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  2. Foreground detection - Wikipedia

    en.wikipedia.org/wiki/Foreground_detection

    The following analyses make use of the function of V(x,y,t) as a video sequence where t is the time dimension, x and y are the pixel location variables. e.g. V(1,2,3) is the pixel intensity at (1,2) pixel location of the image at t = 3 in the video sequence.

  3. Scale-invariant feature transform - Wikipedia

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

    The way Lowe [2] determined whether a given candidate should be kept or 'thrown out' is by checking the ratio between the distance from this given candidate and the distance from the closest keypoint which is not of the same object class as the candidate at hand (candidate feature vector / closest different class feature vector), the idea is ...

  4. Level-set method - Wikipedia

    en.wikipedia.org/wiki/Level-set_method

    Video of spiral being propagated by level sets (curvature flow) in 2D.Left image shows zero-level solution. Right image shows the level-set scalar field. The Level-set method (LSM) is a conceptual framework for using level sets as a tool for numerical analysis of surfaces and shapes.

  5. Euclidean distance - Wikipedia

    en.wikipedia.org/wiki/Euclidean_distance

    The distance from a point to a plane in three-dimensional Euclidean space [7] The distance between two lines in three-dimensional Euclidean space [8] The distance from a point to a curve can be used to define its parallel curve, another curve all of whose points have the same distance to the given curve. [9]

  6. 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 ...

  7. 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 ...

  8. Similarity measure - Wikipedia

    en.wikipedia.org/wiki/Similarity_measure

    The Euclidean distance formula is used to find the distance between two points on a plane, which is visualized in the image below. Manhattan distance is commonly used in GPS applications, as it can be used to find the shortest route between two addresses. [citation needed] When you generalize the Euclidean distance formula and Manhattan ...

  9. Structural similarity index measure - Wikipedia

    en.wikipedia.org/wiki/Structural_similarity...

    The structural similarity index measure (SSIM) is a method for predicting the perceived quality of digital television and cinematic pictures, as well as other kinds of digital images and videos. It is also used for measuring the similarity between two images.