شماره ركورد كنفرانس :
3297
عنوان مقاله :
Quick SIFT(QSIFT), An Approach to Reduce SIFT Computational Cost
عنوان به زبان ديگر :
Quick SIFT(QSIFT), An Approach to Reduce SIFT Computational Cost
پديدآورندگان :
Fazel Zahra Shiraz University Iran - shiraz , Famouri Mahmoud Shiraz University Iran - shiraz , Nazemi Azadeh Shiraz University Iran - shiraz , Azimifar Zohreh Shiraz University Iran - shiraz
كليدواژه :
Real-time application , Natural image statistics , Feature matching , Feature extraction , Feature descriptor , Feature detector , Interest points
عنوان كنفرانس :
نوزدهمين سمپوزيوم بين المللي هوش مصنوعي و پردازش سيگنال
چكيده لاتين :
SIFT has been proven to be the most robust local
rotation and illumination invariant feature descriptor. Being fully
scale invariant is the most important advantage of this descriptor.
The major drawback of SIFT is time complexity which prevents
utilizing SIFT in real-time applications. This paper describes
a method to increase the speed of SIFT feature extraction
using keypoint estimation and approximation instead of keypoint
calculation in various scales. This research attempts to decrease
SIFT computational cost without sacrificing performance and
propose quick SIFT method (QSIFT). The recent researches in
this area have approved that direct feature value computation
is more expensive than the value extrapolation. Consequently,
the contribution of this research is to reduces the time execution
without losing accuracy.
