International Journal of Innovative Research and Development

Abstract

Image is produced to record or display useful information but due to imperfections in the image capturing process and devices, the recorded image invariably represents a degraded version of the original. These degradations may be as a result of addition of noise, geometrical degradations, illumination and blur. These problems degrade the final quality of the acquired images for display and thus complicate the task of finding the error in object recognition and event detection. The primary objective of this paper is to develop a method for filtering, clearing the historical and degraded documents. ‘Wiener filter algorithm’ use to clear degraded historical handwritten documents .Experimental results is calculated by using MSE(i.e. mean square error) and PSNR (i.e. peak signal noise ratio).This algorithm helps to get the maximum accuracy and performance.  

Keywords

Full Text

Abstract

Glass is a material which is used in the industry and household. The presence of defects or weaknesses in the glass has serious implications. In a glass substrate, the grey level of defects and background are hardly distinguishable and results in a low contrast image. The primary objective of this paper is to develop a method for detection of defects in a glass surface image such as subtle defect , bubble defect , dirt defect checks or marks defect etc. The paper proposes artificial neural network based methodology to detect the defects in the glass Gray level Concurrence Matrix (GLCM) has been used for feature extraction. The neural network is responsible for making intelligent classification based on observations done for various types of glass defects. Experimental results developed a classification matrix and by which performance goal based on MSE (mean square error) is set. .This technique helps to get better results.

Keywords

Full Text