Towards Automated 3D Landmark Localization in Natural Images


Towards Automated 3D Landmark Localization in Natural Images – The aim of this paper is to demonstrate the importance of feature vectors as a tool for visual inspection of landmark images. The current state-of-the-art approaches tend to generate large number of features, which is a drawback when large areas of view are available. Feature vectors are typically used for the automatic inspection of landmark images when the object is being studied. In this research paper, we propose a simple yet effective image classification algorithm consisting of the feature vectors as input and an off-the-shelf detector as output. Based on our feature vectors, we achieve high classification accuracy of 98.8% on the MNIST RGB images.

We propose a novel algorithm for the simultaneous estimation of Gaussian mixture models with probability functions which is faster than the state-of-the-art and achieves similar or better results than the previous state-of-the-art Bayesian learning. We also show that the proposed method can be applied to a non-Gaussian mixture model, which can represent multiple latent variables with Gaussian models and has advantages over Bayesian optimization, such as (but not limited to) the importance of the Gaussian process model prior.

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Towards Automated 3D Landmark Localization in Natural Images

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    Distributed Stochastic Gradient with Variance Bracket SubsamplingWe propose a novel algorithm for the simultaneous estimation of Gaussian mixture models with probability functions which is faster than the state-of-the-art and achieves similar or better results than the previous state-of-the-art Bayesian learning. We also show that the proposed method can be applied to a non-Gaussian mixture model, which can represent multiple latent variables with Gaussian models and has advantages over Bayesian optimization, such as (but not limited to) the importance of the Gaussian process model prior.


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