The next POI recommendation aiming at recommending the venues that people are likely interested in has become a popular service provided by location-based social networks such as Foursquare and Gowalla. Many existing ...
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Expressiveness varies from one person to another. Most images posted on Twitter lack good labels and the accompanying tweets have a lot of noise. Hence, in this paper we identify the contents and sentiments in images ...
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In many applications, data are organized as graphs (e.g., social network and smart city). There could be unstructured data on such a graph, for example, the users’ avatars and images included in a post. It is natural...
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Process Mining typically requires event logs where each event is labelled with a process activity. That’s not always the case, as many process-aware information systems store process-related information in the form o...
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In the field of large-scale field reconstruction, Kriging has been a commonly used technique for spatial interpolation at unobserved locations. However, Kriging’s effectiveness is often restricted when dealing with n...
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The paper deals with the issue of comparing trajectories in augmented and mixed realities incorporated in medical and healthcare systems. The comparison process brings to a sequence of comparing pairs of elements of t...
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The rapid development of data analysis technology and easily accessible datasets enable the construction of comprehensive analysis models, promoting decision-making processes in various fields. Decision trees, known f...
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In this work, we compare the performance of a non-sequentially trained Multilayer Perceptron (MLP) for financial time series forecasting with more complex models like Time-series Neural Networks (TNN) and Long Short-T...
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Pose Estimation is an important component of many realworld computer vision systems. Most existing pose estimation algorithms need a large number of point correspondences to accurately determine the pose of an object....
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ISBN:
(纸本)9783031479656;9783031479663
Pose Estimation is an important component of many realworld computer vision systems. Most existing pose estimation algorithms need a large number of point correspondences to accurately determine the pose of an object. Since the number of point correspondences depends on the object's appearance, lighting and other external conditions, detecting many points may not be feasible. In many real-world applications, movement of objects is limited due to gravity. Hence, detecting objects with only three degrees of freedom is usually sufficient. This allows us to improve the accuracy of pose estimation by changing the underlying equation of the perspective-n-point problem to allow only three variables instead of six. By using the improved equations, our algorithm is more robust against detection errors with limited point correspondences. In this paper, we specify two scenarios where such constraints apply. The first one is about parking a vehicle on a specific spot, while the second scenario describes a camera observing objects from a birds-eye view. In both scenarios, objects can only move in the ground plane and rotate around the vertical axis. Experiments with synthetic data and real-world photographs have shown that our algorithm outperforms state-of-the-art pose estimation algorithms. Depending on the scenario, our algorithm usually achieves 50% better accuracy, while being equally fast.
Drought, a huge scale natural disaster translating lives of many globally, appears no less often than yearly bringing scarcity of rainfall;dry spells majorly affecting crop fields. It can be characterized based on its...
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