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Machine learning methods based on local features have shown promising results in catenary fitting fault detection.
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].
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.
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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Most image registration methods are based on local feature descriptor matching techniques.
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.
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.
A framework for the unification of statistical and structural information for pattern retrieval based on local feature sets is pre-sented.
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.
More suggestions(17)
based on numerous features
based on local dialects
based on local minerals
based on local transformations
based on local resources
based on local traditions
based on local data
based on local capabilities
based on local conditions
based on reproductive features
based on local measurements
based on local landmarks
based on local needs
based on behavioral features
based on local micro-organisms
based on biometric features
based on local concerns
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