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The range is one of the measures of variation. It can be defined as the difference between a dataset's highest and lowest values. For example, in the study of seven 16-ounce soda cans, the filled volume of soda was measured, thus producing the following amount (in ounces) of soda:
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Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters
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A New Filtering System for Using a Consumer Depth Camera at Close Range.

Yuanxing Dai1, Yanming Fu2, Baichun Li3

  • 1School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110004, China. yuanxing_dai@163.com.

Sensors (Basel, Switzerland)
|August 11, 2019
PubMed
Summary
This summary is machine-generated.

Consumer depth cameras create noise at close ranges, hindering real-time applications. This study introduces a composite filtering system to effectively remove noise from depth images, improving 3D reconstruction and human-computer interaction.

Keywords:
Kinect v2close rangedepth image filteringdepth resolutionhand posepoint clouds filtering

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Area of Science:

  • Computer Vision
  • 3D Sensing
  • Image Processing

Background:

  • Consumer depth cameras offer high resolution at close range but introduce significant noise, particularly at object edges.
  • This noise impedes real-time applications that require immediate, post-processing-free point cloud data.
  • Existing methods often rely on window smoothing, which can blur fine details and are not ideal for real-time scenarios.

Purpose of the Study:

  • To develop a novel composite filtering system for eliminating noise in depth images acquired by consumer cameras at close range.
  • To address the limitations of existing noise reduction techniques for real-time depth data processing.
  • To enhance the usability of depth camera data for immediate applications like human-computer interaction and 3D reconstruction.

Main Methods:

  • Proposed a composite filtering system with three modules designed to target and eliminate distinct types of noise regions based on their position and shape.
  • Developed noise elimination algorithms that do not rely on window smoothing, preserving surface details.
  • Implemented GPU acceleration for all algorithms to achieve high-speed processing.

Main Results:

  • The filtering system effectively eliminated a significant portion of noise areas in human hand depth images.
  • Contrast experiments using Kinect v2 and SR300 demonstrated good results and high real-time performance.
  • The system proved capable of handling noise without window smoothing, maintaining data integrity.

Conclusions:

  • The proposed composite filtering system is effective in reducing noise from close-range consumer depth cameras.
  • The system's real-time performance and noise reduction capabilities make it suitable as a pre-processing step for various applications.
  • This approach enhances the feasibility of real-time human-computer interaction and 3D reconstruction using depth sensor data.