euclidean distance

However, every attribute has different function in actual samples; the weighted Euclidean distance is usually chosen.

By comparison with retrieving results of Euclidean distance in the same condition, it proved that this method has obvious advantage.

With an Euclidean distance based classifier, each nonoverlapping window of the texture image is then assigned to its corresponding category.

It has been proved that this distance measure is more efficient than the Euclidean distance.

The traditional method about KNN is generally used Euclidean distance formula to measure the distance between the two samples.

The Euclidean distance and weighted methods were considered as the criteria for the Case-Based Reasoning to get the ways of similarity.

Weighted euclidean distance was used to improve retrieval efficiency.

The distance vectors are available in a distance map made by the Euclidean Distance Transform.

Euclidean distance was used to recognize and analyze unknown samples.

This method established a new kind of relative similarity degree by combining the Euclidean distance with grey correlation degree.

It efficiently preserves the global Euclidean distance, i. e. diversity information between data, but fails to the nonlinear data.

using the Euclidean distance between the feature to detect objects; Filtering the static object through three continuous frame.

Finally, weighted Euclidean distance classifier was utilized in recognition.

In these topological spaces, it usually adopts Euclidean distance to measure similarity between objects.

The global features and local features will be recognized and sorted by weighting Euclidean distance and the minimum distance, respectively.

A new method to deform surfaces using radial basis function is proposed by replacing the Euclidean distance as geodesic distance.

finally, feature points are matched by the Euclidean distance under the continuity constraint.

The iris feature matching was based on the Euclidean distance between the corresponding feature points.

Float vector based image feature has high dimension and usually makes use of Euclidean distance as its similarity definition.

The Euclidean distance of singular values is used as the measurement of similarity between two gray charts, realizing fault diagnosis.

Based on the Euclidean distance, a distributed grid location estimation was proposed as follows to improve the original estimation.

In the process of pattern recognition, we use Euclidean distance formula to classify sample.

Section6 includes iris matching via minimum Euclidean distance.

The result using GTI model is somewhat inferior to Euclidean distance.

However, the conventional RBFs are based on the isotropic Euclidean distance and thus is more applicable to isotropic problems.

Hardware Realization of a Fast Algorithm for Euclidean Distance Transformation

A Parallel Algorithm for Quasi Euclidean Distance Transform

Based-on Euclidean Distance Method for Improving the Classification Errors of Artificial Neural Networks

A New Algorithm for Complete Euclidean Distance Transform

Integrated Dynamic Radio Resource Management Based on Euclidean Distance Metric

A New Method of Three-Dimensional Euclidean Distance Transform

A Fast Algorithm for Complete Euclidean Distance Transformation Based on Contour Scanning

Multi-Modal Data Fusion for Person Authentication based on Weighted Euclidean Distance Method

Automatic image mosaic technology based on least Euclidean distance criteria of net template

artificial immune algorithm based on euclidean distance and king - crossover

A Model Based on Euclidean Distance for Performance Evaluation of Camouflage Equipment

Study on Algorithm for State Prediction of Rotary Kiln Based on Euclidean Distance and Phase Plane

Modified Polygonal Mesh Voxelization Based on Euclidean Distance Measurement

Corner detection algorithm based on euclidean distance

Fast Euclidean Distance Transform Based on Contour Tracking

Calculation of Euclidean Distance between Vertices Based on VCC