Object recognition: differenze tra le versioni

Versione delle 11:16, 3 feb 2010

Versione delle 23:39, 8 apr 2010

Riga 105:

PCA-SIFT <ref>Ke, Y., and Sukthankar, R., PCA-SIFT: A More Distinctive Representation for Local Image DescriptorsComputer Vision and Pattern Recognition, 2004.</ref>and [[GLOH]] <ref>Mikolajczyk, K., and Schmid, C., "A performance evaluation of local descriptors", IEEE Transactions on Pattern Analysis and Machine Intelligence, 10, 27, pp 1615--1630, 2005.</ref> are variants of [[Scale-invariant feature transform|SIFT]]. PCA-SIFT descriptor is a vector of image gradients in x and y direction computed within the support region. The gradient region is sampled at 39x39 locations, therefore the vector is of dimension 3042. The dimension is reduced

to 36 with [[Analisi delle componenti principali|PCA]]. Gradient ___location-orientation histogram ([[GLOH]]) is an extension of the [[Scale-invariant feature transform|SIFT]] descriptor designed to increase its robustness and distinctiveness. The [[Scale-invariant feature transform|SIFT]] descriptor is computed for a log-polar ___location grid with three bins in radial direction (the radius set to 6, 11, and 15) and 8 in angular direction, which results in 17 ___location bins. The central bin is not divided in angular directions. The gradient orientations are quantized in 16 bins resulting in 272 bin histogram. The size of this descriptor is reduced with [[PCA]]. The [[covariance matrix]] for [[PCA]] is estimated on image patches collected from various images. The 128 largest [[eigenvector]]s are used for description.

== Applications ==