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Girdhar, Akshay
- A Constrained Based Bi Level Histogram Equalization Method
Authors
1 CSE Department with Chandigarh Group of Colleges, Mohali, IN
2 CSIO, Chandigarh, IN
3 GNDEC, Ludhiana, IN
Source
Digital Image Processing, Vol 3, No 12 (2011), Pagination: 733-736Abstract
These days images are being captured by digital cameras and other systems. Though the quality of images taken by these systems has been improved up to high extend, but there are some artifacts do exists with images. Still images are required to be enhanced. This paper proposes a novel method Constrained BasedBased Histogram Equalization Method(CBBHE) for image contrast enhancement .CBBHE is the extension of Image Inversion and Bi-Level Histogram Equalization (IIBLHE) method. In the proposed method, an input image is divided into two sub images. Then the probability distribution functions of sub images are modified by introducing constraints. Experimental results are presented and compared with results from IIBHE method.
Keywords
Contrast Enhancement, Cumulative Density Function (CDF), Histogram, Histogram Equalization, Probability Density Function (PDF).- A Technique for Glass Defect Detection
Authors
Source
International Journal of Innovative Research and Development, Vol 2, No 13 (2013), Pagination: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.