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teaching:gsoc2017 [2017/02/08 08:07] – [KnowRob -- Robot Knowledge Processing] lisca | teaching:gsoc2017 [2017/02/08 08:10] – [Proposed Topics] lisca | ||
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use-cases can be found at the [[http:// | use-cases can be found at the [[http:// | ||
website]]. | website]]. | ||
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+ | ===== openEASE -- Experiment Knowledge Database ===== | ||
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+ | OpenEASE is a generic knowledge database for collecting and analysing experiment data. Its foundation is the KnowRob knowledge processing system and ROS, enhanced by reasoning mechanisms and a web interface developed for inspecting comprehensive experiment logs. These logs can be recorded for example from complex CRAM plan executions, virtual reality experiments, | ||
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+ | The OpenEASE web interface as well as further information and publication material can be accessed through its publicly available [[http:// | ||
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+ | ===== RoboSherlock -- Framework for Cognitive Perception ===== | ||
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+ | RoboSherlock is a common framework for cognitive perception, based on the principle of unstructured information management (UIM). UIM has proven itself to be a powerful paradigm for scaling intelligent information and question answering systems towards real-world complexity (i.e. the Watson system from IBM). Complexity in UIM is handled by identifying (or hypothesizing) pieces of | ||
+ | structured information in unstructured documents, by applying ensembles of experts for annotating information pieces, and by testing and integrating these isolated annotations into a comprehensive interpretation of the document. | ||
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+ | RoboSherlock builds on top of the ROS ecosystem and is able to wrap almost any existing perception algorithm/ | ||
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+ | ===== Proposed Topics ===== | ||
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+ | In the following, we list our proposals for the Google Summer of Code topics that contribute to the aforementioned | ||
+ | open-source projects. | ||
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+ | ==== Topic 1: Multi-modal Cluttered Scene Analysis in Knowledge Intensive Scenarios ==== | ||
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+ | {{ : | ||
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+ | **Main Objective: | ||
+ | edge detection). | ||
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+ | **Task Difficulty: | ||
+ | | ||
+ | **Requirements: | ||
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+ | **Expected Results:** Currently the RoboSherlock framework lacks good perception algorithms that can generate object-hypotheses in challenging scenarios(clutter and/or occlusion). The expected results are several software components based on recent advances in cluttered scene analysis that are able to successfully recognized objects in the scenarios mentioned in the objectives, or a subset of these. | ||
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+ | Contact: [[team/ | ||
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Prof. Dr. hc. Michael Beetz PhD
Head of Institute
Contact via
Andrea Cowley
assistant to Prof. Beetz
ai-office@cs.uni-bremen.de
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