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Most image registration methods are based on local feature descriptor matching techniques.
A framework for the unification of statistical and structural information for pattern retrieval based on local feature sets is pre-sented.
This paper proposes using computer vision approaches based on local feature representation, feature learning, and classification for sex prediction from human cranial data obtained from CT scans.
We therefore summarize the contributions of the paper as follows: This paper proposes using computer vision approaches based on local feature representation, feature learning, and classification for sex prediction from human cranial data obtained from CT scans.
In the paper entitled "Face retrieval based on robust local features and statistical-structural learning approach," I. Defee and D. Zhong propose a framework for the unification of statistical and structural information for pattern retrieval based on local feature sets.
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Machine learning methods based on local features have shown promising results in catenary fitting fault detection.
Feature-based tracking algorithms could be break down into the three categories: algorithms based on global features, algorithms based on local features, and algorithms based on dependency graph.
The framework presented in this paper is based on local features and also cares about computational issues while keeping advantages in terms of precision and robustness.
Considering also the suggested improvements, the FFT system shows promise both as a stand-alone system and especially in combination with approaches that are based on local features.
Many CBIR systems are based on local features, such as SIFT [2], RootSIFT [22], and SURF, to simultaneously attain the invariance and distinctiveness [12].
Popular global image representations, such as BoW [3], VLAD [8], and FV [10], are all based on local features (e.g., SIFT [2]).
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based on local farm
based on local eyewitness
based on local distance
based on local practice
based on significant feature
based on local fee-for-service
based on local property
based on local land
based on local history
based on different feature
based on local expert
based on normalized feature
based on local currency
based on supervised feature
based on local well
based on local illumination
based on statistical feature
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