关键词: 百褶裙, 机器视觉技术, 动态美感, 美感评价, 图像识别

Abstract:

Objective The study aimed to assess the dynamic aethetics of pleated skirts, a hallmark of Chinese tradition celebrated for their fluid and elegant movement. Recognizing the gap in scientific methods for measuring the beauty of traditional ethnic garments in motion, it leveraged machine vision technology and high-speed photography to develop quantifiable visual feature indices and a comprehensive mathematical model.

Method The study utilized machine vision technology integrated with high-speed cameras to evaluate objectively the dynamic aesthetics of pleated skirts. A custom dynamic image capture device was designed, incorporating a rotating display platform and multiple high-speed cameras positioned at different angles to capture dynamic images of the skirts at various rotational speeds. Multi-angle image capture was firstly carried out at different rotational speeds to ensure a comprehensive recording of the dynamic process. Subsequently, machine vision algorithms were applied to process and analyze these images, extracting key dynamic visual features such as projected contour area, lateral expansion area, skirt width, expansion angle, and liveliness. These features were systematically quantified to assess the dynamic aesthetics of the skirts under different speed conditions.

Results This study conducted an in-depth analysis of the dynamic aesthetics of pleated skirts using machine vision technology and high-speed cameras, uncovering the relationship between motion and perceived beauty. A custom dynamic image capture device was utilized, equipped with a high-speed industrial camera capable of capturing up to 245 frames/s, and a dynamic display platform that allowed for precise control of the skirt's rotational speed. The study extracted and quantified dynamic visual characteristics such as projected contour area, lateral expansion area, skirt width, expansion angle, and liveliness using this setup. Based on these quantified dynamic features and subjective evaluations from experts, regression analysis was employed to develop a mathematical model capable of objectively assessing skirt aesthetics. Within the rotational speed range of 24 r/min to 48 r/min, various dynamic characteristics of the skirts were extracted and quantified, where the projected contour area were considered between 2 281.9 cm²and 8 253.0 cm², the lateral expansion area from 4 112.3 cm² to 6 278.3 cm², skirt width from 36.427 cm to 51.178 cm, the expansion angle from 32°to 101°; and the liveliness index from 60.2% to 79.1%. These data indicate significant differences in the dynamic performance of pleated skirts at different rotational speeds. Subjective evaluations of the dynamic aesthetics of pleated skirts were carried out by fashion professionals at different rotational speeds using five main aesthetic criteria, which are drape, overall aesthetics, contour curve aesthetics, airiness, and pleat uniformity, using a scoring scale from 1 to 10. For overall aesthetics, the average score was 6.569 with a standard deviation (SD) of 1.963, wheras the average score for drape was 6.053 (SD 1.873), the average score for contour curve aesthetics was 6.224 (SD 1.955), the average score for airiness was 6.211 (SD 2.121), and the average score for pleat uniformity was 5.629 (SD 2.220). The entropy method was employed to calculate a weighted summation of these scores, yielding the final subjective aesthetic score. Pearson correlation analysis revealed a strong correlation between the dynamic visual characteristics of the skirts and the final aesthetic scores obtained from the subjective evaluation. Within the rotational speed range of 24 r/min to 48 r/min, changes in projected contour area showed the strongest correlation with aesthetic scores, with a correlation coefficient as high as 0.893 for flowing elegance and 0.882 for overall aesthetics, underscoring its critical role in visual appeal. Lateral expansion area and expansion angle also exhibited significant positive correlations with aesthetic scores, with correlation coefficients of 0.741 for lateral expansion area and airiness, and 0.740 for expansion angle and overall aesthetics. In contrast, liveliness displayed a weaker correlation but still had some influence, such as a correlation coefficient of 0.644 with drape. Skirt width showed a negative correlation with aesthetic scores, with relatively low correlation coefficients across all aesthetic criteria, such as -0.486 with overall aesthetics, indicating that within a certain speed range, wider skirts may detract from their dynamic aesthetic appeal. Regression analysis was performed, using the final aesthetic score as the dependent variable and the quantified dynamic visual indices as independent variables, resulting in the establishment of a mathematical model for evaluating the dynamic aesthetics of skirts. The regression analysis revealed significant differences in the impact of the main visual indices on aesthetic scores. The non-standardized coefficient for the constant term was 4.519, with a very high level of significance (P < 0.001). Among the indices, the projected contour area had the highest standardized coefficient of 0.563, indicating the greatest influence on aesthetic scores (P < 0.001). The lateral expansion area followed, with a standardized coefficient of 0.294 (P < 0.001), also showing a significant impact. The influence of dynamic skirt width was minor, with a standardized coefficient of 0.036, and was not significant (P = 0.660). The adjusted R²of the regression model was 0.811, indicating that the model explained 81.1% of the variance in aesthetic scores, and the overall regression model was highly significant (F = 78.224, P < 0.001), demonstrating a high level of fit. These findings further validate the effectiveness of the machine vision-based method for objectively evaluating skirt aesthetics.

Conclusion This study developed an analysis device based on machine vision technology, capable of accurately identifying and quantifying the dynamic visual characteristics of skirts. By leveraging machine vision, the device effectively addresses the challenge of accurately measuring skirt parameters in dynamic conditions, significantly improving both measurement efficiency and precision. Moreover, it mitigates the subjective limitations of traditional manual evaluation of skirt aesthetics. The regression model, constructed using data obtained from this device in combination with subjective assessments, exhibits a high degree of fit, further demonstrating the feasibility of using this device for dynamic evaluation of skirts. The objective evaluation mathematical model established from this foundation also contributes to the standardization and quantification of aesthetic evaluation in the fashion industry. This research not only pioneers a new method for evaluating the dynamic aesthetics of skirts but also provides valuable data references for skirt design. In the future, the device and evaluation model can be further optimized to accommodate a wider variety of garments. Additionally, by integrating artificial intelligence, it will be possible to explore the variations in garment aesthetics under diverse environmental conditions, thereby enriching the breadth and depth of research in garment aesthetics.

Key words: pleated skirt, machine vision technology, dynamic aesthetics, aesthetic evaluation, image recognization