Temat: atr - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Automatic target recognition of complex internal structure
Autorzy:: Niedziela, T.
Powiązania:: https://bibliotekanauki.pl/articles/223757.pdf
Data publikacji:: 2014
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: Automatic Target Recognition - ATR
target detection
Fourier transform
transformata Fouriera
rozpoznawanie obrazów
układy automatycznego rozpoznania
Opis:: The paper presents methods of automatic target recognition from images with complex internal structure. Recognition methods proposed are based on the Fourier transform properties. These methods make it possible to bring the problem to Fourier transform, feature extraction and input image classification. Image data synthesis within diffraction plane significantly increases the probability of target detection.
Źródło:: Archives of Transport; 2014, 32, 4; 61-72
0866-9546
2300-8830
Pojawia się w:: Archives of Transport
Dostawca treści:: Biblioteka Nauki

Artykuł

Skocz do pozycji: 2.

Tytuł:: Development of models for determining the traffic volume for the analysis of roads efficiency
Autorzy:: Spławińska, M.
Powiązania:: https://bibliotekanauki.pl/articles/223741.pdf
Data publikacji:: 2015
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: roads
automatic traffic recorder
ATR
design hourly volume
DHV
multiple regression
artificial neural networks
drogi
automatyczny rejestrator ruchu
analiza efektywności dróg
sztuczna inteligencja
Opis:: The article presents different methods of estimating DHV, including traditional Factor Approach, developed Regression Models and Artificial Neural Networks models. As explanatory variables: quantitative variables (AADT and the share of heavy vehicles) as well as qualitative variables (the cross-section, roads class, nature of the area, the profile of seasonal variations, region of Poland and the nature of traffic patterns) were used. In addition, the results of preliminary analyses of the DHV estimates based on the maximum hourly volume derived from a few hours traffic measurement on weekdays where there is the greatest share of hours with the highest traffic volume in the year were presented. On the basis of comparisons of the presented methods, Multiple Regression Model was identified as the most useful.
Źródło:: Archives of Transport; 2015, 33, 1; 81-91
0866-9546
2300-8830
Pojawia się w:: Archives of Transport
Dostawca treści:: Biblioteka Nauki

Artykuł

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