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A distance transform, also known as distance map or distance field, is a derived representation of a digital image. The choice of the term depends on the point of view on the object in question: whether the initial image is transformed into another representation, or it is simply endowed with an additional map or field.
Image registration is the process of transforming different sets of data into one coordinate system. Data may be multiple photographs, data from different sensors, times, depths, or viewpoints. [ 1 ] It is used in computer vision , medical imaging , [ 2 ] military automatic target recognition , and compiling and analyzing images and data from ...
A city block, residential block, urban block, or simply block is a central element of urban planning and urban design. In a city with a grid system, the block is the smallest group of buildings that is surrounded by streets. City blocks are the space for buildings within the street pattern of a city, and form the basic unit of a city's urban ...
These differences are summed to create a simple metric of block similarity, the L 1 norm of the difference image or Manhattan distance between two image blocks. The sum of absolute differences may be used for a variety of purposes, such as object recognition , the generation of disparity maps for stereo images, and motion estimation for video ...
There are many variants of photogrammetry. One example is the extraction of three-dimensional measurements from two-dimensional data (i.e. images); for example, the distance between two points that lie on a plane parallel to the photographic image plane can be determined by measuring their distance on the image, if the scale of
z = AC is the distance from the camera plane to the object. So assuming the cameras are level, and image planes are flat on the same plane, the displacement in the y axis between the same pixel in the two images is, = Where k is the distance between the two cameras times the distance from the lens to the image.
Taxicab geometry or Manhattan geometry is geometry where the familiar Euclidean distance is ignored, and the distance between two points is instead defined to be the sum of the absolute differences of their respective Cartesian coordinates, a distance function (or metric) called the taxicab distance, Manhattan distance, or city block distance.
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, subsets of keypoints that agree on the object and its location, scale, and orientation in the new image are ...