Wyniki wyszukiwania - Biblioteka Nauki

Nowa wersja platformy, zawierająca wyłącznie zasoby pełnotekstowe, jest już dostępna.
Przejdź na https://bibliotekanauki.pl

Ograniczanie wyników

2 Machine Graphics and Vision

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Sortuj według:

Ogranicz wyniki do:

Image annotating tools for agricultural purpose : a requirements study

100%

Bator M. , Pankiewicz M.

Machine Graphics and Vision

2019

tom Vol. 28, No. 1/4

69--77

Images of natural scenes, like those relevant for agriculture, are characterised with a variety of forms of objects of interest and similarities between objects that one might want to discriminate. This introduces uncertainty to the analysis of such images. Requirements for an image annotation tool to be used in pattern recognition design for agriculture were discussed. A selection of open source annotating tools were presented. Advices how to use the software to handle uncertainty and missing functionalities were described.

Assessment of the Possibility of Imitating Experts’ Aesthetic Judgments about the Impact of Knots on the Beauty of Furniture Fronts Made of Pine Wood

80%

Gajowniczek K. , Bator M. , Śmietańska K. , Górski J.

Machine Graphics and Vision

2023

tom Vol. 32, No. 2

67--88

Our research aims to reconstruct expert preferences regarding the visual attractiveness of furniture fronts made of pine wood using machine learning algorithms. A numerical experiment was performed using five machine learning algorithms of various paradigms. To find the answer to the question of what determines the expert’s decision, we determined the importance of variables for some machine learning models. For random forest and classification trees, it involves the overall reduction in node impurities resulting from variable splitting, while for neural networks it uses the Garson algorithm. Based on the numerical experiments we can conclude that the best results of expert decision reconstruction are provided by a neural network model. The expert’s decision is better reconstructed for more beautiful images. The decision for nice images is made based on the best 4 or 5 variables, while for ugly images many more features are important. Prettier images and those for which the expert’s decision is better reconstructed have fewer knots.