Zobrazeno 1 - 10
of 2 955
pro vyhledávání: '"Wielgosz, A."'
This study examines the effectiveness of Spiking Neural Networks (SNNs) paired with Dynamic Vision Sensors (DVS) to improve pedestrian detection in adverse weather, a significant challenge for autonomous vehicles. Utilizing the high temporal resoluti
Externí odkaz:
http://arxiv.org/abs/2406.00473
This research advances individual tree crown (ITC) segmentation in lidar data, using a deep learning model applicable to various laser scanning types: airborne (ULS), terrestrial (TLS), and mobile (MLS). It addresses the challenge of transferability
Externí odkaz:
http://arxiv.org/abs/2401.15739
Publikováno v:
26th IEEE International Conference on Intelligent Transportation Systems ITSC 2023
Pedestrian intention prediction is crucial for autonomous driving. In particular, knowing if pedestrians are going to cross in front of the ego-vehicle is core to performing safe and comfortable maneuvers. Creating accurate and fast models that predi
Externí odkaz:
http://arxiv.org/abs/2401.06757
Autor:
Xiang, Binbin, Wielgosz, Maciej, Kontogianni, Theodora, Peters, Torben, Puliti, Stefano, Astrup, Rasmus, Schindler, Konrad
Detailed forest inventories are critical for sustainable and flexible management of forest resources, to conserve various ecosystem services. Modern airborne laser scanners deliver high-density point clouds with great potential for fine-scale forest
Externí odkaz:
http://arxiv.org/abs/2312.15084
Autor:
Puliti, Stefano, Pearse, Grant, Surový, Peter, Wallace, Luke, Hollaus, Markus, Wielgosz, Maciej, Astrup, Rasmus
The FOR-instance dataset (available at https://doi.org/10.5281/zenodo.8287792) addresses the challenge of accurate individual tree segmentation from laser scanning data, crucial for understanding forest ecosystems and sustainable management. Despite
Externí odkaz:
http://arxiv.org/abs/2309.01279
Autor:
Krupiński, Jan, Wielgosz, Maciej, Mazurek, Szymon, Strzałka, Krystian, Russek, Paweł, Caputa, Jakub, Łukasik, Daria, Grzeszczyk, Jakub, Karwatowski, Michał, Fraczek, Rafał, Jamro, Ernest, Pietroń, Marcin, Koryciak, Sebastian, Dąbrowska-Boruch, Agnieszka, Wiatr, Kazimierz
This paper presents a computer-aided cytology diagnosis system designed for animals, focusing on image quality assessment (IQA) using Convolutional Neural Networks (CNNs). The system's building blocks are tailored to seamlessly integrate IQA, ensurin
Externí odkaz:
http://arxiv.org/abs/2308.06055
Autor:
Strzałka, Krystian, Mazurek, Szymon, Wielgosz, Maciej, Russek, Paweł, Caputa, Jakub, Łukasik, Daria, Krupiński, Jan, Grzeszczyk, Jakub, Karwatowski, Michał, Frączek, Rafał, Jamro, Ernest, Pietroń, Marcin, Koryciak, Sebastian, Dąbrowska-Boruch, Agnieszka, Wiatr, Kazimierz
This paper explores the innovative use of simulation environments to enhance data acquisition and diagnostics in veterinary medicine, focusing specifically on gait analysis in dogs. The study harnesses the power of Blender and the Blenderproc library
Externí odkaz:
http://arxiv.org/abs/2307.11695
Autor:
Mazurek, Szymon, Wielgosz, Maciej
In this paper, we delve into the critical aspect of dataset quality assessment in machine learning classification tasks. Leveraging a variety of nine distinct datasets, each crafted for classification tasks with varying complexity levels, we illustra
Externí odkaz:
http://arxiv.org/abs/2306.15392
Autor:
Caputa, Jakub, Wielgosz, Maciej, Łukasik, Daria, Russek, Paweł, Grzeszczyk, Jakub, Karwatowski, Michał, Mazurek, Szymon, Frączek, Rafał, Śmiech, Anna, Jamro, Ernest, Koryciak, Sebastian, Dąbrowska-Boruch, Agnieszka, Pietroń, Marcin, Wiatr, Kazimierz
The primary objective of this research was to enhance the quality of semantic segmentation in cytology images by incorporating super-resolution (SR) architectures. An additional contribution was the development of a novel dataset aimed at improving i
Externí odkaz:
http://arxiv.org/abs/2306.11848
Autor:
Grzeszczyk, Jakub, Karwatowski, Michał, Łukasik, Daria, Wielgosz, Maciej, Russek, Paweł, Mazurek, Szymon, Caputa, Jakub, Frączek, Rafał, Śmiech, Anna, Jamro, Ernest, Koryciak, Sebastian, Dąbrowska-Boruch, Agnieszka, Pietroń, Marcin, Wiatr, Kazimierz
This paper shows the machine learning system which performs instance segmentation of cytological images in veterinary medicine. Eleven cell types were used directly and indirectly in the experiments, including damaged and unrecognized categories. The
Externí odkaz:
http://arxiv.org/abs/2305.04332