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» Discriminant Embedding for Local Image Descriptors
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ICCV
2007
IEEE
14 years 6 months ago
Discriminant Embedding for Local Image Descriptors
Invariant feature descriptors such as SIFT and GLOH have been demonstrated to be very robust for image matching and visual recognition. However, such descriptors are generally par...
Gang Hua, Matthew Brown, Simon A. J. Winder
ECCV
2008
Springer
14 years 6 months ago
Scale-Dependent/Invariant Local 3D Shape Descriptors for Fully Automatic Registration of Multiple Sets of Range Images
Abstract. Despite the ubiquitous use of range images in various computer vision applications, little has been investigated about the size variation of the local geometric structure...
John Novatnack, Ko Nishino
CVPR
2011
IEEE
13 years 8 days ago
Deformation and Illumination Invariant Feature Point Descriptor
Recent advances in 3D shape recognition have shown that kernels based on diffusion geometry can be effectively used to describe local features of deforming surfaces. In this paper...
Francesc Moreno (Institut de Robotica i Informatic...
PAMI
2011
12 years 11 months ago
Learning Linear Discriminant Projections for Dimensionality Reduction of Image Descriptors
This paper proposes a general method for improving image descriptors using discriminant projections. Two methods based on Linear Discriminant Analysis have been recently introduce...
Hongping Cai, Krystian Mikolajczyk, Jiri Matas
PSIVT
2009
Springer
400views Multimedia» more  PSIVT 2009»
13 years 11 months ago
Local Image Descriptors Using Supervised Kernel ICA
PCA-SIFT is an extension to SIFT which aims to reduce SIFT’s high dimensionality (128 dimensions) by applying PCA to the gradient image patches. However PCA is not a discriminati...
Masaki Yamazaki, Sidney Fels