Segmentation of Database Images Using Gradient Based Multiscale Morphological Reconstruction

The main purpose of this research is to improve the accuracy of object segmentation in database images by constructing an object segmentation algorithm. Image segmentation is a crucial step in the field of image processing and pattern recognition. Segmentation allows the identification of structures in an image which can be utilized for further processing. Both region-based and object-based segmentation are utilized for large-scale database images in a robust and principled manner. Gradient based MultiScalE Graylevel mOrphological recoNstructions (G-SEGON) is used for segmenting an image. SEGON roughly identifies the background and object regions in the image. This proposed method comprises of four phases namely pre-processing phase, object identification phase, object region segmentation phase, majority selection and refinement phase. After developing the grey level mesh the resultant image is converted into gradient and K-means clustering segmentation algorithm is used to segment the object from the gradient image. After implementation the accuracy of the proposed G-SEGON technique is compared with the existing method to prove its efficiency.