sample drift can become large relative to the localization precision. Here we present a novel, efficient, and robust method for estimating drift using a simple peak-finding algorithm based on mean shifts that is effective for SMLM in 2 or 3 dimensions.