Comparing Proposed Signature with SURF in Object Detection Process

ABSTRACT

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.

Keywords:

• Object detection and matching
• Segmentation
• Signature
• SURF

Additional information

Notes on contributors

Hany Abdelrahman Mohammed Elsalamony

Hany Abdelrahman Mohammed Elsalamony received his PhD degree in mathematics from Helwan University in 2006. Currently, he works as a lecturer of computer science and mathematics at Helwan University, Cairo, Egypt. Before working in different universities and institutes in the Arab region, he has collected different experiences in information technology by working in many concerned projects. Dr. Hany published and reviewed several papers; all are focused on image processing and data mining. His current research interests cover medical image processing, the theory of data mining and analysis and their applications on image understanding and signal processing.

E-mail: [email protected]