Object Recognition: Performance evaluation using SIFT and SURF
TL;DR: An attempt has been done to compare the results obtained from the implementation of Scale Invariant Feature Transform with another very important technique called Speeded-Up Robust Feature Transform (SURF) to conclude with certain interesting results.
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Abstract: Object Recognition has become one of the most attractive areas of research for most of the scientists over the past few decades. Object recognition has extensive applications in numerous areas of interest. In this paper, the importance of object recognition in different applications has been highlighted. The very famous and impressive technique by David Lowe which is Scale Invariant Feature Transform (SIFT) has been implemented for object recognition and an attempt has been done to compare the results obtained from it with the another very important technique called Speeded-Up Robust Feature Transform (SURF) to conclude with certain interesting results.
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TL;DR: An overview of the contemporary state of art techniques mainly Feature-based approaches along with the most recent and effective techniques been applied in this area are given.
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References
Object recognition from local scale-invariant features
David G. Lowe
- 20 Sep 1999
TL;DR: Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds.
SURF: speeded up robust features
Herbert Bay,Tinne Tuytelaars,Luc Van Gool +2 more
- 07 May 2006
TL;DR: A novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
A Combined Corner and Edge Detector
Chris Harris,Mike Stephens +1 more
- 01 Jan 1988
TL;DR: The problem the authors are addressing in Alvey Project MMI149 is that of using computer vision to understand the unconstrained 3D world, in which the viewed scenes will in general contain too wide a diversity of objects for topdown recognition techniques to work.
Face recognition using sift features
Cong Geng,Xudong Jiang +1 more
- 07 Nov 2009
TL;DR: Two new approaches are proposed: Volume-SIFT (VSIFT) and Partial-Descriptor-Sift (PDSIFT) for face recognition based on the original SIFT algorithm, which can achieve comparable performance as the most successful holistic approach ERE and significantly outperforms FLDA and NLDA.
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