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Automated Joint Space Detection Improves Bone Segmentation Accuracy
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Directional joint bilateral filter for depth images.

Anh Vu Le1, Seung-Won Jung2, Chee Sun Won3

  • 1Department of Electronics and Electrical Engineering, Dongguk University-Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea. levuanh.hut@gmail.com.

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Summary
This summary is machine-generated.

This study introduces adaptive directional filters to improve noisy depth maps from Kinect sensors. Our method effectively fills holes and reduces noise, enhancing depth map quality for computer vision applications.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Sensor Technology

Background:

  • Depth maps from low-cost sensors like Kinect are prone to noise and missing data.
  • Reliable depth data is crucial for applications such as object recognition and multi-view rendering.

Purpose of the Study:

  • To develop advanced post-processing techniques for enhancing depth map quality.
  • To introduce novel adaptive directional filters for noise suppression and hole filling in depth maps.

Main Methods:

  • Proposed adaptive directional filters with window shapes adjusted based on color image edge direction.
  • Implemented hole-filling and noise-suppression algorithms for depth map post-processing.

Main Results:

  • Demonstrated superior performance in filtering depth maps compared to existing methods.
  • Achieved significant improvements in depth map quality, particularly around edge boundaries.

Conclusions:

  • Adaptive directional filters offer a robust solution for improving depth map reliability.
  • The proposed method enhances depth map quality for advanced computer vision and rendering tasks.