Temat: reference machines - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: XENOS – the new standard
Autorzy:: Werner, K.
Powiązania:: https://bibliotekanauki.pl/articles/132160.pdf
Data publikacji:: 2014
Wydawca:: Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:: measuring machine
reference machines
Opis:: XENOS. The name conjures up images of superheroes. Fitting, considering that the new coordinate measuring machine from ZEISS is entering a new realm of accuracy and dynamics with its innovative technology. XENOS delivers length measurement error of just 0.3 micrometers – unbeatable accuracy in this machine class. ZEISS has repeatedly pushed the limits of contact measuring with systems such as ZEISS PRISMO, ZEISS UPMC ultra or ZEISS F25. Today, they serve as reference machines in national institutes around the world.
Źródło:: Advanced Technologies in Mechanics; 2014, 1, no. 1 (1); 28-31
2392-0327
Pojawia się w:: Advanced Technologies in Mechanics
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: A two-layer neural system for reduced-reference visual quality assessment
Autorzy:: Redi, J.
Gastaldo, P.
Zunino, R.
Powiązania:: https://bibliotekanauki.pl/articles/91584.pdf
Data publikacji:: 2011
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: reduced-reference system
image
visual quality assessmen
luminance distribution
visual distortions
support vector machines
SVM
Opis:: Real-time assessment of visual quality can be efficiently supported by reduced-refe-rence paradigms, which require a very limited amount of information on the original signal, easily embeddable in the signal itself. In this paper, a reduced-reference system for image quality assessment is proposed, based on a small sized numerical description of images encoding the luminance distribution and its variations due to visual distortions. The assessment paradigm is implemented exploiting machine learning tools and articulates in two phases: first, a Support Vector Machines-based classifier identifies the kind of distortion affecting the image; then, the actual quality level of the distorted image is computed by a specifically trained SVM regressor. The general validity of the approach is supported by experimental validations based on subjective quality data.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2011, 1, 1; 27-41
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

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