In this section, we perform thorough comparative study on feature-based image comparison with various combinations of the four feature detectors (i.e., SIFT detector, SURF detector, Harris detector, and the improved DWT-based detector) and the three feature descriptors (i.e., SIFT descriptor, SURF descriptor, and the moment invariants).
Four image feature detectors, Harris detector, the improved DWT-based corner detector, SIFT detector, and SURF detector, as well as three image feature descriptors, SIFT descriptor, SURF feature descriptor, and moment invariants-based feature descriptor were examined, and different detector/descriptor combinations were tested on two image datasets.
Given two sets of feature descriptors generated by analyzing two images, for each feature descriptor in the first image, the distances between this descriptor and all feature descriptors in the second image are calculated.
Finally, random forests are used to learn a stable relationship between slice feature descriptors and head pose parameters.
In order to seamlessly capture the similarity between feature descriptors, we perform shape clustering on mid-level features that are generated via graph regularized sparse coding.
Generally, the information fusion in the CSI field can be performed in four levels: feature, descriptor, similarity, and decision.
Two different layers of feature descriptors, dense and condensed, are designed to sample the manifold for representing object movies.
There forms a mesh topology due to the fact that a line descriptor links two feature points and meanwhile a feature point links multiple line descriptors.
If the ratio between the smallest distance and the second smallest distance is below a threshold, the feature pair with the smallest distance is claimed to be a matching feature pair or that the two feature points that these two descriptors are describing correspond to each other.
Liminal (Latin for "threshold") describes the transition when something or someone is poised between two contradictory descriptors, neither fully one nor fully the other.
