Sentence examples for dense features from inspiring English sources

Optical devices often require dense features over large areas, making direct write processes such as e-beam lithography less favorable.

Proximity effect correction is needed in order to properly delineate dense features as well as meet the required CD uniformity.

On the other hand, according to [1], dense features, such as spin images, perform better than sparse features, such as SIFT.

It was found that, as a self-developing resist, nitrocellulose showed low sensitivity and low contrast, making it unsuitable for patterning high-resolution dense features.

Using the same feature learning framework, Tang and Liu [15] also propose an algorithm to learn dense features from the d-smallest eigenvectors of the normalized graph Laplacian.

Again, two strategies were used: (i) SURF extracting the 128 dimensional extended feature vector and (ii) CNN used only with dense features.

A local feature region is not necessarily required to have extremely dense feature points.

b A landmark with dense feature points is selected if several landmark candidates have the same number of arcs.

The authors selected passive-aggressive (for dense feature extraction) and confidence weighted algorithms (for sparse feature extraction) as the online learning algorithms and trained their models with extracted features [61, 62].

As the number of neighboring feature space elements is retrieved, the enhanced algorithm can adaptively select the kernel bandwidth, which enables the system to provide a detailed result in dense feature space regions.

In consideration of the shortcoming, Wang et al. [23, 24] used dense optical flow to sample dense feature points on a regular grid, and the points located at spatially homogeneous regions were removed to generate a spatio-temporal dense trajectory (DT) and improved dense trajectory (iDT).