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  2. Split and merge segmentation - Wikipedia

    en.wikipedia.org/wiki/Split_and_merge_segmentation

    Download QR code; Print/export ... Split and merge segmentation is an image processing technique ... segmentation of a gray scale image using matlab. [2 ...

  3. 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 ...

  4. List of datasets in computer vision and image processing

    en.wikipedia.org/wiki/List_of_datasets_in...

    Image captioning 2016 [8] R. Krishna et al. Berkeley 3-D Object Dataset 849 images taken in 75 different scenes. About 50 different object classes are labeled. Object bounding boxes and labeling. 849 labeled images, text Object recognition 2014 [9] [10] A. Janoch et al. Berkeley Segmentation Data Set and Benchmarks 500 (BSDS500)

  5. Random walker algorithm - Wikipedia

    en.wikipedia.org/wiki/Random_walker_algorithm

    Beyond image segmentation, the random walker algorithm or its extensions has been additionally applied to several problems in computer vision and graphics: Image Colorization [18] Interactive rotoscoping [19] Medical image segmentation [20] [21] [22] Merging multiple segmentations [23] Mesh segmentation [24] [25] Mesh denoising [26 ...

  6. Graph cuts in computer vision - Wikipedia

    en.wikipedia.org/wiki/Graph_cuts_in_computer_vision

    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.

  7. 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.

  8. Active contour model - Wikipedia

    en.wikipedia.org/wiki/Active_contour_model

    A simple elastic snake is defined by a set of n points for =, …,, the internal elastic energy term , and the external edge-based energy term .The purpose of the internal energy term is to control the deformations made to the snake, and the purpose of the external energy term is to control the fitting of the contour onto the image.

  9. Region growing - Wikipedia

    en.wikipedia.org/wiki/Region_growing

    Region growing is a simple region-based image segmentation method. It is also classified as a pixel-based image segmentation method since it involves the selection of initial seed points. This approach to segmentation examines neighboring pixels of initial seed points and determines whether the pixel neighbors should be added to the region.