Zobrazeno 1 - 10
of 303
pro vyhledávání: '"Pattichis, Marios S."'
The paper develops datasets and methods to assess student participation in real-life collaborative learning environments. In collaborative learning environments, students are organized into small groups where they are free to interact within their gr
Externí odkaz:
http://arxiv.org/abs/2405.02317
There is strong interest in developing mathematical methods that can be used to understand complex neural networks used in image analysis. In this paper, we introduce techniques from Linear Algebra to model neural network layers as maps between signa
Externí odkaz:
http://arxiv.org/abs/2402.00261
The paper provides a survey of the development of machine-learning techniques for video analysis. The survey provides a summary of the most popular deep learning methods used for human activity recognition. We discuss how popular architectures perfor
Externí odkaz:
http://arxiv.org/abs/2312.05352
Large-scale training of Convolutional Neural Networks (CNN) is extremely demanding in terms of computational resources. Also, for specific applications, the standard use of transfer learning also tends to require far more resources than what may be n
Externí odkaz:
http://arxiv.org/abs/2207.00672
We introduce the problem of detecting a group of students from classroom videos. The problem requires the detection of students from different angles and the separation of the group from other groups in long videos (one to one and a half hours). We u
Externí odkaz:
http://arxiv.org/abs/2112.12217
Autor:
Liapi, Georgia D., Loizou, Christos P., Pattichis, Constantinos S., Pattichis, Marios S., Nicolaides, Andrew N., Griffin, Maura, Kyriacou, Efthyvoulos
Publikováno v:
In Computer Methods and Programs in Biomedicine December 2024 257
We study the problem of detecting talking activities in collaborative learning videos. Our approach uses head detection and projections of the log-magnitude of optical flow vectors to reduce the problem to a simple classification of small projection
Externí odkaz:
http://arxiv.org/abs/2110.07646
Autor:
Teeparthi, Sravani, Jatla, Venkatesh, Pattichis, Marios S., Pattichis, Sylvia Celedon, LopezLeiva, Carlos
Long-term object detection requires the integration of frame-based results over several seconds. For non-deformable objects, long-term detection is often addressed using object detection followed by video tracking. Unfortunately, tracking is inapplic
Externí odkaz:
http://arxiv.org/abs/2110.07070
In the last two decades, unsupervised latent variable models---blind source separation (BSS) especially---have enjoyed a strong reputation for the interpretable features they produce. Seldom do these models combine the rich diversity of information a
Externí odkaz:
http://arxiv.org/abs/1911.04048
Autor:
Ulloa, Alvaro, Jing, Linyuan, Good, Christopher W, vanMaanen, David P, Raghunath, Sushravya, Suever, Jonathan D, Nevius, Christopher D, Wehner, Gregory J, Hartzel, Dustin, Leader, Joseph B, Alsaid, Amro, Patel, Aalpen A, Kirchner, H Lester, Pattichis, Marios S, Haggerty, Christopher M, Fornwalt, Brandon K
Predicting future clinical events helps physicians guide appropriate intervention. Machine learning has tremendous promise to assist physicians with predictions based on the discovery of complex patterns from historical data, such as large, longitudi
Externí odkaz:
http://arxiv.org/abs/1811.10553