Sentence examples for every descriptor from inspiring English sources

In Shen et al.'s algorithm, for every descriptor there are both positive and negative weights.

Every descriptor is then represented as the most appropriate visual word by the nearest neighbor clustering.

So far, every descriptor has found its place in the descriptive scheme and has been assigned an initial level.

The majority of descriptor classes return array-valued results, which raises the total number of descriptor values from 43 to 274 (i.e. on average, every descriptor class calculates more than 6 descriptor values).

For cognitive abilities, five parameters were selected to describe reading verb, goal, text type, topic, and language, among which the first three are essential to every descriptor while the last two are added if the compulsory elements fail to distinguish reading abilities between levels.

For every descriptor, the increasing percentiles from the 10th to 90th were calculated (one value per simulated study).

In the first level, a separate ELM-E classifier is trained for every image descriptor.

For every unscaled descriptor, x i, we define the partial activity as β i x i, using the unscaled regression coefficient, β i.

Every local descriptor has N vectors pointing to the N landmark points and the associated local likelihood patterns, where local likelihoods are obtained from the HOG vectors.

Utilizing every available descriptor that may produce a predictive model with a good correlation coefficient, makes the models difficult to interpret and do not stand up to external validation.

After the quantization stage in the original VLAD algorithm, every SIFT descriptor is assigned to a cluster center (visual word) μ i. Herein, we introduce the SDE in each cluster as follows: considering an input image of size H × W pixels, the biggest scale of all local features is σ∗, and the biggest orientation of all local features is θ∗.