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Proposed Method for Detecting Objects

by Hany A. Elsalamony
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 107 - Number 2
Year of Publication: 2014
Authors: Hany A. Elsalamony

Hany A. Elsalamony . Proposed Method for Detecting Objects. International Journal of Computer Applications. 107, 2 ( December 2014), 11-18. DOI=10.5120/18722-9946

@article{ 10.5120/18722-9946,
author = { Hany A. Elsalamony },
title = { Proposed Method for Detecting Objects },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 107 },
number = { 2 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 11-18 },
numpages = {9},
url = { },
doi = { 10.5120/18722-9946 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:39:59.162848+05:30
%A Hany A. Elsalamony
%T Proposed Method for Detecting Objects
%J International Journal of Computer Applications
%@ 0975-8887
%V 107
%N 2
%P 11-18
%D 2014
%I Foundation of Computer Science (FCS), NY, USA

Most of object detection and classification algorithms are only locating regions in the image, whether it is within a template-sliding mask or interested region blobs. However, such regions may be ambiguous, especially when the object of interest is very small, unclear, or anything else. This paper presents proposed algorithm for automatic object detection and matching based on its own proposed signature using morphological segmentation tools. Moreover, the algorithm tries to match the objects; neither among object's blobs nor among regions of interest; but among the constructed proposed objects' signatures. During the matching process, SURF method has presented to make a comparison of the experimental results. The performance has been tested 120 from a wide variety of unlike objects; it has been achieved 100% in the case of constructing object signatures, also it has been achieved 96% of right matching whereas SURF has achieved 85% for all test objects.

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Index Terms

Computer Science
Information Sciences


Object Detection and Matching Signature SURF Segmentation.