Comparing Proposed Signature with SURF in Object Detection Process

Most object detection and classification algorithms only locate regions in the image, whether they are within a template-sliding mask or in interested region blobs. However, such regions may be ambiguous, especially when the object of interest is very small or unclear. This paper proposes an algorithm for automatic object detection and matching based on its own signature using morphological segmentation tools. Moreover, the algorithm tries to match objects, not in object blobs or regions of interest, but among the constructed proposed object signatures. During the matching process, the SURF method makes a comparison of process between its matching performance and the proposed matching process on all experimental objects. The process has tested a wide variety of 120 dissimilar objects; it has achieved 100% of constructed signatures, and it has achieved 96% of correct object matching; whereas SURF has achieved only 85% for all tested objects.