Adaptive Grass-Fire Blob Detection Algorithm (AGFBDA) for the Image Matrices

The blob detection methods plays the vital role in the various image processing models. The blob detection models are utilized for the localization of the visible objects in the given images in order to understand the type and size of the objects. The blob detection can be utilized for the classification, categorization and other purposes in the image processing models. In the proposed model, the robust blob detection model based upon the recursive grass-fire method has been implemented to localize the objects in the given image matrix. The proposed model is intended to handle the moving or still objects in the given image, where the proposed model has been performed adequately well. The proposed model has been tested over the standard image set in order to understand the overall performance under the various perspectives, where it has detection all of the objects very clearly and described the boundary (known as bounding box) around the detected objects. The proposed model has efficiently detected and localized all of the objects in the given image set.