This research work presents the development of algorithms and programs used for classifying of white rice grains obtained from the camera by using image processing techniques. This procedure begins with improving input images converting color images to binary images by setting the threshold value automatically by using the Otsu’s method noise filter process image processing with structural elements that form a square determining the grain boundary by using the Canny’s operator determining the length of the grain by using the Euclidean’s method. Next the length results of the grains have been obtained from the decision process based on the Rice Standards of Thailand (RST) in order to receive the type solutions of tested white rice. From the testing results for 800 images it has been shown that in case of 500 images the developed program provided the mean absolute error of the grain length of 0.01 mm per grain. Moreover for testing of 300 images the developed program provided the separation accuracy of the grains up to 99.33% in term of the RST. This can be useful in determining the price of white rice in a rice mill with farmers.

This research report is manipulated that summarized the moment of research project in the subject of “The optimal image processing for paddy studies: Variety classification and rice kernel damage inspection”. This project had been take time in 12 mounts. We focused on rice variety classification and rice kernel damage inspection. We also investigated the apparent friction of rice surface. Results showed that area and size were the quantities that have the potential to classify rice variety and also rice kernel damage inspection. In addition we also found that geometric mean diameter performed characteristic to apparent friction of rice surface in kerosene. These results have the potential to be the basis information in terms of developing instrument for quality control and variety classification of cereal.

In present there are widely use robot in many fields. Mobile robot can help us in several task such as moving to toxic area risk area or explosion area mine. Movement control can be used in many ways. This paper proposed mobile robot that movc by wheels. It move to previous defined picture. By using small camera process an images and check it that is the previous one by using neural network. If it is robot will move toward to that picture. The movement is in flat plane. If that defined picture is not in center robot will move to left or right depend on its position until it is on center. This paper proposed image processing only.

In present there are widely use robot in many fields. Mobile robot can help us in several task such as moving to toxic area risk area or explosion area mine. Movement control can be used in many ways. This paper proposed mobile robot that move by wheels. It move to previous defined picture. By using small camera process an images and check it that is the previous one by using neural network. If it is robot will move toward to that picture. The movement is in flat plane. If that defined picture is not in center robot will move to left or right depend on its position until it is on center. This paper proposed image processing only.

This thesis emphasizes the improvement approaches of quality on digital data to be more sharpness by two- dimensional technique. Today the availability from the improvement of Digital Image Processing is used widely in various occupations such as medical field with reading the results from CT scanning military field with the improvement of aerial map for country prevention agricultural field with leaf analysis of its disease and the educational field with the image processing and robot vision to increase its effective learning and remembering development. It can be seen that the image development through its sharpness is really crucial therefore this thesis used the Gray Scale for transforming by two-dimensional wavelet and LL Band got from this wavelet transform to be improved by histogram technique. The histogram was divided into 4 parts based on its cumulative density at 0.25 0.5 0.75 and 1 then smoothed the data by separation after that brought the results to invert wavelet which gives the new image through improved LL band. The research result revealed that the mean of the Absolute Mean Brightness Error (AMBE) was decreased while enable the image sharpness to suitably increase 80%.