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pro vyhledávání: '"Uřičář, Michal"'
Autor:
Šimsa, Štěpán, Šulc, Milan, Uřičář, Michal, Patel, Yash, Hamdi, Ahmed, Kocián, Matěj, Skalický, Matyáš, Matas, Jiří, Doucet, Antoine, Coustaty, Mickaël, Karatzas, Dimosthenis
This paper introduces the DocILE benchmark with the largest dataset of business documents for the tasks of Key Information Localization and Extraction and Line Item Recognition. It contains 6.7k annotated business documents, 100k synthetically genera
Externí odkaz:
http://arxiv.org/abs/2302.05658
Information extraction from semi-structured documents is crucial for frictionless business-to-business (B2B) communication. While machine learning problems related to Document Information Extraction (IE) have been studied for decades, many common pro
Externí odkaz:
http://arxiv.org/abs/2206.11229
Manual annotation of soiling on surround view cameras is a very challenging and expensive task. The unclear boundary for various soiling categories like water drops or mud particles usually results in a large variance in the annotation quality. As a
Externí odkaz:
http://arxiv.org/abs/2105.07930
Existing datasets for training pedestrian detectors in images suffer from limited appearance and pose variation. The most challenging scenarios are rarely included because they are too difficult to capture due to safety reasons, or they are very unli
Externí odkaz:
http://arxiv.org/abs/2012.08274
Autor:
Das, Arindam, Krizek, Pavel, Sistu, Ganesh, Burger, Fabian, Madasamy, Sankaralingam, Uricar, Michal, Kumar, Varun Ravi, Yogamani, Senthil
Automotive cameras, particularly surround-view cameras, tend to get soiled by mud, water, snow, etc. For higher levels of autonomous driving, it is necessary to have a soiling detection algorithm which will trigger an automatic cleaning system. Local
Externí odkaz:
http://arxiv.org/abs/2007.00801
Autor:
Maddu, Pullarao, Doherty, Wayne, Sistu, Ganesh, Leang, Isabelle, Uricar, Michal, Chennupati, Sumanth, Rashed, Hazem, Horgan, Jonathan, Hughes, Ciaran, Yogamani, Senthil
Automated Parking is a low speed manoeuvring scenario which is quite unstructured and complex, requiring full 360{\deg} near-field sensing around the vehicle. In this paper, we discuss the design and implementation of an automated parking system from
Externí odkaz:
http://arxiv.org/abs/1912.11066
Let's Get Dirty: GAN Based Data Augmentation for Camera Lens Soiling Detection in Autonomous Driving
Autor:
Uricar, Michal, Sistu, Ganesh, Rashed, Hazem, Vobecky, Antonin, Kumar, Varun Ravi, Krizek, Pavel, Burger, Fabian, Yogamani, Senthil
Wide-angle fisheye cameras are commonly used in automated driving for parking and low-speed navigation tasks. Four of such cameras form a surround-view system that provides a complete and detailed view of the vehicle. These cameras are directly expos
Externí odkaz:
http://arxiv.org/abs/1912.02249
Cameras are an essential part of sensor suite in autonomous driving. Surround-view cameras are directly exposed to external environment and are vulnerable to get soiled. Cameras have a much higher degradation in performance due to soiling compared to
Externí odkaz:
http://arxiv.org/abs/1905.01492
Autor:
Yogamani, Senthil, Hughes, Ciaran, Horgan, Jonathan, Sistu, Ganesh, Varley, Padraig, O'Dea, Derek, Uricar, Michal, Milz, Stefan, Simon, Martin, Amende, Karl, Witt, Christian, Rashed, Hazem, Chennupati, Sumanth, Nayak, Sanjaya, Mansoor, Saquib, Perroton, Xavier, Perez, Patrick
Fisheye cameras are commonly employed for obtaining a large field of view in surveillance, augmented reality and in particular automotive applications. In spite of their prevalence, there are few public datasets for detailed evaluation of computer vi
Externí odkaz:
http://arxiv.org/abs/1905.01489
Autor:
Uricar, Michal, Krizek, Pavel, Hurych, David, Sobh, Ibrahim, Yogamani, Senthil, Denny, Patrick
Generative Adversarial Networks (GAN) have gained a lot of popularity from their introduction in 2014 till present. Research on GAN is rapidly growing and there are many variants of the original GAN focusing on various aspects of deep learning. GAN a
Externí odkaz:
http://arxiv.org/abs/1902.03442