Image Segmentation Method for an Illumination Highlight Region of Interior Design Effects Based on the Partial Differential Equation

The saliency calculation model based on the principle of partial differential equations sometimes highlights areas with high contrast in the background, and the salient targets obtained occasionally have holes. The above problems can be solved by combining the improved convex hull calculation center saliency map. This paper designs a single-target color image segmentation algorithm based on partial differential equations. First, we calculate the basic saliency map according to the uniqueness of the color and the spatial distribution of the color; second, we then use the superpixel to improve the convex hull and calculate the central saliency map according to the principle; finally, the basic saliency map and the central saliency map are calculated. The weighted fusion is used to obtain the comprehensive saliency map, and the threshold method is used to segment the comprehensive saliency map to obtain the final target image. This paper designs an evaluation standard suitable for the segmentation of the illuminated highlight area of the effect image. It compares the experimental results of the segmentation method in this paper with the SLIC (Simple Linear Iterative Clustering) method and the traditional superpixel method to segment the illuminated highlight area. The segmentation method is applied to the image enhancement experiment. Based on the fuzzy means clustering algorithm, a fuzzy clustering objective function including brightness, color, and distance parameters is designed, which improves the weight of the brightness value in the clustering and improves the edge fit of the segmentation of the lighting highlight area of the rendering. The segmentation method produced by combining the clustering method with the superpixel biased clustering method can improve the output effect of the illuminated highlight area of the effect image after segmentation. We perform color equalization processing on the image to be segmented to reduce the impact of light, then set the closed value of the brightness information component, perform segmentation judgment, and expand the long and short axes of the ellipse model in the high-brightness area to further reduce the impact of light. The experimental results prove that the above method has a better segmentation effect than the traditional ellipse model and can accurately segment the gesture image. Compared with the existing mainstream saliency calculation models, this algorithm is closer to the true value image in terms of visual effects and has obvious advantages in terms of accuracy.

A directional ellipse to describe directional behavior and player activity area in soccer

This study aimed to compare four standard deviational ellipse models to assess directional behavior and player activity area in four small-sided games (SSG) of soccer played on pitches with the same width (40 m) and different lengths (30, 40, 50, and 60 m). Fourteen participants played four 7-a-side SSGs on each of the four pitch sizes. Based on GPS data, four ellipse models were calculated for each outfield player and pitch size: major ranges (MR) measuring standard deviation in fixed length and width directions, linear regression assuming length (LR LvsW) or width (LR WvsL) as the independent variable, and principal component analysis (PCA) assuming both length and width as independent variables. Slope, area, semi-major and semi-minor axes, and eccentricity were calculated for each ellipse model. The PCA and LR LvsW models showed similar and valid results for each variable, especially for larger pitch sizes. LR WvsL showed unreliable results. The length axis should be considered as the independent axis when assessing the main direction of players’ movements and playing area through a standard deviational ellipse in soccer. This methodology could also be applied to evaluate a team’s labor distribution and spatial distribution of its players.

Anthropometric Landmarks Extraction and Dimensions Measurement Based on ResNet

Anthropometric dimensions can be acquired in 2D images by landmarks. Body shape variance causes low accuracy and bad robustness of landmarks extracted, and it is difficult to determine the position of axis division point when dimensions are calculated by the ellipse model. In this paper, landmarks are extracted from images by convolutional neural network instead of the gradient of body outline. A general multi-ellipse model is proposed, the anthropometric dimensions are obtained from the length of different elliptical segments and the position of axis division point is determined by thickness–width ratio of body parts. Finally, an evaluation is completed based on 87 subjects, in which it turns out that the average accuracy of our method for identifying landmarks is 96.6%, when the number of rotation angles is 2, the three main dimensional errors calculated by our model are smaller than existing method, and the errors of other dimensions are also within the margin of error for garment measuring.

An improved bootstrap method introducing error ellipse for numerical analysis of fatigue life parameters

Purpose The purpose of this paper is to introduce error ellipse into the bootstrap method to improve the reliability of small samples and the credibility of the S-N curve. Design/methodology/approach Based on the bootstrap method and the reliability of the original samples, two error ellipse models are proposed. The error ellipse model reasonably predicts that the discrete law of expanded virtual samples obeys two-dimensional normal distribution. Findings By comparing parameters obtained by the bootstrap method, improved bootstrap method (normal distribution) and error ellipse methods, it is found that the error ellipse method achieves the expansion of sampling range and shortens the confidence interval, which improves the accuracy of the estimation of parameters with small samples. Through case analysis, it is proved that the tangent error ellipse method is feasible, and the series of S-N curves is reasonable by the tangent error ellipse method. Originality/value The error ellipse methods can lay a technical foundation for life prediction of products and have a progressive significance for the quality evaluation of products.

UTSM: A Trajectory Similarity Measure Considering Uncertainty Based on an Amended Ellipse Model

Measuring the similarity between a pair of trajectories is the basis of many spatiotemporal clustering methods and has wide applications in trajectory pattern mining. However, most measures of trajectory similarity in the literature are based on precise models that ignore the inherent uncertainty in trajectory data recorded by sensors. Traditional computing or mining approaches that assume the preciseness and exactness of trajectories therefore risk underperforming or returning incorrect results. To address the problem, we propose an amended ellipse model which takes both interpolation error and positioning error into account by making use of motion features of trajectory to compute the ellipse’s shape parameters. A specialized similarity measure method considering uncertainty called UTSM based on the model is also proposed. We validate the approach experimentally on both synthetic and real-world data and show that UTSM is not only more robust to noise and outliers but also more tolerant of different sample frequencies and asynchronous sampling of trajectories.