Autor: Woods, Nancy - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Encapsulation of image metadata for ease of retrieval and mobility
Autorzy:: Woods, Nancy
Robert, Charles
Powiązania:: https://bibliotekanauki.pl/articles/117866.pdf
Data publikacji:: 2019
Wydawca:: Polskie Towarzystwo Promocji Wiedzy
Tematy:: automatic image annotation
image tagging
metadata
automatyczna adnotacja obrazu
znakowanie obrazów
metadane
Opis:: Increasing proliferation of images due to multimedia capabilities of hand-held devices has resulted in loss of source information resulting from inherent mobility. These images are cumbersome to search out once stored away from their original source because they drop their descriptive data. This work, developed a model to encapsulate descriptive metadata into the Exif section of image header for effective retrieval and mobility. The resulting metadata used for retrieval purposes was mobile, searchable and non-obstructive.
Źródło:: Applied Computer Science; 2019, 15, 1; 62-73
1895-3735
Pojawia się w:: Applied Computer Science
Dostawca treści:: Biblioteka Nauki

Artykuł

Skocz do pozycji: 2.

Tytuł:: A robust ensemble model for spoken language recognition
Autorzy:: Woods, Nancy
Babatunde, Gideon
Powiązania:: https://bibliotekanauki.pl/articles/118275.pdf
Data publikacji:: 2020
Wydawca:: Polskie Towarzystwo Promocji Wiedzy
Tematy:: spoken language recognition
computer vision
image recognition
CNN
rozpoznawanie języka mówionego
widzenie komputerowe
rozpoznawanie obrazu
Opis:: The identity of a language being spoken has been tackled over the years via statistical models on audio samples. A drawback of these approaches is the unavailability of phonetically transcribed data for all languages. This work proposes an approach based on image classification that utilized image representations of audio samples. Our model used Neural Networks and deep learning algorithms to analyse and classify three languages. The input to our network is a Spectrogram that was processed through the networks to extract local visual and temporal features for language prediction. From the model, we achieved 95.56 % accuracy on the test samples from the 3 languages.
Źródło:: Applied Computer Science; 2020, 16, 3; 56-68
1895-3735
Pojawia się w:: Applied Computer Science
Dostawca treści:: Biblioteka Nauki

Artykuł

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