Wydawnictwa Uczelniane / TUL Press

Stały URI zbioruhttp://hdl.handle.net/11652/17

Przeglądaj

Filtry

20233

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Tematsearch.filters.applied.operator.equals: search.filters.subject.klasyfikacja

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Teraz wyświetlane 1 - 3 z 3
  • Pozycja
    Supporting Surgical Training with the Help of Computer Vision and Machine Learning Methods
    (Wydawnictwo Politechniki Łódzkiej, 2023) Forczmański, Paweł; Ryder, Yoonhee C; Mott, Nicole M; Gross, Christopher L.; Yu, Joon B.; Rooney, Deborah M.; Jeffcoach, David R.; Bidwell, Serena; Anidi, Chioma; Rosenthal, Lindsay; Kim, Grace J.
    The paper presents a novel concept of laparoscopic skills evaluation based on the automated analysis of videos recorded during simulationbased training exercises via an artificial intelligence algorithm. It has been tested on data collected during the training of actual surgeons. Its performance is promising, providing an opportunity to build an automatic system used mainly in developing countries.
  • Pozycja
    Graph-Supported Preparation of GIS Machine Learning Datasets
    (Wydawnictwo Politechniki Łódzkiej, 2023) Ernst, Sebastian
    The paper presents an approach to preparing spatial (GIS) datasets for machine learning models, and using graph structure to materialise and utilise the results. The presented work is based on the Spatially-Triggered Graph Transformations (STGT) methodology, previously used for many realworld applications, e.g. in the area of smart cities. A workflow using OSM data is presented, aimed at improving the granularity and semantic annotation of map features.
  • Pozycja
    Transformers Neural Networks Applications in Different Computer Vision Tasks
    (Wydawnictwo Politechniki Łódzkiej, 2023) Brodzicki, Andrzej; Piekarski, Michał; Kostuch, Aleksander; Noworolnik, Filip; Aleksandrowicz, Maciej; Wójcicka, Anna; Jaworek-Korjakowska, Joanna
    Transformers architectures are one of the latest inventions in the field of deep learning. Originally dedicated to NLP, they begin to find use in computer vision too. In this paper, we briefly describe the idea behind vision transformers and present a few examples, where we utilised them in our research, focusing on the field of medical images and autonomous driving. We show, that vision transformers can be used in various tasks, such as detection or classification, as well as explain how some of their drawbacks can be mitigated with a transfer learning approach.