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Coloured tiles from two northern Indian monuments were analysed for their body and glaze composition. The results suggest that three different groups of tiles were used, all comprising a stonepaste body with alkali glaze. One group has strong similarities to a major Indian glass group, known as high alumina mineral natron glass, while the other two are similar to Western and Central Asian plant ash glazes, although with much lower lime content. The colorants conform with those usually employed in pre‐modern glazes, with lead‐tin yellow Type I and Type II for opaque yellow, copper blue‐turquoise, cobalt blue, manganese purple, and green through mixing of lead‐tin yellow and copper blue.

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ABSTRACT:Image fusion is a process of integrating multiple images of the same scene into a single output fused image. It minimizes redundancy and reduce uncertainty and extract all the useful information from the source images. The process of image fusion is required for different applications like remote sensing, medical imaging, machine vision and military applications where critical information and quality is required. In this paper the image fusion using the combination of wavelet transform and adaptive neuro fuzzy logic is implemented. The results are compared with the pixel level image fusion in spatial domain with fuzzy and neuro fuzzy logic approach along with the quality evaluation indices for image fusion like entropy, RMSE(Root Mean Square Error), PSNR(Peak Signal to Noise Ratio) and Correlation Coefficient. Experimental results prove that the above algorithm is better than the other fusion techniques.KEYWORDS: fuzzy logic, neuro fuzzy logic, image quality indices, root mean square error, peak signal to noise ratio, correlation coefficient. I.INTRODUCTIONAny piece of paper makes sense only when it is able to convey the information across. The clarity of information is important. Image Fusion is a mechanism to improve the quality of information from a set of images. By the process of image fusion, the good information from each of the given images is fused together to form a resultant image whose quality is superior to any of the input images. This is achieved by applying a sequence of operators on the images that would make the good information in each of the image prominent [20]. The resultant image is formed by combining such magnified information from the input images into a single image.The input images may be from different sensors [9], medical images [11,16], remote sensing images [7]. Image fusion aims at aggregating two or multiple images from same information sources, so as to achieve improved accuracy and robust inference performance by utilizing redundancy and complementariness in information. It is used for medical di agnostics and treatment. A patient's images in different data formats can be fused. These forms can include magnetic resonance image (MRI), computed tomography (CT), and positron emission tomography (PET). In radiology and radiation oncology, these images serve different purposes. For example, CT images are used more often to ascertain differences in tissue density while MRI images are typically used to diagnose brain tumors. Image fusion is also used in the field of remote sensing wherein multivariate images like thermal images, IR Images, UV Images, ordinary optical image etc can be fused together to get a better image taken from a satellite. II. LITERATURE SURVEY