Temat: multispectral image - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Detection of human eye components on the basis of multispectral imaging
Autorzy:: Michalak, M.
Nurzyńska, K.
Świtoński, A.
Powiązania:: https://bibliotekanauki.pl/articles/333415.pdf
Data publikacji:: 2012
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: rozpoznawanie wzorców
analiza wielospektralna
przetwarzanie obrazów
segmentacja obrazu
pattern recognition
multispectral analysis
image processing
image segmentation
Opis:: In this paper the methods for selecting of the most important parts of the human eyes are described. On the basis of the real 21 channel multispectral images the model of finding the lens and the spot are defined. These methods are based on the most popular algorithms of image processing. The approach to veins detection is still undefined but in the article the most important channels are pointed out and the channel difference between eyelash and the veins is also mentioned.
Źródło:: Journal of Medical Informatics & Technologies; 2012, 19; 41-47
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Fast multispectral deep fusion networks
Autorzy:: Osin, V.
Cichocki, A.
Burnaev, E.
Powiązania:: https://bibliotekanauki.pl/articles/200648.pdf
Data publikacji:: 2018
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: multispectral imaging
data fusion
deep learning
convolutional network
object detection
image segmentation
obrazowanie wielospektralne
fuzja danych
uczenie głębokie
sieci splotowe
wykrywanie obiektów
segmentacja obrazu
Opis:: Most current state-of-the-art computer vision algorithms use images captured by cameras, which operate in the visible spectral range as input data. Thus, image recognition systems that build on top of those algorithms can not provide acceptable recognition quality in poor lighting conditions, e.g. during nighttime. Another significant limitation of such systems is high demand for computational resources, which makes them impossible to use on low-powered embedded systems without GPU support. This work attempts to create an algorithm for pattern recognition that will consolidate data from visible and infrared spectral ranges and allow near real-time performance on embedded systems with infrared and visible sensors. First, we analyze existing methods of combining data from different spectral ranges for object detection task. Based on the analysis, an architecture of a deep convolutional neural network is proposed for the fusion of multi-spectral data. This architecture is based on the single shot multi-box detection algorithm. Comparison analysis of the proposed architecture with previously proposed solutions for the multi-spectral object detection task shows comparable or better detection accuracy with previous algorithms and significant improvement of the running time on embedded systems. This study was conducted in collaboration with Philips Lighting Research Lab and solutions based on the proposed architecture will be used in image recognition systems for the next generation of intelligent lighting systems. Thus, the main scientific outcomes of this work include an algorithm for multi-spectral pattern recognition based on convolutional neural networks, as well as a modification of detection algorithms for working on embedded systems.
Źródło:: Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 6; 875-889
0239-7528
Pojawia się w:: Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:: Biblioteka Nauki

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