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%2F67985556%3A_____%2F20%3A00533825" target="_blank" >RIV/67985556:_____/20:00533825 - 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
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20206 - Computer hardware and architecture
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
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
2020 IEEE International Conference on Image Processing (ICIP)
ISBN
978-1-7281-6396-3
ISSN
1522-4880
e-ISSN
2381-8549
Number of pages
5
Pages from-to
2036-2040
Publisher name
IEEE
Place of publication
Piscataway
Event location
Abu Dhabi
Event date
Oct 25, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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