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  2. Scale-invariant feature transform - Wikipedia

    en.wikipedia.org/wiki/Scale-invariant_feature...

    SIFT keypoints of objects are first extracted from a set of reference images [1] and stored in a database. An object is recognized in a new image by individually comparing each feature from the new image to this database and finding candidate matching features based on Euclidean distance of their feature vectors. From the full set of matches ...

  3. Distance matrix - Wikipedia

    en.wikipedia.org/wiki/Distance_matrix

    In general, a distance matrix is a weighted adjacency matrix of some graph. In a network, a directed graph with weights assigned to the arcs, the distance between two nodes of the network can be defined as the minimum of the sums of the weights on the shortest paths joining the two nodes (where the number of steps in the path is bounded). [2]

  4. Euclidean distance matrix - Wikipedia

    en.wikipedia.org/wiki/Euclidean_distance_matrix

    In mathematics, a Euclidean distance matrix is an n×n matrix representing the spacing of a set of n points in Euclidean space. For points x 1 , x 2 , … , x n {\displaystyle x_{1},x_{2},\ldots ,x_{n}} in k -dimensional space ℝ k , the elements of their Euclidean distance matrix A are given by squares of distances between them.

  5. Euclidean distance - Wikipedia

    en.wikipedia.org/wiki/Euclidean_distance

    In mathematics, the Euclidean distance between two points in Euclidean space is the length of the line segment between them. It can be calculated from the Cartesian coordinates of the points using the Pythagorean theorem , and therefore is occasionally called the Pythagorean distance .

  6. Lloyd's algorithm - Wikipedia

    en.wikipedia.org/wiki/Lloyd's_algorithm

    Lloyd's algorithm is usually used in a Euclidean space. The Euclidean distance plays two roles in the algorithm: it is used to define the Voronoi cells, but it also corresponds to the choice of the centroid as the representative point of each cell, since the centroid is the point that minimizes the average squared Euclidean distance to the ...

  7. Eigenface - Wikipedia

    en.wikipedia.org/wiki/Eigenface

    A nearest-neighbour method is a simple approach for finding the Euclidean distance between two vectors, where the minimum can be classified as the closest subject. [ 3 ] : 590 Intuitively, the recognition process with the eigenface method is to project query images into the face-space spanned by eigenfaces calculated, and to find the closest ...

  8. Closest pair of points problem - Wikipedia

    en.wikipedia.org/wiki/Closest_pair_of_points_problem

    The closest pair of points problem or closest pair problem is a problem of computational geometry: given points in metric space, find a pair of points with the smallest distance between them. The closest pair problem for points in the Euclidean plane [ 1 ] was among the first geometric problems that were treated at the origins of the systematic ...

  9. Diffusion map - Wikipedia

    en.wikipedia.org/wiki/Diffusion_map

    This distance is robust to noise, since the distance between two points depends on all possible paths of length between the points. From a machine learning point of view, the distance takes into account all evidences linking x i {\displaystyle x_{i}} to x j {\displaystyle x_{j}} , allowing us to conclude that this distance is appropriate for ...