The segmentation of interesting objects within the image is an important process to provide information for other procedures. The major problem of image segmentation is the number of objects suitable for classifying objects. This research aims to present on Image Segmentation using Fuzzy C- Mean Via Image Filter. The objects will be split up using their object intensities. The proposed method can classify several objects at the same process. Therefore it saves time to process less than other existing methods. The proposed method is consisted of three steps: noise reduction histogram smoothing and object classification. The results of the proposed method found that objects are automatically classified by Fuzzy C-Mean. Moreover the algorithm that successfully calculates the proper number of segmentations

This report aims to present the random noise elimination of image based on edge preservation. This solution is a pixel processing using 29 templates. Each template consists of the considered pixel and its neighbor pixels. To eliminate noises the smoothing process replaces the new pixel intensity into the same considered processing pixel by the mean intensity of neighbor pixels. The condition of the template selection is based on the minimum variance of templates. As the experimental results this solution brings the peak signal-to-noise ratio (PSNR) higher than Gaussian filter Tomita’s marks Fukuda’s marks and Nagao’s marks and also gains contrast difference.

The image enhancement process of a digital image is a part of digital signal processing which it plays an important role in some industrial segments for instance object classification or bio-identification satellite exploration or aerial images robot vision cancer treatment etc. These image applications are necessary to consume many resources in physical hardware and computer programming including the intelligent algorithm development of complexly advanced processing in order to enhance digital images – as well as be easy to analyse by humans. This research is aimed to present on Contrast Enhancement using Bi-Histogram Equalization via Two Dimensional Discrete Wavelet Transform (BHE2DWT) in order to compose the coefficient wavelet from other signals. At the end of the process information on the approximation band from DWT will be divided into two sub-histograms and then histograms will be independently equalized. After that resulting histograms will be decomposed by IDWT. Resulting images will be not only more displaying in high contrast than their original images but also preserve the absolute mean brightness error (AMBE) to close their original images.

Image enhancement is one of using in various digital signal processing areas. Advances in microcontrollers DSP boards and computers have been developed traditional algorithms to improve the quality of the resulting image. This paper aims to present the contrast enhancement using weighted bi-histogram equalisation. In additional this technique must use two weighted factors which is calculated by the histogram distribution ratio. Likewise an original image will be equalised by the modification of the probability density function of the gray levels. As the experimental results the contrasts of resulting images are improved for robot vision implement and human perception including absolute mean brightness error (AMBE) limitation.

This research aim to improve a combustion system of boiler within increase combustion efficiency and use all out of the energy. The large boilers were used in the industrial factories which consume a lot of energy for production. By oil and gas fuel will be increasing costs everyday cause many factories interested in energy saving with any method technical engineering specifically for production costs and environment effect decreasing. This researching was installed and program was invented in the industrial factory. This industry factory consumed cogeneration energy for fabric dying. The efficiency before installing the software is measured about 65.85 - 71.98% which heat in exhaust gas about 20% of overall energy is filled in the system. After installing heat loss in the system has been fallen until remain about 5 - 12% and efficiency of heat in system has been reached a peak of 80 - 85%.