Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to thei...
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Optical motion tracking systems often require a lot of manual work to generate clean labeled trajectories. This can be a deterrent if the goal is the creation of large motion tracking datasets. Especially in the case ...
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Optical motion tracking systems often require a lot of manual work to generate clean labeled trajectories. This can be a deterrent if the goal is the creation of large motion tracking datasets. Especially in the case of hand tracking, issues of occlusion (often self-occlusion by other fingers) make the post-processing task very difficult and time intensive. We introduce a fully automatic optical motion tracking method that utilizes a model based inverse kinematics approach. The Hungarian method is used to efficiently calculate associations between model markers and motion capture markers and we demonstrate an elegant solution to the problem of occlusions using a posture interpolation step.
The aim of this work is to propose the fully automated pathological area extraction from multi-parametric 2D MR images of brain. The proposed method is based on multi-resolution symmetry analysis and automatic thresho...
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Methods for mobile robot localization that use eigenspaces of panoramic snapshots of the environment are in general sensitive to changes in the illumination of the environment. Therefore, we propose an approach which ...
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Methods for mobile robot localization that use eigen spaces of panoramic snapshots of the environment are in general sensitive to changes in the illumination of the environment. Therefore, we propose an approach which...
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Methods for mobile robot localization that use eigenspaces of panoramic snapshots of the environment are in general sensitive to changes in the illumination of the environment. Therefore, we propose an approach which ...
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Methods for mobile robot localization that use eigenspaces of panoramic snapshots of the environment are in general sensitive to changes in the illumination of the environment. Therefore, we propose an approach which achieves a reliable localization under severe illumination conditions. The method uses gradient filtering of the eigenspace. After testing the approach on images obtained by a mobile robot, we show that it outperforms the standard eigenspace-based recognition method.
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