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The abovementioned process considers landmark quality using the local feature region.
Using the convolution point by point in the small sample target object images by using the local feature learnt in the CNN model, the global features of the small sample target object images can be obtained.
In contrast to the conventional boosting cascade models (e.g., BinBoost [2], joint cascade [3], and LUT-AdaBoost [4 6]), we propose a novel and robust cascade algorithm (McSURF) that can simultaneously learn multi-task cascades using the local feature detector and descriptor SURF [7].
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Specifically, to predict the feature at frame t and dimension d, (y_{t}^{(d)}), we only use the local feature trajectory and feature vector that contains (x_{t}^{(d)}) as shown in Fig. 5.
The ligand poses were minimized using the local optimization feature in Prime, the flexible residue distance was set to 4.0 Å from a ligand pose, and the ligand charges obtained in the QPLD stage were used.
Specifically, 3.3% of the total reads could be mapped by allowing indels (the gapped alignment) and 9.4% could be salvaged using the local alignment feature.
Ten motifs per bird were analyzed for the within-syllable variability via 45 pair-wise comparisons of the acoustic features using the local similarity measure [37].
The global feature is classified using SRC while the local feature vector is classified by SVM with RBF kernel.
Notice that, to build S f, we only make use of the local feature descriptors.
Interest points [8, 9], edges [10], etc. are often used as the local features because of their robustness in extraction and matching.
The parameters used to select the local feature region were experimentally defined as follows: Euclidean distance D to group two feature points ("Distinctive feature region extraction based on feature point grouping") was set to (sqrt{10}).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com