No abstract

This series of studies proposed an automatic machine embroidery image color analysis system. Based on the machine embroidery image color separation system proposed in part I of this series of papers (Kuo CFJ, Jian BJ and Wu HC. Automatic machine embroidery image color analysis system, part I: using Gustafson-Kessel clustering algorithm in embroidery fabric color separation. Textil Res J 2011, submitted for publication), this paper presents an automatic repetitive pattern image recognition algorithm for machine embroidery. Firstly, the image pre-processing is implemented to eliminate image acquisition noise and smooth the texture of machine embroidery. After the completion of image pre-processing, the genetic algorithm (GA) is applied to obtain the color image of repetitive patterns. The novel fitness function proposed in this paper can increase the calculation of the color entropy value to improve the measurement block accuracy of color similarity in previous literature. When applied to the GA, the system can accurately identify and cut out images of repetitive patterns. In addition, since the proposed fitness function has been normalized, it can accurately calculate the optimal target function, indicating that the proposed algorithm can automatically and accurately identify and cut out the images of repetitive patterns in machine embroidery.

This series of study proposed the automatic machine embroidery image color analysis system as an extension of the previous proposed machine embroidery color separation system and repetitive pattern search system. This paper integrated the machine embroidery image color analysis system to achieve system automation. The system is divided into three stages: (1) acquire the embroidery fabric image, filter the acquired image noise, and smooth the embroidery fabric texture before using the transform to reduce image information amount and improve subsequent calculation speed; (2) use the genetic algorithm for the repetitive pattern search, and restore the identified repetitive pattern images to the original size by image pyramids, then use frequency domain template matching to determine the locations of the repetitive patterns to verify repetitive pattern accuracy; (3) apply the Gustafson–Kessel cluster algorithm and cluster validity partition index SC to obtain the machine embroidery image’s number of colors and corresponding areas, then employ half-toning technology to determine color types for chromatography. The integrated system can accurately identify the repetitive patterns for chromatography to realize the automatic drafting of embroidery fabric. It can be further integrated with automatic manufacturing equipment for machine embroidery automation.