GLCM ARCHITECTURE FOR IMAGE EXTRACTION
M. Tech, B. Sashi Kanth, Mr. Harsha, S. Visweswara Rao · 2014
In this paper we are extracting the image features using different algorithms that are specified with architectural models with internal modules represented. The main objective involves calculating the different features for a given image. Digital image has several features where a feature is a characteristic that can capture a certain visual property of an image either globally for the whole image, or locally for objects or regions. A key function in different image applications is Feature extraction. There are different algorithms to extract texture features such as Structural, Statistical methods [3]. Feature extraction is a key function in various image processing applications. A feature is an image characteristic that can capture certain visual property of the image. Texture is an important feature of many image types, which is the pattern of information or arrangement of the structure found in a picture. Texture features are used in different applications such as image processing, remote sensing and contentbased image retrieval. These features can be extracted in several ways. The most common way is using a Gray Level Co-occurrence Matrix (GLCM). GLCM contains the second-order statistical information of neighboring pixels of an image. Textural properties can be calculated from GLCM to understand the details about the image content. [6]. The Gray Level Co occurrence Matrix is a second order Statistical method. These features are implemented in VERILOG language. The tools used for implementation of the paper are XILINX ISE.