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The random walker algorithm is a segmentation algorithm solving the combinatorial Dirichlet problem, adapted to image segmentation by L. Grady in 2006. [16] In 2011, C. Couprie et al. proved that when the power of the weights of the graph converge toward infinity, the cut minimizing the random walker energy is a cut by maximum spanning forest. [17]
This algorithm is part of Vincent and Soille's watershed segmentation algorithm, [11] other implementations also exist. [ 12 ] In order to do that a linked list is formed that will keep the indexes of the pixels that are connected to each other, steps (2) and (3) below.
Instance segmentation is an approach that identifies, for every pixel, the specific belonging instance of the object. It detects each distinct object of interest in the image. [19] For example, when each person in a figure is segmented as an individual object. Panoptic segmentation combines both semantic and instance segmentation. Like semantic ...
Watershed delineation is the process of identifying the boundary of a watershed, also referred to as a catchment, drainage basin, or river basin. It is an important step in many areas of environmental science, engineering, and management, for example to study flooding, aquatic habitat, or water pollution.
This algorithm with its applications in computer vision is described in more detail in Lindeberg's thesis [7] as well as the monograph on scale-space theory [8] partially based on that work. Earlier presentations of this algorithm can also be found in [9] [10]. More detailed treatments of applications of grey-level blob detection and the scale ...
As applied in the field of computer vision, graph cut optimization can be employed to efficiently solve a wide variety of low-level computer vision problems (early vision [1]), such as image smoothing, the stereo correspondence problem, image segmentation, object co-segmentation, and many other computer vision problems that can be formulated in terms of energy minimization.
A fully automatic brain segmentation algorithm based on closely related ideas of multi-scale watersheds has been presented by Undeman and Lindeberg [12] and been extensively tested in brain databases. These ideas for multi-scale image segmentation by linking image structures over scales have also been picked up by Florack and Kuijper. [13]
The snakes model is popular in computer vision, and snakes are widely used in applications like object tracking, shape recognition, segmentation, edge detection and stereo matching. A snake is an energy minimizing, deformable spline influenced by constraint and image forces that pull it towards object contours and internal forces that resist ...