Distinctive Image Features from Scale-Invariant Keypoints
Citations
340 citations
Cites background or methods or result from "Distinctive Image Features from Sca..."
...In our experiments (Intel Core2 Quad CPU 2.83GHz), the average time for constructing a feature descriptor is: 2.1ms for SIFT, 3.8ms for DAISY, 5.3ms for HRI-CSLTP and 5.5ms for LIOP....
[...]
...It is worth noting that different from other methods such as [13, 11, 22, 9], we do not rotate the local patch according to the local consistent orientation(e....
[...]
...For example, Harris corner [10] and DoG(Difference of Gaussian) [13] for interest point detection, and Harris-affine [15], Hessian-affine [17], MSER(Maximally Stable Extremal Region) [14], IBR(Intensity-Based Region) and EBR(EdgeBased Region) [24] for affine covariant region detection....
[...]
...More recently, the local binary pattern [19] based methods are proposed and achieve high performance comparable to SIFT....
[...]
...Local image features have been widely used in many computer vision applications such as object recognition [13] , texture recognition [12], wide baseline matching [24], image retrieval [18] and panoramic image stitching [3]....
[...]
340 citations
339 citations
Cites methods from "Distinctive Image Features from Sca..."
...Lowe for matching SIFT features in [13] and by K....
[...]
...In this article, SIFT (blobs) [13], SURF (blobs) [14], KAZE (blobs) [15], AKAZE (blobs) [16], ORB (corners) [17], and BRISK (corners) [18] algorithms are compared for image matching and registration....
[...]
...Lowe introduced Scale Invariant Feature Transform (SIFT) in 2004 [13], which is the most renowned featuredetection-description algorithm....
[...]
337 citations
333 citations
References
46,906 citations
16,989 citations
13,993 citations
7,057 citations
3,422 citations