Research>Digital Human for Human Centered Design

4-Dimensional Measurement System - 3D Measurement of the Foot While Walking -

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Background/Overview

Most of the existing systems for measuring shapes of human body parts apply 3D reconstruction methods which use controlled light sources (e.g. scanning by laser beam). Such methods require several seconds to acquire data. Therefore, the subject must stand still during the measurement. The purpose of our research is to record the changes of foot shape while walking.

In the field of Computer Vision, 3D reconstruction from multiple images is a common challenge, and much research has been conducted. We have constructed a system to take multiple, trigger-synchronized movies.We are researching to create a 3D reconstruction from each set of frames to produce 4-Dimensional measurement data, charting 3D shapes through time. While our ultimate goal is to reconstruct the 4-Dimensional shape of the whole foot, currently, the focus is on the basic feature values (ball, instep, and heel) of the foot while walking.

Current Target

The 3 lines in the figure are called "ball", "instep", and "heel", and each line is defined by anatomical landmarks. In Japanese Industrial Standard (JIS), shoe size is defined by 3 parameters; "length of foot", "perimeter of ball", and "width of ball". At the same time, "instep" and "heel" are used to evaluate how a shoe fits. Thus, these 3 features are basic and important. In this research, we apply tape to these features, and reconstruct the 3D shape of those features as outlined by the tape.

3lines.jpg
Technical Points
  • Camera calibration
  • Feature region detection in each image
    • Feature regions are detected by subtraction of the background image, checking color values, and other indicators.

  • Edge-based, stereo matching methods
    • In order to get stabilized matching, search for corresponding points only along the edges of the detected feature regions.
    • Using the zero-crossing method, estimate the edge of the feature regions with sub-pixel precision.

  • Selection of camera pairs for stereo matching
    • Selection using the direction of cameras
    • Selection using epipolar geometry
Capturing System

We have 6, IEEE1394 cameras synchronized by a trigger signal. Each camera is connected to a Windows PC. The capturing program is remotely controlled via TCP/IP. This system captures multiple stereo movies at 14FPS (70msec interval). The captured images are sized XGA (1027x768 pixels) and have 16bpp (4 Bytes in 2 pixels) YUV pixels.

cameras.jpg system.jpg
Examples of the Process and Output

I . Input images
cam01.jpg cam07.jpg cam02.jpg cam05.jpg cam06.jpg cam08.jpg

II . Detected feature regions in each image
region01.jpg region07.jpg region02.jpg region05.jpg region06.jpg region08.jpg

III . Principles for the selection of camera pairs for stereo matching

III-A . Do not use a pair of cameras showing images in opposing directions as shown below.
fig1a.jpg fig1b.jpg

III-B . Do not use a camera pair when the edge direction is perpendicular to a point on the epipolar line.
fig2a.jpg fig2b.jpg fig2c.jpg

III-C . Only use points that have a properly matching correspondence.
fig3.jpg

IV . Reconstructed 3D shape from the above input images
cg1.jpg cg2.jpg cg3.jpg

V . Input movies (mpeg at half resolution) and a movie of the reconstructed 3D shape
Input Camera #1(160kB)
Input Camera #2(160kB)
Input Camera #3(160kB)
Input Camera #4(160kB)
Input Camera #5(160kB)
Input Camera #6(160kB)
Reconstructed 3D shape(9MB)