Graph cuts-combinatorial optimization in vision

    Research output: Chapter in Book/Report/Conference proceedingChapter

    2 Citations (Scopus)

    Abstract

    Many problems in computer vision, image processing, and computer graphics can be put into labeling problems [1]. In such a problem, an undirected graph is given as an abstraction of locations and their neighborhood structure, along with a set of labels. Then, the solutions to the problem is identified with labelings, or assignments of a label to each vertex in the graph. The problem is then to find the best labeling according to the criteria in the problem's requirements. An energy is a translation of the criteria into a function that evaluates how good the given labeling is, so that smaller energy for a labeling means a better corresponding solution to the problem. Thus, the problem becomes an “energy minimization problem”. This separates the problem and the technique to solve it in a useful way by formulating the problem as an energy, it tends to make the problem more clearly defined, and also, once the problem is translated into an energy minimization problem, it can be solved using general algorithms.

    Original languageEnglish
    Title of host publicationImage Processing and Analysis with Graphs
    Subtitle of host publicationTheory and Practice
    PublisherCRC Press
    Pages25-64
    Number of pages40
    ISBN (Electronic)9781439855089
    ISBN (Print)9781315217284
    DOIs
    Publication statusPublished - 2012 Jan 1

    Fingerprint

    Combinatorial optimization
    Labeling
    Labels
    Computer graphics
    Computer vision
    Image processing

    ASJC Scopus subject areas

    • Computer Science(all)
    • Engineering(all)

    Cite this

    Ishikawa, H. (2012). Graph cuts-combinatorial optimization in vision. In Image Processing and Analysis with Graphs: Theory and Practice (pp. 25-64). CRC Press. https://doi.org/10.1201/b12281

    Graph cuts-combinatorial optimization in vision. / Ishikawa, Hiroshi.

    Image Processing and Analysis with Graphs: Theory and Practice. CRC Press, 2012. p. 25-64.

    Research output: Chapter in Book/Report/Conference proceedingChapter

    Ishikawa, H 2012, Graph cuts-combinatorial optimization in vision. in Image Processing and Analysis with Graphs: Theory and Practice. CRC Press, pp. 25-64. https://doi.org/10.1201/b12281
    Ishikawa H. Graph cuts-combinatorial optimization in vision. In Image Processing and Analysis with Graphs: Theory and Practice. CRC Press. 2012. p. 25-64 https://doi.org/10.1201/b12281
    Ishikawa, Hiroshi. / Graph cuts-combinatorial optimization in vision. Image Processing and Analysis with Graphs: Theory and Practice. CRC Press, 2012. pp. 25-64
    @inbook{a131c574c4454aa0abadec2205e7a627,
    title = "Graph cuts-combinatorial optimization in vision",
    abstract = "Many problems in computer vision, image processing, and computer graphics can be put into labeling problems [1]. In such a problem, an undirected graph is given as an abstraction of locations and their neighborhood structure, along with a set of labels. Then, the solutions to the problem is identified with labelings, or assignments of a label to each vertex in the graph. The problem is then to find the best labeling according to the criteria in the problem's requirements. An energy is a translation of the criteria into a function that evaluates how good the given labeling is, so that smaller energy for a labeling means a better corresponding solution to the problem. Thus, the problem becomes an “energy minimization problem”. This separates the problem and the technique to solve it in a useful way by formulating the problem as an energy, it tends to make the problem more clearly defined, and also, once the problem is translated into an energy minimization problem, it can be solved using general algorithms.",
    author = "Hiroshi Ishikawa",
    year = "2012",
    month = "1",
    day = "1",
    doi = "10.1201/b12281",
    language = "English",
    isbn = "9781315217284",
    pages = "25--64",
    booktitle = "Image Processing and Analysis with Graphs",
    publisher = "CRC Press",

    }

    TY - CHAP

    T1 - Graph cuts-combinatorial optimization in vision

    AU - Ishikawa, Hiroshi

    PY - 2012/1/1

    Y1 - 2012/1/1

    N2 - Many problems in computer vision, image processing, and computer graphics can be put into labeling problems [1]. In such a problem, an undirected graph is given as an abstraction of locations and their neighborhood structure, along with a set of labels. Then, the solutions to the problem is identified with labelings, or assignments of a label to each vertex in the graph. The problem is then to find the best labeling according to the criteria in the problem's requirements. An energy is a translation of the criteria into a function that evaluates how good the given labeling is, so that smaller energy for a labeling means a better corresponding solution to the problem. Thus, the problem becomes an “energy minimization problem”. This separates the problem and the technique to solve it in a useful way by formulating the problem as an energy, it tends to make the problem more clearly defined, and also, once the problem is translated into an energy minimization problem, it can be solved using general algorithms.

    AB - Many problems in computer vision, image processing, and computer graphics can be put into labeling problems [1]. In such a problem, an undirected graph is given as an abstraction of locations and their neighborhood structure, along with a set of labels. Then, the solutions to the problem is identified with labelings, or assignments of a label to each vertex in the graph. The problem is then to find the best labeling according to the criteria in the problem's requirements. An energy is a translation of the criteria into a function that evaluates how good the given labeling is, so that smaller energy for a labeling means a better corresponding solution to the problem. Thus, the problem becomes an “energy minimization problem”. This separates the problem and the technique to solve it in a useful way by formulating the problem as an energy, it tends to make the problem more clearly defined, and also, once the problem is translated into an energy minimization problem, it can be solved using general algorithms.

    UR - http://www.scopus.com/inward/record.url?scp=85046821035&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=85046821035&partnerID=8YFLogxK

    U2 - 10.1201/b12281

    DO - 10.1201/b12281

    M3 - Chapter

    SN - 9781315217284

    SP - 25

    EP - 64

    BT - Image Processing and Analysis with Graphs

    PB - CRC Press

    ER -