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In this fusion method we incorporate the merits of interscale dependencies, which can amplify the significant features, dilute noise and distinguish edge structures from noise more effectively, into the multifocus image fusion technique.
In the proposed C-MI-FFD method, we incorporate several constraints, which are derived from structure information of the images, into the MI objective function so as to improve the tracking performance.
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Finally, to organize neighborhood moves and local search methods efficiently, we incorporate them into the framework of variable neighborhood search (VNS), variable neighborhood descent (VND) and random neighborhood structure (RNS).
By utilizing the finite element method, we can incorporate as much prior information as possible into the solution of the inverse problem, thereby improving the quality of a current image.
In order to study the effect of different delivery methods on the cells, we employed another method wherein we incorporated the α-KG containing microspheres inside the three-dimensional (3-D) cryogel scaffolds, synthesized via cryogelation technology.
In addition to this base non-rigid registration method, we have incorporated several other features to increase the accuracy of the result whilst minimising the computational time needed.
The method we used incorporates machine learning techniques to generate optimal designs with orders of magnitude better than the mean properties of the input training data, and at a much lower computational cost compared to exhaustive methods.
We incorporate this method into a one-dimensional impurity transport model.
In the end, we incorporate our method into a hidden Markov model (HMM) framework to automatically segment a genome into constrained and non-constrained regions.
In our method, which we name Telescoper, we incorporate the following three algorithmic ideas, the latter two of which make novel use of read-pairs: Iterative extensions: a seed sequence is extended iteratively using reads localized to a particular region by their mates, thus allowing for gradual extension into difficult regions.
We incorporate cryptographic methods in our protocol to protect the original dataset and all other intermediate results.
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