Sentence examples for features we used from inspiring English sources

Section 3 presents the features we used in this paper.

As global features we used the well-established Gabor features.

The following sections describe the features we used with this tool.

To extract facial features we used GL functions, which provide a self-steering pyramidal analysis structure.

To train the AdaBoost with Haar-like features, we used 15,705 bird images and 18,688 non-bird images similarly collected to train ResNet.

Since the ECBDL14 dataset contains a large number of features, we used the DEFW-BigData algorithm [42] to improve the classification performance by obtaining the most relevant features.

In detail, we will discuss the types of features we used, the use of shape context features matching, PageRank, structure similarity computation, and spectral clustering.

To quantitatively measure landscape features we used several classical geomorphic indices (spacing ratio, hypsometric curves and integral, stream frequency drainage, stream length-gradient).

As features we use both lexical features and Twitter profile features.

In Section 2.5, we describe which features we use in the random forest.

As features we use 11 Mel-Frequency Cepstrum Coefficients (MFCCs) and the energy of the signal plus their first derivatives.