Blurred And Compressed Trademark Image Retreival Under Noise And Orientation Based On Curvature Shape Descriptor

摘要

Digital images are a convenient media for describing and storing spatial, temporal, spectral, and physical components of information contained in a variety of domains (e.g.. aerial/satellite images in remote sensing, medical images in telemedicine, fingerprints in forensics, museum collections in art history, and registration of trademarks and logos). Retrieval of digital images is one of the challenging issue in any Digital Image Processing system. Although advances in image compression algorithms have alleviated the storage requirement to some extent, the large volume of these images makes it difficult for a user to browse through the entire database. Therefore, an efficient and automatic procedure based on curvature shape descriptors is proposed, which make use shape descriptor along with compression techniques to make database feasible to store large number of images and retrieve image under noise, blurrring and orientation changes. In CSD approach, the image is subjected to compression and then later it is represented in its contour format by its coordinates and are mathematically processed for curvature evolution over various sigma levels so as to remove the unevenness caused by some external disturbances. For each sigma level, zero crossing points are evaluated which are used as the features for image retrieval along with arc length