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  2. Minimum spanning tree-based segmentation - Wikipedia

    en.wikipedia.org/wiki/Minimum_spanning_tree...

    Image segmentation strives to partition a digital image into regions of pixels with similar properties, e.g. homogeneity. [1] The higher-level region representation simplifies image analysis tasks such as counting objects or detecting changes, because region attributes (e.g. average intensity or shape [2]) can be compared more readily than raw pixels.

  3. Random walker algorithm - Wikipedia

    en.wikipedia.org/wiki/Random_walker_algorithm

    The random walker algorithm is an algorithm for image segmentation.In the first description of the algorithm, [1] a user interactively labels a small number of pixels with known labels (called seeds), e.g., "object" and "background".

  4. ITK-SNAP - Wikipedia

    en.wikipedia.org/wiki/ITK-SNAP

    Labeling can take place in all three orthogonal cut planes and results can be visualized as a three-dimensional rendering. This makes it easier to ensure that the segmentation maintains reasonable shape in 3D. Automatic segmentation ITK-SNAP provides automatic functionality segmentation using the level-set method. This makes it possible to ...

  5. Insight Segmentation and Registration Toolkit - Wikipedia

    en.wikipedia.org/wiki/Insight_Segmentation_and...

    ITK is a cross-platform, open-source application development framework widely used for the development of image segmentation and image registration programs. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical ...

  6. Spectral clustering - Wikipedia

    en.wikipedia.org/wiki/Spectral_clustering

    A popular normalized spectral clustering technique is the normalized cuts algorithm or Shi–Malik algorithm introduced by Jianbo Shi and Jitendra Malik, [2] commonly used for image segmentation. It partitions points into two sets ( B 1 , B 2 ) {\displaystyle (B_{1},B_{2})} based on the eigenvector v {\displaystyle v} corresponding to the ...

  7. CVIPtools - Wikipedia

    en.wikipedia.org/wiki/CVIPtools

    The Computer Vision and Image Processing Feature Extraction and Pattern Classification Tool, CVIP-FEPC, is used to advance human and computer vision applications. While its primary function is computer vision, it serves various purposes, such as supporting the development of image compression schemes for human vision applications by identifying ...

  8. Image segmentation - Wikipedia

    en.wikipedia.org/wiki/Image_segmentation

    In digital image processing and computer vision, image segmentation is the process of partitioning a digital image into multiple image segments, also known as image regions or image objects (sets of pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to ...

  9. Split and merge segmentation - Wikipedia

    en.wikipedia.org/wiki/Split_and_merge_segmentation

    Split and merge segmentation is an image processing technique used to segment an image. The image is successively split into quadrants based on a homogeneity criterion and similar regions are merged to create the segmented result.