Exploratory learning structures in artificial cognitive systems
Image and Vision Computing
Feature map hashing: sub-linear indexing of appearance and global geometry
Proceedings of the international conference on Multimedia
Spatially-sensitive affine-invariant image descriptors
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Learning Real-Time Perspective Patch Rectification
International Journal of Computer Vision
Detection and matching of curvilinear structures
Pattern Recognition
Mutual information refinement for flash-no-flash image alignment
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
Detection of near-duplicate patches in random images using keypoint-based features
ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
Towards large-scale geometry indexing by feature selection
Computer Vision and Image Understanding
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We propose a novel representation of local image structure and a matching scheme that are insensitive to a wide range of appearance changes. The representation is a collection of local affine frames that are constructed on outer boundaries of maximally stable extremal regions (MSERS) in an affine-covariant way. Each local affine frame is described by a relative location of other local affine frames in its neighborhood. The image is thus represented by quantities that depend only on the location of the boundaries of MSERs. Inter-image correspondences between local affine frames are formed in constant time by geometric hashing. Direct detection of local afine frames removes the requirement of a point-based hashing to establish reference frames in a combinatorial way, which has in the case of affine transform complexily that is cubic in the number of points. Local affine frames, which are also the quantities represented in the hash table, occupy a 6 0 space and hence data collisions are less likely compared with 2 0 point hashing. Experimentally, the robustness of the method and its insensitiviq to photometric changes is demonstrated on images from different spectral bands of satellite sensor; on images of a transparent object and on images of an object taken during day and night.