## 6 Conclusions  
In this paper, a hybrid method has been proposed for repeated pattern detection on unknown fronto-parallel images. Unlike previous methods that rely solely on image features or prediction models, the proposed method combines deep features and an autocorrelation model to achieve a good balance between accuracy and efficiency. Specifically, the method introduces a filter selection strategy based on edge detection results to extract the most effective filters for feature extraction. Furthermore, a dynamic similarity threshold has been incorporated into the autocorrelation model for template matching, enabling more accurate repeated pattern size optimization. A dataset (RPD) containing 774 real fabric scanned images and 61 computer-generated images has been built and shared for comparative study. Experimental results on the RPD dataset have demonstrated that the proposed method achieved the highest overall accuracy (67.3%), with 65.8% accuracy on real fabric images and 84.4% on computer-generated images. The average runtime was 40 s per image, which was only 11% of the runtime of the baseline method, while requiring just 0.1 GFLOPs and 10 MB of memory, compared to 0.72 GFLOPs and 60 MB for the baseline method. These results illustrate the effectiveness of the proposed filter selection strategy, resulting in the best balance between accuracy and efficiency. Further experiments have been conducted to analyze the contributions of different components in the proposed method. Replacing AlexNet with deeper CNN models such as VGG16 and ResNet50 showed that deeper networks do not significantly improve accuracy when the number of selected filters is fixed, but they substantially increase runtime. Similarly, replacing the Canny edge detector with a deep learning-based boundary detector (BDCN) does not lead to significant accuracy improvement while slightly increasing the time costs. These results validate that the proposed method achieves an effective trade-off  
Figure 10: Detection results of environmental images: (**a**) the detection results from [12], and (**b**) the results from the current method. between accuracy and efficiency by carefully balancing feature selection and computational complexity. In addition, the proposed method has demonstrated good adaptability and practical value in real-world applications, such as design element extraction and resource preparation in textile design. Beyond textiles, the method has been shown to be able to detect diverse repeated patterns, highlighting its potential for broader applications, such as defect detection and texture-based image retrieval. In the future, further work should focus on developing a better fusion algorithm for consistent displacement vector selection from selected feature maps, allowing more activation peaks to be considered for improved accuracy. Moreover, a more advanced backbone network could be explored to further enhance both accuracy and efficiency. Beyond this, another key direction is to extend the application of repeated pattern detection results to other tasks, such as fabric defect detection, texture-based image retrieval, and structural pattern analysis in real-world scenarios. These efforts will broaden the impact of repeated pattern detection and explore its potential in various application fields. Conceptualization, Y.Z. and P.Y.M. ; Methodology, H.Q. and Y.Z. ; Software, H.Q. and G.F.; Formal analysis, H.Q. and Y.Z. ; Investigation, H.Q. ; Resources, G.F.; Writing--original draft, H.Q. ; Writing--review and editing, P.Y.M. and L.L. ; Visualization, L.L. ; Supervision, P.Y.M. and L.L. ; Funding acquisition, P.Y.M. and L.L. All authors have read and agreed to the published version of the manuscript. The work described in this paper was supported by grants from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project Nos. 152112/19E and 15602323). The work was also supported, in part, by The Hong Kong Polytechnic University (Project Nos. P0049355/CD95 and P0051330/BDVH).