His primary areas of study are Computer network, Multimedia, Artificial intelligence, Bandwidth and Access network. Pål Halvorsen interconnects Wireless, Distributed computing and Mobile device in the investigation of issues within Computer network. His Multimedia research is multidisciplinary, incorporating elements of Automatic summarization, User experience design, Disease detection and Panorama.
His studies in Artificial intelligence integrate themes in fields like Field and Pattern recognition. Pål Halvorsen combines subjects such as Layer, Quality of experience, Throughput, Adaptation and User studies with his study of Bandwidth. His Access network research is multidisciplinary, relying on both Multihoming and The Internet.
Pål Halvorsen mostly deals with Artificial intelligence, Computer network, Multimedia, Distributed computing and Deep learning. Pål Halvorsen has researched Artificial intelligence in several fields, including Machine learning, Computer vision and Pattern recognition. Computer network connects with themes related to The Internet in his study.
His Multimedia research is multidisciplinary, incorporating perspectives in Visualization and World Wide Web. His Distributed computing study combines topics from a wide range of disciplines, such as Overlay network, Scalability, Latency and Parallel computing. In his study, which falls under the umbrella issue of Bandwidth, Real-time computing is strongly linked to Video quality.
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Segmentation, Deep learning and Convolutional neural network. His Artificial intelligence study combines topics in areas such as Field, Machine learning and Identification. The concepts of his Pattern recognition study are interwoven with issues in Contextual image classification, Convolution and Autoencoder.
Pål Halvorsen focuses mostly in the field of Segmentation, narrowing it down to topics relating to Gold standard and, in certain cases, Noise. His Deep learning study integrates concerns from other disciplines, such as Artificial neural network and Medical imaging. His research integrates issues of Transfer of learning and Object in his study of Convolutional neural network.
Pål Halvorsen mainly investigates Artificial intelligence, Segmentation, Pattern recognition, Deep learning and Convolutional neural network. His work carried out in the field of Artificial intelligence brings together such families of science as Field, Machine learning and Identification. The Field study combines topics in areas such as Domain, Computer-aided diagnosis, Information retrieval and Data analysis.
His Segmentation study is concerned with the field of Computer vision as a whole. The Video processing and Texture research Pål Halvorsen does as part of his general Computer vision study is frequently linked to other disciplines of science, such as Medical instruments, therefore creating a link between diverse domains of science. His work on Image segmentation and Sørensen–Dice coefficient as part of general Pattern recognition study is frequently connected to Colonoscopy, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
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Commute path bandwidth traces from 3G networks: analysis and applications
Haakon Riiser;Paul Vigmostad;Carsten Griwodz;Pål Halvorsen.
acm sigmm conference on multimedia systems (2013)
KVASIR: A Multi-Class Image Dataset for Computer Aided Gastrointestinal Disease Detection
Konstantin Pogorelov;Kristin Ranheim Randel;Carsten Griwodz;Sigrun Losada Eskeland.
acm sigmm conference on multimedia systems (2017)
Kvasir-SEG: A Segmented Polyp Dataset
Debesh Jha;Pia H. Smedsrud;Michael A. Riegler;Pål Halvorsen.
conference on multimedia modeling (2020)
ResUNet++: An Advanced Architecture for Medical Image Segmentation
Debesh Jha;Pia H. Smedsrud;Michael A. Riegler;Dag Johansen.
international symposium on multimedia (2019)
Tiling in Interactive Panoramic Video: Approaches and Evaluation
Vamsidhar Reddy Gaddam;Michael Riegler;Ragnhild Eg;Carsten Griwodz.
IEEE Transactions on Multimedia (2016)
Flicker effects in adaptive video streaming to handheld devices
Pengpeng Ni;Ragnhild Eg;Alexander Eichhorn;Carsten Griwodz.
acm multimedia (2011)
HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy.
Hanna Borgli;Vajira Thambawita;Pia H Smedsrud;Steven Hicks.
Scientific Data (2020)
ChaLearn Joint Contest on Multimedia Challenges Beyond Visual Analysis: An overview
Hugo Jair Escalante;Victor Ponce-Lopez;Jun Wan;Michael A. Riegler.
international conference on pattern recognition (2016)
DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation
Debesh Jha;Michael A. Riegler;Dag Johansen;Pal Halvorsen.
computer-based medical systems (2020)
Soccer video and player position dataset
Svein Arne Pettersen;Dag Johansen;Håvard Johansen;Vegard Berg-Johansen.
acm sigmm conference on multimedia systems (2014)
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