DEVELOPMENT PROCESS OF THE SEWING PATH

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DEVELOPMENT PROCESS OF THE SEWING PATH

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Automatic sewing machine can produce a perfect pattern on the cloth according to the faultless stitch data. The image needed to be processed by the following steps:

A. Noise processing The unexpected noise will be introduced during the image processing process by the following factors: the accuracy of the sampling sensor, signal processing, data processing, interference between the sensors, and data transfer process. Merritt Sewing Machine Price list in Chennai – VS Sewing Machines Noise production in the digitization of the image will greatly affect the accuracy of the conversional calculation between the sewing pattern and the sewing path. Therefore, the reduction of the noise effect will be an important process for the image processing.

B. Binarization processing Binarization processing is based on a designated threshold [4]. If the grayscale of the pixel of an image is greater than the threshold, the grayscale of the pixel is set to be one. Otherwise, the grayscale is set to be zero for the grayscale less than the threshold. Binarization processing will result in am image with the binary format and is an important process for the character or the text recognition.

C. Edge thinning Edge thinning will enhance the image recognition. This study implements the skeletonization process on the binarization image to obtain am image represented by the thinning edges. Skeletonization process reduces the object of the image into to an pixel-wide line that still preserves the figure structure produced by the binarization processing [11,12]. For example, length, direction and position of the line will be preserved.

D. Feature Recognition Feature recognition is used to detect two important points along the image line: branch points and end points.

(1) Detection of the branch points: Branch points are defined as the points with the level of the branchness along the axial of the branch center line to be greater than two [3]. An example is shown on Fig. 3. The process to find the branch point is to scan each foreground pixel p1 and calculate the number of the switching time from background to foreground by counting order p2, p3, …, p9. Fig. 4 depicts an example of the position order for calculation of the branchness. The example on the Fig. 5 shows the branchness to be three.

(2) Detection of the end points: End point is the point with the branchness of one.

E. Path planning We may regard each branch point and end point as the vertex and the central axial connected the adjacent vertexes to be the edge. The original image can be represented by these edges to form a graph that can be used as the building block for the creation of the sewing path. The algorithm of the path planning is involving the following two parts [6]:

(1) Finding the longest path along the graph

(2) Finding the edge beside the longest path by using the method of Depth-First-Search (DFS) If the searching process passes through all the edges, then the sewing path will be created.