They essentially capture the feature distribution among neighbouring regions.
Previous works have shown that the joint feature distribution of two properties can improve the performance.
Statistical analysis of cost distance rasters enables quantitative spatial analysis of feature distribution in the landscape.
Texture analysis on the carbon surface showed a mesoporous feature distribution.
In particular, we iteratively adapt the feature distribution in photographs to fit paintings and maximize the joint likelihood of labeled and unlabeled data.
For unknown feature distribution cases, the post-damage distribution was consistently estimated and the detection delays were only a few time steps longer than the delays from the general method that assumes we know the post-damage feature distribution.
To combine the feature distribution information in regions with varying size and shape, we fuse the results based on the two models at the score level.
The joint feature distribution makes feature relationships explicit instead of roping that a trained classifier picks up a non-linear relation present in the data.
The spatial pyramid model extracts features from coarse to fine grid regions, and, it models a local to global feature distribution.
By varying the shape of the convolution kernel, we are able to obtain the feature distribution in regions with different shapes.
We show that the three past disambiguation algorithms we evaluate demonstrate biases depending on the feature distribution of the target disambiguation population.
