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334,142 results on '"Mattern, Friedemann"'

1. Fine-Grained Product Class Recognition for Assisted Shopping

Author

George, Marian, Mircic, Dejan, Sörös, Gábor, Floerkemeier, Christian, and Mattern, Friedemann

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Assistive solutions for a better shopping experience can improve the quality of life of people, in particular also of visually impaired shoppers. We present a system that visually recognizes the fine-grained product classes of items on a shopping list, in shelves images taken with a smartphone in a grocery store. Our system consists of three components: (a) We automatically recognize useful text on product packaging, e.g., product name and brand, and build a mapping of words to product classes based on the large-scale GroceryProducts dataset. When the user populates the shopping list, we automatically infer the product class of each entered word. (b) We perform fine-grained product class recognition when the user is facing a shelf. We discover discriminative patches on product packaging to differentiate between visually similar product classes and to increase the robustness against continuous changes in product design. (c) We continuously improve the recognition accuracy through active learning. Our experiments show the robustness of the proposed method against cross-domain challenges, and the scalability to an increasing number of products with minimal re-training., Comment: Accepted at ICCV Workshop on Assistive Computer Vision and Robotics (ICCV-ACVR) 2015

Published
2015

2. From the Internet of Computers to the Internet of Things

Author

Mattern, Friedemann, Floerkemeier, Christian, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Sachs, Kai, editor, Petrov, Ilia, editor, and Guerrero, Pablo, editor

Published
2010
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7. Super-distributed RFID Tag Infrastructures

Author

Bohn, Jürgen, Mattern, Friedemann, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Markopoulos, Panos, editor, Eggen, Berry, editor, Aarts, Emile, editor, and Crowley, James L., editor

Published
2004
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10. Evaluating Mobile Phones as Energy Consumption Feedback Devices

Author

Weiss, Markus, Loock, Claire-Michelle, Staake, Thorsten, Mattern, Friedemann, Fleisch, Elgar, Akan, Ozgur, Series editor, Bellavista, Paolo, Series editor, Cao, Jiannong, Series editor, Dressler, Falko, Series editor, Ferrari, Domenico, Series editor, Gerla, Mario, Series editor, Kobayashi, Hisashi, Series editor, Palazzo, Sergio, Series editor, Sahni, Sartaj, Series editor, Shen, Xuemin (Sherman), Series editor, Stan, Mircea, Series editor, Xiaohua, Jia, Series editor, Zomaya, Albert, Series editor, Coulson, Geoffrey, Series editor, Sénac, Patrick, editor, Ott, Max, editor, and Seneviratne, Aruna, editor

Published
2012
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48. Automatically estimating the savings potential of occupancy-based heating strategies

Author

Becker, Vincent, Kleiminger, Wilhelm, and Mattern, Friedemann

Subjects
Household heating simulation, Smart heating, Occupancy detection, Energy savings, Smart energy
Abstract

A large fraction of energy consumed in households is due to space heating. Especially during daytime, the heating is often running constantly, controlled only by a thermostat – even if the inhabitants are not present. Taking advantage of the absence of the inhabitants to save heating energy by lowering the temperature thus poses a great opportunity. Since the concrete savings of an occupancy-based heating strategy strongly depend on the individual occupancy pattern, a fast and inexpensive method to quantify these potential savings would be beneficial. In this paper we present such a practical method which builds upon an approach to estimate a household’s occupancy from its historical electricity consumption data, as gathered by smart meters. Based on the derived occupancy data, we automatically calculate the potential savings. Besides occupancy data, the underlying model also takes into account publicly available weather data and relevant building characteristics. Using this approach, households with high potential for energy savings can be quickly identified and their members could be more easily convinced to adopt an occupancy-based heating strategy (either by manually adjusting the thermostat or by investing in automation) since their monetary benefits can be calculated and the risk of misinvestment is thus reduced. To prove the usefulness of our system, we apply it to a large dataset containing relevant building and household data such as the size and age of several thousand households and show that, on average, a household can save over 9% heating energy when following an occupancy-based heating regime, while certain groups, such as single-person households, can even save 14% on average.

Published
2017

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