@inproceedings{MTMT:33097232,
	title = {Towards Using Fully Observable Policies for POMDPs},
	url = {https://m2.mtmt.hu/api/publication/33097232},
	author = {Sulyok, András Attila and Karacs, Kristóf},
	booktitle = {2022 2nd International Conference on Computing and Machine Intelligence (ICMI)},
	doi = {10.1109/ICMI55296.2022.9873768},
	unique-id = {33097232},
	year = {2022},
	pages = {1-5}
}

@article{MTMT:33039528,
	title = {OCT-leletek telemedicinális értékelésének pontossága cukorbetegekben},
	url = {https://m2.mtmt.hu/api/publication/33039528},
	author = {Németh, János Tibor and Nyitrai, Beatrix and Karacs, Kristóf and Szabó, Dorottya and Ecsedy, Mónika and Szalai, Irén and Tóth, Gábor and Sándor, Gábor László and Magyar, Márton and Benyó, Fruzsina and Papp, András},
	doi = {10.55342/szemhungarica.2022.159.2.64},
	journal-iso = {SZEMÉSZET},
	journal = {SZEMÉSZET},
	volume = {159},
	unique-id = {33039528},
	issn = {0039-8101},
	year = {2022},
	pages = {64-68},
	orcid-numbers = {Németh, János Tibor/0000-0001-8575-4888; Ecsedy, Mónika/0000-0001-8472-2382; Szalai, Irén/0000-0001-9245-4285; Tóth, Gábor/0000-0002-9176-9442; Sándor, Gábor László/0000-0002-2484-9848; Papp, András/0000-0002-6824-2909}
}

@article{MTMT:32783352,
	title = {Tracking Highly Similar Rat Instances under Heavy Occlusions: An Unsupervised Deep Generative Pipeline},
	url = {https://m2.mtmt.hu/api/publication/32783352},
	author = {Gelencsér-Horváth, Anna and Kopácsi, László and Varga, Viktor and Keller, Dávid and Dobolyi, Árpád and Karacs, Kristóf and Lőrincz, András},
	doi = {10.3390/jimaging8040109},
	journal-iso = {J IMAGING},
	journal = {JOURNAL OF IMAGING},
	volume = {8},
	unique-id = {32783352},
	issn = {2313-433X},
	abstract = {Identity tracking and instance segmentation are crucial in several areas of biological research. Behavior analysis of individuals in groups of similar animals is a task that emerges frequently in agriculture or pharmaceutical studies, among others. Automated annotation of many hours of surveillance videos can facilitate a large number of biological studies/experiments, which otherwise would not be feasible. Solutions based on machine learning generally perform well in tracking and instance segmentation; however, in the case of identical, unmarked instances (e.g., white rats or mice), even state-of-the-art approaches can frequently fail. We propose a pipeline of deep generative models for identity tracking and instance segmentation of highly similar instances, which, in contrast to most region-based approaches, exploits edge information and consequently helps to resolve ambiguity in heavily occluded cases. Our method is trained by synthetic data generation techniques, not requiring prior human annotation. We show that our approach greatly outperforms other state-of-the-art unsupervised methods in identity tracking and instance segmentation of unmarked rats in real-world laboratory video recordings.},
	year = {2022},
	orcid-numbers = {Gelencsér-Horváth, Anna/0000-0002-8223-960X; Varga, Viktor/0000-0003-0410-6171; Keller, Dávid/0000-0002-5105-2295; Dobolyi, Árpád/0000-0003-0397-2991; Lőrincz, András/0000-0002-1280-3447}
}

@article{MTMT:31306422,
	title = {Peeling off image layers on topographic architectures},
	url = {https://m2.mtmt.hu/api/publication/31306422},
	author = {Radványi, Mihály Gergely and Karacs, Kristóf},
	doi = {10.1016/j.patrec.2020.04.023},
	journal-iso = {PATTERN RECOGN LETT},
	journal = {PATTERN RECOGNITION LETTERS},
	volume = {135},
	unique-id = {31306422},
	issn = {0167-8655},
	year = {2020},
	eissn = {1872-7344},
	pages = {50-56},
	orcid-numbers = {Radványi, Mihály Gergely/0000-0002-9329-1630}
}

