Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320–088X IJCSMC, Vol. 2, Issue. 5, May 2013, pg.82 – 85 RESEARCH ARTICLE © 2013, IJCSMC All Rights Reserved 82 Study on Various Glass Defect Using Glass Edge Detection Methods Sumeet Singh Tiwana 1 , Sukhpreet Kaur 2 1 Student of masters of technology Computer Science, Department of Computer Science Engineering, Sri Guru Granth Sahib World University, Fatehgarh Sahib, Punjab, India 2 Assistant Professor, Department of Computer Science and Engineering, Sri Guru Granth Sahib World University, Fatehgarh Sahib, Punjab, India 1 Sumeet.tiwana77@gmail.com; 2 preetsukhpreet@gmail.com Abstract— Glass defects are a major reason for poor quality and of embarrassment for manufacturers. It is a tedious process to manually inspect very large size glasses. The manual inspection process is slow, time- consuming and prone to human error. Automatic inspection systems using image processing can overcome many of these disadvantages and offer manufacturers an opportunity to significantly improve quality and reduce costs. In this paper we review various glass defects and the possible automated solutions using image processing techniques for defect detection. Key Terms: - Defects; methods; processing I. INTRODUCTION [1] Digital image processing- Image processing is a method to convert an image into digital form and perform some operations on it, in order to get an enhanced image or to extract some useful information from it. It is a type of signal dispensation in which input is image, like video frame or photograph and output may be image or characteristics associated with that image. It is among rapidly growing technologies today, with its applications in various aspects of a business. Image Processing forms core research area within engineering and computer science disciplines too [1]. An image may be considered to contain sub-images sometimes referred to as regions-of-interest, ROIs, or simply regions. This concept reflects the fact that images frequently contain collections of objects each of which can be the basis for a region. In a sophisticated image processing system it should be possible to apply specific image processing operations to selected regions. Thus one part of an image might be processed to suppress motion blur while another part might be processed to improve color rendition. Sequence of image processing [9]: In modern sciences and technologies, images also gain much broader scopes due to the ever growing importance of scientific visualization. Examples include microarray data in genetic research, or real-time multi-asset portfolio trading in finance [3]. Before going to processing an image, it is converted into a digital form. Digitization includes sampling of image and quantization of sampled values. After converting the image into bit information, processing is performed. This processing technique may be, Image enhancement, Image reconstruction, and Image compression.