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Automated Object Labeling for Cnn-Based Image Segmentation

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F20%3A00347540" target="_blank" >RIV/68407700:21340/20:00347540 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1109/ICIP40778.2020.9191320" target="_blank" >http://dx.doi.org/10.1109/ICIP40778.2020.9191320</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/ICIP40778.2020.9191320" target="_blank" >10.1109/ICIP40778.2020.9191320</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Automated Object Labeling for Cnn-Based Image Segmentation

  • Original language description

    Deep learning-based methods for classification and segmentation require large training sets. Generating training data is often a tedious and expensive task. In industrial applications, such as automated visual inspection of products in an assemble line, objects for classification are well defined yet labeled data are difficult to obtain. To alleviate the problem of manual labeling, we propose to train a convolutional neural network with an automatically generated training set using a naive classifier with handcrafted features. We show that when the naive classifier has high precision then the trained network has both high precision and recall despite the low recall of the naive classifier. We demonstrate the proposed methodology on real scenario of detecting a car coolant tank. However, the proposed methodology facilitates collection of train data for a wider type of CNN based methods such as near-duplicate image detection or segmenting tampered areas of images.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10102 - Applied mathematics

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2020

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    IEEE International Conference on Image Processing

  • ISBN

    9781728163956

  • ISSN

  • e-ISSN

  • Number of pages

    5

  • Pages from-to

    2036-2040

  • Publisher name

    IEEE Computer Society

  • Place of publication

    Cannes

  • Event location

    Abu Dhabi

  • Event date

    Sep 25, 2020

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article