@inbook {25869,
	title = {Camera Synchronization for Panoramic Videos},
	booktitle = {MediaSync},
	year = {2018},
	month = {03/2018},
	pages = {565-592},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-319-65840-7_20},
	url = {https://doi.org/10.1007/978-3-319-65840-7_20},
	author = {Vamsidhar Reddy {Gaddam} and Langseth, Ragnar and H{\r a}kon Kvale {Stensland} and Griwodz, Carsten and Riegler, Michael and Kupka, Tomas and Espeland, H{\r a}vard and Johansen, Dag and Johansen, H{\r a}vard and Halvorsen, P{\r a}l},
	editor = {Montagud, Mario and Cesar, Pablo and Boronat, Fernando and Jansen, Jack}
}
@inproceedings {25433,
	title = {A Holistic Multimedia System for Gastrointestinal Tract Disease Detection},
	journal = {8th annual ACM conference on Multimedia Systems (MMSys)},
	year = {2017},
	month = {06/2017},
	pages = {112-123},
	publisher = {ACM},
	abstract = {Analysis of medical videos for detection of abnormalities and diseases requires both high precision and recall, but also real-time processing for live feedback and scalability for massive screening of entire populations. Existing work on this field does not provide the necessary combination of retrieval accuracy and performance.\&nbsp;In this paper, a multimedia system is presented where the aim is to tackle automatic analysis of videos from the human gastrointestinal (GI) tract. The system includes the whole pipeline from data collection, processing and analysis, to visualization. The system combines filters using machine learning, image recognition and extraction of global and local image features. Furthermore, it is built in a modular way so that it can easily be extended. At the same time, it is developed for efficient processing in order to provide real-time feedback to the doctors. Our experimental evaluation proves that our system has detection and localisation accuracy at least as good as existing systems for polyp detection, it is capable of detecting a wider range of diseases, it can analyze video in real-time, and it has a low resource consumption for scalability.},
	isbn = {978-1-4503-5002-0},
	doi = {10.1145/3083187.3083189},
	url = {http://dl.acm.org/citation.cfm?id=3083189},
	author = {Pogorelov, Konstantin and Sigrun Losadal {Eskeland} and de Lange, Thomas and Griwodz, Carsten and Kristin Ranheim {Randel} and H{\r a}kon Kvale {Stensland} and Dang-Nguyen, Duc-Tien and Spampinato, Concetto and Johansen, Dag and Riegler, Michael and Halvorsen, P{\r a}l},
	editor = {Cesar, Pablo and Hsu, Cheng-hsin}
}
@inproceedings {25432,
	title = {Load Balancing of Multimedia Workloads for Energy Efficiency on the Tegra K1 Multicore Architecture},
	journal = {8th annual ACM conference on Multimedia Systems (MMSys)},
	year = {2017},
	month = {06/2017},
	pages = {124-135},
	publisher = {ACM},
	abstract = {Energy efficiency is a timely topic for modern mobile computing. Reducing the\&nbsp;energy consumption of devices not only increases their battery lifetime, but\&nbsp;also reduces the risk of hardware failure. Many researchers strive tounderstand the relationship between software activity and hardware power usage.\&nbsp;A recurring strategy for saving power is to reduce operating frequencies. It is\&nbsp;widely acknowledged that standard frequency scaling algorithms generally\&nbsp;overreact to changes in hardware utilisation. More recent and original efforts\&nbsp;attempt to balance software workloads on heterogeneous multicore architectures,\&nbsp;such as the Tegra K1, which includes a quad-core CPU and a CUDA-capable GPU.\&nbsp;However, it is not known whether it is possible to utilise these processor\&nbsp;elements in parallel to save energy. Research into these types of systems are\&nbsp;unfortunately often evaluated with the Performance Per Watt (PPW) metric, which\&nbsp;is an unaccurate method because it ignores constant power usage from idle\&nbsp;components. We show that this metric can end up increase energy usage on the\&nbsp;Tegra K1, and give a false impression of how such systems consume energy. \&nbsp;In\&nbsp;reality, we show that it is much harder to save energy by balancing workloads\&nbsp;between the heterogeneous cores of the Tegra K1, where we demonstrate only a 5\%\&nbsp;energy saving by offloading 10\% DCT workload from the GPU to the CPU.\&nbsp;Significantly more energy can be saved (up to 50\%) using the appropriate\&nbsp;processor for different workloads.\&nbsp;},
	isbn = {978-1-4503-5002-0},
	doi = {10.1145/3083187.3083195},
	url = {http://dl.acm.org/citation.cfm?doid=3083187.3083195},
	author = {Kristoffer Robin {Stokke} and H{\r a}kon Kvale {Stensland} and Griwodz, Carsten and Halvorsen, P{\r a}l},
	editor = {Cesar, Pablo and Hsu, Cheng-hsin}
}
