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In recent years, a great number of datasets were published to train and evaluate computer vision (CV) algorithms. These valuable contributions helped to push CV solutions to a level where they can be used for safety-relevant applications, such as autonomous driving. However, major questions concerning quality and usefulness of test data for CV evaluation are still unanswered. Researchers and engineers...
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 systems: the hazard and operability analysis...
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 and Operability Analysis (HAZOP). It is designed...
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 objects can be effectively distinguished by...
This paper introduces a model-based approach for testing robustness of computer vision solutions with respect to a given task or application. Assessment of essential CV component robustness is crucial to ensure a safe robot and human coexistence. Currently this is mostly a manual and heuristic task lacking reliable metrics for determining the completeness and strength of a given test set. Our novel...
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