جستجوی مقالات مرتبط با کلیدواژه « image processing » در نشریات گروه « صنایع »

Statistical process control plays an impressive role in industries due to the growing complicated products and processes. This tool helps practitioners prevent the production of defected products and waste of money and time. Due to the importance of processes and inefficiency of methods based on human inspection, the use of image data for statistical process control has gained great attention among researchers in recent years. Image data have been applied by industries for many years for separating defected products and preventing them to get to customers. In recent years, some methods are proposed by researchers in the area of applying statistical features of image data in statistical process control. Image data analysis is decomposed into two categories: spatial domain and frequency domain. The main concentration of previous research studies in the area of process monitoring using image data is in the area of spatial domain analysis. This study has proposed three methods based on one-dimensional wavelet decomposition for monitoring image data with respect to frequency domain features. At each level of decomposition, wavelet transformation decomposes each signal into two elements including an approximation part (which is similar to the main signal and is performed as a low-pass filter) and a detail element (which is performed as a high-pass filter). The first method proposed in this paper only applies approximation coefficient for process monitoring. The second and third methods consider the detail coefficient by using hard thresholding and soft thresholding, respectively. These methods use a likelihood ratio-based statistic for process monitoring. Hence, they can show an out-of-control status and estimate the change point that is one of the most important diagnostic information. The performance of these methods is evaluated and compared with respect to the average run length and the difference between real and estimated changepoint criteria. Simulation studies are performed by using a textile image. Results showed a suitable degree of accuracy in detecting out of control status and estimating change points.

In this paper, using the Growing Neural Gas (GNG), the graph associated with a typed Persian character is extracted from the image. This graph learns the shape of the character and is used for character recognition. GNG, is one of the self-organized algorithms and a competitive learning approach. The main achievement of this method in addition to speed and accuracy is resistance to major challenges such as rotating, noise and deformation. This algorithm takes the image of a typed Persian character as input and returns the graph associated with that character.

Nowadays, close competition and high cost of production cause the loss reduction and waste recovery to be one of the most concern of industrial and production companies and factories. For this, managers are looking for new methods to reduce the defect in the product line of the factories. Image processing and machine monitoring systems are technologies used in different fields of military, physician, agriculture, industry, etc. Purpose of this study is to use an image processing model to recognize the defect in the product line. As a case study, oil bottles produced by Shiraz Vegetable Oil Company are used. Input data to the program is the images of the intact and defected oil bottles and output is the final judgment of program about the correctness of the bottles. Image processing is performed using software MATLAB. In this study two different procedures are used to identify the intact and defected oil bottles. The first theory is based on the comparison of the area of intact and defected bottle images and the second theory compares the ratio of the height to the sum of some widths for two different images. Second theory uses edge function algorithm and the result obtained from this theory is more optimize with respect to the first theory. This theory is programmed without using the image of an intact object as reference image. This is one of the advantages of second theory with respect to the first one; because a small defect in reference image of the first theory can strongly affect the results. This research is also, consist of two recommended theories. The first theory is based on a reference point in the image and summarizing some of the distances from the edge of the bottle to the reference point. Such, that acceptable results can be obtained by comparing the summarized distances in the intact and defected bottle. The second recommended theory is based on the comparison of the center of area in the intact and defected bottles.