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.

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.

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%.

Since the details in quasi-homogeneous region will be degraded from a conventional global image enhancement methods such as histogram equalization. This degradation causes from the saturation of the gray levels in equalization process. Then the local histogram equalization for each quasi-homogeneous region will be used in order to improve the details in the regions. To obtain the quasi-homogeneous regions the image can be segmented by using a watershed transform. The watershed transform is based on mathematical morphology; therefore the segmented regions can be effectively separated meaningful. Two adjacent regions in images which almost similar in gray levels can be separted by the watershed transform. In the color image enhancement three spectral bands are enhanced independently. Then all the three bands are assigned to each processed image in order to produce a color composite image. By the proposed algorithm the result images show the better perception on image details. Therefore the high efficiency of image classification can be obtained by using this color image.

Currently competition in terms of resolution and contrast of photos from digital cameras are widely mentioned. The higher resolution the photos contain; the more details the picture has. This fact enables the more details of pictures which also hold much more capacity of data. This also causes the electronic devices to be updated all the time to cope with those problems. Therefore the conventional devices are obsolete. This research has been developed and applied to improve digital photos with high contrast. The researcher has solved the problems by keeping the average error of the lowest results and improving heat histogram of various curves. The heat will break the histogram of the original image into n part by the concave side of the heat histogram. The part of this histogram is equal to n+1 and then smooth each other heap histogram independently. The result showed that the Tennengrade or contrast of the image is higher than the original one. However it can retain brightness errors (AMBE) as well