Perception-Action Based Object Detection From Local Descriptor Combination And Reinforcement Learning

Lucas Paletta, Gerald Fritz, Christin Seifert

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


    This work proposes to learn visual encodings of attention patterns\nthat enables sequential attention for object detection in real world\nenvironments. The system embeds a saccadic decision procedure in\na cascaded process where visual evidence is probed at informative\nimage locations. It is based on the extraction of information theoretic\nsaliency by determining informative local image descriptors that\nprovide selected foci of interest. The local information in terms\nof code book vector responses and the geometric information in the\nshift of attention contribute to recognition states of a Markov decision\nprocess. A Q-learner performs then performs search on useful actions\ntowards salient locations, developing a strategy of action sequences\ndirected in state space towards the optimization of information maximization.\nThe method is evaluated in outdoor object recognition and demonstrates\nefficient performance.
    Original languageEnglish
    Title of host publicationImage Analysis
    Subtitle of host publication14th Scandinavian Conference, SCIA 2005, Joensuu, Finland, June 19-22, 2005. Proceedings
    EditorsHeikki Kalviainen, Jussi Parkkinen, Arto Kaarna
    Number of pages10
    ISBN (Electronic)978-3-540-31566-7
    ISBN (Print)978-3-540-26320-3
    Publication statusPublished - 2005
    Event14th Scandinavian Conference on Image Analysis, SCIA 2005 - Joensuu, Finland
    Duration: 19 Jun 200522 Jun 2005
    Conference number: 14

    Publication series

    NameLecture Notes in Computer Science
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Conference14th Scandinavian Conference on Image Analysis, SCIA 2005
    Abbreviated titleSCIA


    Dive into the research topics of 'Perception-Action Based Object Detection From Local Descriptor Combination And Reinforcement Learning'. Together they form a unique fingerprint.

    Cite this