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Test data plays an important role in computer vision (CV) but is plagued by two questions: Which situations should be covered by the test data and have we tested enough to reach a conclusion? In this paper we propose a new solution answering these questions using a standard procedure devised by the safety community to validate complex systems: The Hazard(More)
This extended abstract outlines a model-based approach for generating test data to assess the robustness of computer vision (CV) solutions with respect to a given task or application. The outlined approach enables the automatic generation of test data with a measurable coverage of optical situations both typical as well as critical for a given application.(More)
This paper presents a technique for automatic distribution of points on 3D-surfaces that are defined as meshes of polygons (usually triangles) such that the distribution has a low discrepancy. The work is motivated by the quest for representing arbitrary 3D-objects by a minimal number of surface points such that different views and arbitrary occlusions of(More)
In this work we show how to specifically sample domain parameters-for a certain system under test (SUT)-to create corresponding test data in order to find the system's limits of operation and discover its flaws. The SUT is part of an aerial sense and avoid system that performs aerial object detection in a video stream. In order to generate synthetic test(More)
Good test data is crucial for driving new developments in computer vision (CV), but two questions remain unanswered: which situations should be covered by the test data, and how much testing is enough to reach a conclusion? In this paper we propose a new answer to these questions using a standard procedure devised by the safety community to validate complex(More)
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