| Summary: | 碩士 === 國立成功大學 === 電機工程學系碩博士班 === 90 === The traditional capacitor recognition is manual, unreliable, and inefficiency. Automatic recognition would therefore have great benefits and have been widely adopted for various automatic visual inspections in today’s industry. This thesis implements a recognition system to calculate how many layers in a MLCC image. The main difficulties of the problem includes: without template image, noise near each layer…, etc.
The proposed algorithms combined basic image processing and image transform techniques in our recognition system to find the number of layers. Our algorithm consists of three parts: (1) skew detection and alignment, (2) object segmentation and (3) layer number calculation. In the first part, by using the information from centroid and direction, block matching algorithm is performed to find the skew angle and then the image is aligned via a rotation matrix. In the second part, projection is performed first. After that we use wavelet frames and fuzzy c-means to locate the layer boundary in the image. At last part, fast Fourier transform and pitch detection is performed to find pitch between layers. The number of layers is computed by combing the pitch and the layer boundary.
In the experiments, we used eight products with total 256 images as test samples. The error is defined as the difference between the actual layer number and the number computed from the computer system. According to our experimental results, the errors are less than 0.5 for all 256 images. As for the processing time, 443.4 ms in average is required by one image and the average of the image size is 300×300.
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