Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Marko Subašić"'
Publikováno v:
Diagnostics, Vol 14, Iss 13, p 1443 (2024)
Objectives: The purpose of this study was to develop a deep learning algorithm capable of diagnosing radicular cysts in the lower jaw on panoramic radiographs. Materials and Methods: In this study, we conducted a comprehensive analysis of 138 radicul
Externí odkaz:
https://doaj.org/article/19a24c5432444c11972c20a4631b8d7b
Publikováno v:
Acta Stomatologica Croatica, Vol 57, Iss 1, Pp 70-84 (2023)
Introduction: Artificial intelligence has been applied in various fields throughout history, but its integration into daily life is more recent. The first applications of AI were primarily in academia and government research institutions, but as tech
Externí odkaz:
https://doaj.org/article/342f9b0244f84f458bcbda161f85464b
Publikováno v:
Sensors, Vol 24, Iss 2, p 693 (2024)
The term out-of-stock (OOS) describes a problem that occurs when shoppers come to a store and the product they are seeking is not present on its designated shelf. Missing products generate huge sales losses and may lead to a declining reputation or t
Externí odkaz:
https://doaj.org/article/f896877c37554a0f873cd8e4a7ddefe6
Publikováno v:
Medicina, Vol 59, Iss 12, p 2138 (2023)
Background and Objectives: The purpose of this study was to develop and evaluate a deep learning model capable of autonomously detecting and segmenting radiolucent lesions in the lower jaw by utilizing You Only Look Once (YOLO) v8. Materials and Meth
Externí odkaz:
https://doaj.org/article/79af96f7b8d84854b30447407d135be5
Publikováno v:
Croatian Journal of Forest Engineering, Vol 31, Iss 2, Pp 157-163 (2010)
Forest dieback is taking on increasing proportions in many parts of Croatia. To improve the situation, it is of primary importance to acquire timely, accurate and inexpensive information on the scale of forest damage. Such information can be collecte
Externí odkaz:
https://doaj.org/article/ad276b114448438c9faaa68f508ab2aa
Publikováno v:
Croatian Journal of Forest Engineering, Vol 29, Iss 2, Pp 201-211 (2008)
The paper explores the possibilities of assessing five stand parameters (tree number, volume, stocking, basal area and stand age) with the application of a multi-layer perceptron artificial neural network. An IKONOS satellite image (PAN 1 m x 1 m) wa
Externí odkaz:
https://doaj.org/article/cf1fe5277a1347cbb59f1d5fbd791eb0
In this paper, we introduce a new large-scale publicly available color constancy dataset which we are calling the Shadows & Lumination dataset. The dataset contains 2500 minimally processed images from various indoor, outdoor, and night-time scenes.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::189e81225d7bd7c3e404334cf38a38e6
https://www.bib.irb.hr/1265610
https://www.bib.irb.hr/1265610
Image white-balancing is an integral part of every camera’s processing pipeline. White-balancing is used to remove illumination chromaticity from an image. Most research in this field has been limited to images with a single uniform illuminant. In
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::97600565f94a800ee245aa8ccf054ce1
https://www.bib.irb.hr/1264894
https://www.bib.irb.hr/1264894
Images have an ever-increasing presence in our daily lives. This increases the need for accurate and efficient image processing. One of the first processing steps in modern cameras is image white-balancing, the process of making the image invariant t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::46cf5d94bdebafde77bc574d35b63d19
https://doi.org/10.1016/j.patrec.2022.04.035
https://doi.org/10.1016/j.patrec.2022.04.035
Color constancy is an important part of the Human Visual System that allows us to recognize colors of object invariant to the light that is illuminating them. Computational color constancy is the process of estimating the illumination of a scene usin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fad57fb5a478f508e51c68211470c895
https://doi.org/10.1016/j.image.2022.116822
https://doi.org/10.1016/j.image.2022.116822