Detection and matching of object using proposed signature - Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition - page 500

ing object signatures, also it has achieved 96% of exact matching whereas SURF has achieved
85% for all experimental objects.
References
[1] Elsalamony HA. Automatic video stream indexing and retrieving based on face detection
using wavelet transformation. In: Second international conference on signal processing
systems (ICSPS) 5-7 July, 2010; 2010:153-157.
[2] Fischler M, Elschlager R. The representation and matching of pictorial structures.
IEEE Trans Comput. 1973;C-22(1):67-92.
[3] Bouchard G, Triggs B. A hierarchical part-based model for visual object categorization. In:
IEEE computer society conference on computer vision and patern recognition; 2005.
[4] He L, Wang H, Zhang H. Object detection by parts using appearance, structural and
shape features. In: International conference on mechatronics and automation (ICMA);
2011:489-494.
[5] Hutenlocher D, Lilien R, Olson C. View-based recognition using an eigenspace
approximation to the Hausdorff measure. IEEE Trans Patern Anal Mach Intell.
1999;21(9):951-955.
[6] Lai K, Bo L, Ren X, Fox D. Detection-based object labeling in 3D scenes. In: International
conference on robotics and automation, 2012; 2012.
[7] Gavrila D, Philomin V. Real-time object detection for smart vehicles. In: Proceedings. 7th
international conference on computer vision; 1999:87-93.
[8] Rowley HA, Baluja S, Kanade T. Human face detection in visual scenes. Adv Neur Inf.
1995;8.
[9] Viola P, Jones M, Snow D. Detecting pedestrians using paterns of motion and appearance.
In: IEEE conference on computer vision and patern recognition; 2003.
[10] Zahn C, Roskies R. Fourier descriptors for plane closed curves. IEEE Trans Comput.
1972;21(3):269-281.
[11] Ali I, Mille J, Tougne L. Space-time spectral model for object detection in dynamic
textured background. Patern Recogn Let. 2012;33(13):1710-1716 ISSN 0167-8655.
[12] Veltkamp RC, Hagedoorn M. State of the art in shape matching. Technical Report UU-
CS-1999-27, Utrecht; 1999.
[13] Elsalamony HA. Automatic object detection and matching based on proposed sig-
nature. 68-73. International conference on audio, language and image processing (ICALIP),
16-18 July, 2012. 2012;vol. 1.
[14] Lowe DG. Distinctive image features from scale-invariant key points. Int J Comput Vis.
2004;60:91-110.
[15] Lowe DG. Object recognition from local scale-invariant features. In: Proceedings of the in-
ternational conference on computer vision, Greece, 1999; 1999.
[16] Ke Y, Sukthankar R. PCA-SIFT: a more distinctive representation for local image descriptors.
In: Proceedings of the IEEE conference on computer vision and patern recognition,
USA, 2004; 2004.
[17] Bay H, Ess A, Tuytelaars T, VanGool L. Surf: speeded up robust features. Int J Comput
Vis Image Und. 2008;110:404-417.
[18] Bay H, Tuytelaars T, VanGool L. Surf: speeded up robust features. In: Proceedings of the
European conference on computer vision, Austria, 2006; 2006.
[19] Juan L, Gwun O. A comparison of SIFT, PCA-SIFT and SURF. Int J Image Process.
2009;3:143-152.