Sentence examples for based on feature extraction from inspiring English sources

A series of 21 multi-temporal satellite images (18 HJ-1A/B CCD and three Landsat TM/OLI) of the RSR area collected at different water levels within a five month period (31 December 2013 28 May 2014) was used to extract waterlines based on feature extraction techniques and artificial further modification.

This is exactly how a radiologist relates a case to those he saw in the past.Making a clean breastDr Tourassi has found that her system can reliably distinguish tumour masses from normal tissues, and has a lower rate of false positives than systems based on feature extraction.

Using support vector machine (SVM), [33] proposed a method that can classify two different types of oscillations based on feature extraction.

Moreover, it performs better than other state-of-the-art methods based on feature extraction, and it can help improve the CAD of breast cancer.

We analyze the effect of the computer-aided system based on feature extraction and classification with and without motion compensation with different parameter settings and for several extracted features describing kinetics, morphology, or both (Tables1 and2).

Background: Our methodology describes a human activity recognition framework based on feature extraction and feature selection techniques where a set of time, statistical and frequency domain features taken from 3-dimensional accelerometer sensors are extracted.

SIFT features are extracted from an image, and rectangular regions that contain a feature cluster are selected ("Distinctive feature region extraction based on feature point grouping").

Euclidean distance D to group two feature points ("Distinctive feature region extraction based on feature point grouping") was set to (sqrt{10}).

It consists of three phases: Feature region detection: SIFT features are extracted from an image, and rectangular regions that contain a feature cluster are selected ("Distinctive feature region extraction based on feature point grouping").

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}).

These methods are based on common feature extraction with a genetic algorithm (GA) for a learning model, GA-scaled regression clustering, and GA-based feature selection from the correlation matrix.