In this chapter we will introduce a new effective framework for learning an object detector. The main idea is to minimize the manual effort when learning a classifier and to combine the power of a discriminative class...
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Common methods for interactive texture segmentation rely on probability maps based on low dimensional features such as e.g. intensity or color, that are usually modeled using basic learning algorithms such as histogra...
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We repeated a study on the usage of a magic lens and a static peephole interface for playing a find-and-select game in a public space. While we reproduced the study setup and procedure the task was conducted in a publ...
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We examine a new method of façade segmentation in a multi-view scenario. A set of overlapping, thus redundant street-side images exists and each image shows multiple buildings. A semantic segmentation identifies ...
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In this paper, we propose a novel, efficient and fast method to extract the walkable area from high-resolution aerial images for the purpose of computer-aided evacuation simulation for major public events. Compared to...
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Randomized learning methods (i.e., Forests or Ferns) have shown excellent capabilities for various computervision applications. However, it was shown that the tree structure in Forests can be replaced by even simpler...
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We propose a novel method for unsupervised semantic image segmentation based on mutual information maximization between local and global high-level image features. The core idea of our work is to leverage recent progr...
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Simultaneous localization and mapping (SLAM) is a basic prerequisite in autonomous mobile robotics. Most existing visual SLAM approaches either assume a static environment, or simply 'forget' old parts of the ...
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Pose estimation is essential for automated handling of objects. In many computervision applications only the object silhouettes can be acquired reliably, because untextured or slightly transparent objects do not allo...
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In this work, we propose a robust visual odometry system for RGBD sensors. The core of our method is a combination of edge images and depth maps for joint camera pose estimation. Edges are more stable under varying li...
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