Attribute based affordance detection from human-object interaction images

Mahmudul Hassan, Anuja Dharmaratne

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

    8 Citations (Scopus)


    The detection of functional classification of an object, which is also referred as affordance is a prevalent researched topic in the domain of robotics and computer vision. Typically, the approaches regarding fine level affordance (affordance related to core traits of an object i.e. graspability, rollability etc.) detection are often disjoint from the techniques in higher level affordance detection (i.e. drinkability or pourability of a glass). In this paper, we have proposed an attribute based technique for higher level affordance detection which integrates methods from both fine level and high level affordance detection, and takes three prominent contexts (Human, Object and the ambience) into account. It further represents each of these contexts as a cluster of attributes rather than singular entities thus making the affordance detection process more semantic, efficient, dynamic and general.

    Original languageEnglish
    Title of host publicationImage and Video Technology – PSIVT 2015 Workshops RV 2015, GPID 2013, VG 2015, EO4AS 2015, MCBMIIA 2015, and VSWS 2015, Revised Selected Papers
    EditorsAkihiro Sugimoto, Fay Huang
    Number of pages13
    ISBN (Print)9783319302843
    Publication statusPublished - 2016
    EventRobot Vision 2015 - Auckland, New Zealand
    Duration: 23 Nov 201527 Nov 2015 (Proceedings)

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    ConferenceRobot Vision 2015
    Abbreviated titleRV 2015
    Country/TerritoryNew Zealand
    OtherHeld as a workshop as part of Pacific-Rim Symposium on Image and Video Technology 2015
    Internet address


    • Affordance
    • Attribute transfer
    • Modelling mutual contexts

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