Upright FAST-Harris Filter

Abdulmalik Danlami Mohammed*, Saliu Adam Muhammed**, Idris Mohammed Kolo***, Adama Victor Ndako****, Shafi’i Muhammed Abdulhamid*****, Abdulkadir Baba Hassan******, Abubakar Saddiq Mohammed*******
*_****Lecturer, Department of Computer Science, Federal University of Technology, Minna, Nigeria.
*****Senior Lecturer and Head, Department of Cyber Security Science, Federal University of Technology, Minna, Nigeria
*** ***Associate Professor, Department of Mechanical Engineering, Federal University of Technology, Minna, Nigeria
*******Department of Electrical/Electronic Engineering, Federal University of Technology, Minna, Nigeria
Periodicity:July - September'2018
DOI : https://doi.org/10.26634/jip.5.3.15689

Abstract

The traditional approaches to the classification of image regions suffer drawbacks in the face of imaging conditions (occlusion, illumination changes, rotation, viewpoint changes, and image blurring) and thus contribute to the poor performance of several vision based applications, such as object recognition, object tracking, image retrieval, pose estimation, camera calibration, 3D reconstruction, Structure from motion, stereo images, and image stitching. However, in this work, feature points extraction method by decomposition of image structure is employed in order to overcome these challenges. The decomposition of an image structure into feature set enhances the performance of many vision-based applications and system. The feature point extraction method which we refer to as Upright Feature from Accelerated Segment Test with Harris filter (UFAH) in this text, works by combining Feature from Accelerated Segment Test detector with Harris filter. The result obtained in the evaluation process shows that UFAH is robust and also invariant to imaging conditions (i.e rotation, illumination changes, and image blurring).

Keywords

Image Analysis, Feature Points, Repeatability, Harris Filter, FAST, Upright FAST.

How to Cite this Article?

Mohammed, A.D., Saliu, A.M., Kolo, I.M., Ndako, A.V., Abdulhamid, S.M., Hassan, A.B., and Mohammed, A.S (2018). Color and Shape Based Automatic Detection of Pedestrians in Surveillance Videos. i-manager’s Journal on Image Processing, 5(3),14-20. https://doi.org/10.26634/jip.5.3.15689

References

[1]. Agrawal, M., Konolige, K., & Blas, M. R. (2008, October). Censure: Center surround extremas for realtime feature detection and matching. In European Conference on Computer Vision (pp. 102-115). Springer, Berlin, Heidelberg.
[2]. Alahi, A., Ortiz, R., & Vandergheynst, P. (2012, June). Freak: Fast retina keypoint. In 2012 IEEE Conference on Computer Vision and Pattern Recognition (pp. 510-517). IEEE.
[3]. Bay, H., Tuytelaars, T., & Van Gool, L. (2006, May). Surf: Speeded up robust features. In European Conference on Computer Vision (pp. 404-417). Springer, Berlin, Heidelberg.
[4]. Donoser, M., & Bischof, H. (2006, June). Efficient Maximally Stable External Region (MSER) tracking. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) (Vol. 1, pp. 553-560). IEEE.
[5]. Leutenegger, S., Chli, M., & Siegwart, R. Y. (2011). BRISK: Binary robust invariant scalable keypoints. In 2011 IEEE International Conference on Computer Vision (ICCV) (pp. 2548-2555). IEEE.
[6]. Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91-110.
[7]. Mair, E., Hager, G. D., Burschka, D., Suppa, M., & Hirzinger, G. (2010, September). Adaptive and generic corner detection based on the accelerated segment test. In European Conference on Computer Vision (pp. 183-196). Springer, Berlin, Heidelberg.
[8]. Mikolajczyk, K., & Schmid, C. (2005). A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(10), 1615- 1630.
[9]. Rosten, E., Porter, R., & Drummond, T. (2010). Faster and better: A machine learning approach to corner detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(1), 105-119.
[10]. Rublee, E., Rabaud, V., Konolige, K., & Bradski, G. (2011). ORB: An efficient alternative to SIFT or SURF. 2011 International Conference on Computer Vision (pp. 2564- 2571). doi: 10.1109/ICCV.2011.6126544
[11]. Tuytelaars, T., & Mikolajczyk, K. (2007). Local invariant feature detectors: A survey. Foundations and Trends® in Computer Graphics and Vision, 3(3), 177-280.

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