@article{MTMT:30954730,
	title = {Működő telemedicinális szemészeti szűrőprogramok és lehetőségek hazánkban},
	url = {https://m2.mtmt.hu/api/publication/30954730},
	author = {Németh, János Tibor and Maka, Erika and Szabó, Dorottya and Somogyvári, Zsolt and Kovács, Gábor and Tóth, Gábor and Papp, András and Karacs, Kristóf and Nagy, Zoltán Zsolt},
	journal-iso = {IME},
	journal = {IME},
	volume = {18},
	unique-id = {30954730},
	issn = {1588-6387},
	year = {2019},
	eissn = {1789-9974},
	pages = {46-51},
	orcid-numbers = {Németh, János Tibor/0000-0001-8575-4888; Maka, Erika/0000-0002-3631-3506; Somogyvári, Zsolt/0000-0003-2026-6407; Tóth, Gábor/0000-0002-9176-9442; Papp, András/0000-0002-6824-2909; Nagy, Zoltán Zsolt/0000-0002-7330-0464}
}

@misc{MTMT:30926541,
	title = {Telemedicina a szemészetben, múlt, jelen és jövő},
	url = {https://m2.mtmt.hu/api/publication/30926541},
	author = {Németh, János Tibor and Maka, Erika and Szabó, Dorottya and Somogyvári, Zsolt and Kovács, Gábor and Papp, András and Karacs, Kristóf and Nagy, Zoltán Zsolt},
	unique-id = {30926541},
	year = {2019},
	orcid-numbers = {Németh, János Tibor/0000-0001-8575-4888; Maka, Erika/0000-0002-3631-3506; Somogyvári, Zsolt/0000-0003-2026-6407; Papp, András/0000-0002-6824-2909; Nagy, Zoltán Zsolt/0000-0002-7330-0464}
}

@article{MTMT:3383887,
	title = {Shape Recognition Based on Projected Edges and Global Statistical Features},
	url = {https://m2.mtmt.hu/api/publication/3383887},
	author = {Stubendek, Attila and Karacs, Kristóf},
	doi = {10.1155/2018/4763050},
	journal-iso = {MATH PROBL ENG},
	journal = {MATHEMATICAL PROBLEMS IN ENGINEERING},
	volume = {2018},
	unique-id = {3383887},
	issn = {1024-123X},
	year = {2018},
	eissn = {1563-5147}
}

@inproceedings{MTMT:3335502,
	title = {Saliency based Attention Mechanism for Topographic Architectures},
	url = {https://m2.mtmt.hu/api/publication/3335502},
	author = {Radványi, Mihály Gergely and Karacs, Kristóf},
	booktitle = {15th International Workshop on Cellular Nanoscale Networks and their Applications},
	unique-id = {3335502},
	year = {2016}
}

@inproceedings{MTMT:2964429,
	title = {An Integrated Assistance Tool for Visual Impairment},
	url = {https://m2.mtmt.hu/api/publication/2964429},
	author = {Karacs, Kristóf and Stubendek, Attila and Radványi, Mihály Gergely},
	booktitle = {Proceedings of the Workshop on Information Technology and Bionics},
	unique-id = {2964429},
	year = {2015},
	pages = {87-90}
}

@inproceedings{MTMT:3029261,
	title = {Learning hierarchical spatial semantics for visual orientation devices},
	url = {https://m2.mtmt.hu/api/publication/3029261},
	author = {Karacs, Kristóf and Radványi, Mihály Gergely and Stubendek, Attila and Bezanyi, B},
	booktitle = {10th IEEE Biomedical Circuits and Systems Conference, BioCAS 2014},
	doi = {10.1109/BioCAS.2014.6981665},
	unique-id = {3029261},
	abstract = {Complexity of understanding a visual scene is the single biggest challenge in creating intelligent devices for visually impaired people. The requirement of real time operation makes it inevitable to design algorithms that obey the computing and memory limits of available hardware. We present a hierarchical scene understanding system implemented on a vision system chip. It is restricted to extract specific information for predefined categories of visual scenes, but it is general enough to be able to learn quickly and autonomously. Patches having potential discriminative information are extracted using a hierarchical peeling method. Object groups are created based on proximity and size of the patches. Objects are classified using different classifiers and the votes are combined using a mixture of experts network. Experimental validation has been carried out on authentic image flows recorded by blind subjects. © 2014 IEEE.},
	keywords = {complex networks; Semantics; Computer vision; Integrated circuits; Experimental validations; Intelligent devices; Visually impaired people; Scene understanding; Spatial semantics; Specific information; Real-time operation; Mixture of experts network},
	year = {2014},
	pages = {141-144}
}