Dr. Bugao Xu - Fiber Research Project

1. Fiber Image Analysis System--FIAS (patent pending):
    The FIAS is a customized microscopic image analysis system for automatic, high-volume measurements of fiber geometric attributes. The major components of the FIAS include a zoomable microscope, a high resolution camera, a motorized stage, a pneumatic fiber cutter/spreader, and image analysis software. FIAS offers two basic functions: longitudinal analysis and cross-sectional analysis of fibers.
The longitudinal analysis scans a fiber transversely along its axis, and provides the measurements of fiber diameter/width, diameter distribution, blend ratio of two different fibers, cotton maturity, cotton dead fiber ratio, etc. Prior to the analysis, fibers are cut into 0.5mm snippets, and spread onto a microscope slide using the pneumatic fiber cutter/spreader. The fiber snippets on the slide are consecutively imaged and measured as the slide is automatically shifted by the motorized stage. Proprietary computer algorithms include fiber axis calculation, double transverse scans, false scan prevention, dead fiber detection, and cotton maturity calculation. It is expected to measure up to 50,000 fiber scans per slide, which are sufficient for reliable statistic analysis.
    The cross-sectional analysis provides feature measurements including fiber perimeter, area, equivalent diameter, and shape factors (circularity). For hollow fibers, such as cotton and medullated animal fibers, the wall thickness, maturity or medullation can be measured. For multi-lobal fibers, such as trilobal nylon fibers, the modification ratio can be measured.

            
FIAS (left) and Fiber Cutter/Spreader (right)

2. Imaging Colorimeter for Cotton Trash and Color Measurement
    A new system that automatically captures images of raw cotton, locates non-lint particles in the image, and uses fuzzy logic and neural network methods to classify cotton colors and trash particles. The system considerably improves the agreements with the human classer. It can perform multiple functions: 1) trash content and grading; 2) trash category (leaf, bark and seedcoat); 3) content of yellow spot; 4) color data in various color spaces (Rdab, CIE L*a*b* or CIE L*c*h*) and color grading.

3. Objective Evaluation of Fabric Appearance
    It is to develop the "single hardware setup" solution for multiple functions to objectively characterize and evaluate surface changes of fabrics such as wrinkling, fuzzing, pilling and shrinking. The ratings of these surface features were trained using the standard replicas (AATCC and ASTM) or physical samples representing different scales set visually by experts. The system has greatly improved the objectivity of fabric appearance evaluations, and reduced the cost and the time for performing multiple tests.

4. Laser Profilometer for Wear Assessment
    The project was to measure surface characteristics (profile, roughness, fuzziness, etc.) of a wide range of materials such as fabric, plastic and metal. The system consists of a laser displacement sensor (10 um resolution), motorized x-y mechanical stage (6x6 inch2 travel), and customized software.

    

5. Three-Dimensional Technology for Apparel Mass Customization
    An integrated system has been developed to perform body scanning, body modeling, body measurement, and virtual try-on.

5.1. Rotary body scanner: The rotary scanning unit mainly comprises a CCD camera and a laser line projector. Both the camera and the projector are driven by a step motor, and rotate synchronically. The computer then traces the line in each image and calculates the 3D coordinates of all the pixels on the line using a triangulation algorithm. The coordinates from the frontal and rear images are registered together to form a full body surface according to the relative positions of the two scanning units. A total of 250 to 350 images are captured and processed within 5-7 seconds, depending on the height of the subject. The rotary scanning makes the unit much more compact, lighter and thus more portable. Each unit weighs less than 2.5 kilograms, and the entire setup takes a floorprint of 1.5m by 2.4m.

Loose scans and dense scans

5.2. Body surface modeling: the scanned data are reorganized in a uniform density through the Delaunay triangulation and linear interpolation. The body is divided into six cylindrical segments, and the cubic B-spline surface approximation is performed on each segment separately. The individual B-spline surfaces are then merged to create a seamless model, which not only dramatically reduces the size of the data needed to reconstruct the surface but also smoothes the scan data.

Scanned and Reconstructed Torsos

5.3. Automatic body Measurement: Various tools were developed to automatically locate key body landmarks and to measure the circumferences, widths, heights and distances at or between the landmarks.

5.4. Virtual Clothing: The garment dressing simulation conducts a transformation of multiple 2D garment patterns into a 3D configuration that follows the surface of a human body, and displays the draping style of the garment. During this transformation, the dimensional stability of the garment patterns is an important constraint that needs to be imposed to ensure the created garment to have a size complied with the original design of the patterns. The four issues are fabric modeling, mesh generation, wrapping and draping.

    Fabric modeling is a way to simulate fabric properties using a simple mechanic system. This is an important step for obtaining realistic visualization of a 3D garment. I A mass-spring system constitutes a matrix of mass particles, representing the distributed mass of the fabric, and three types of springs connecting the particles and representing three different internal forces: tensile, shearing and bending. A mass-spring system with reasonable combination of the stiffness and viscosity of the spring can develop a cloth-like deformational behavior.

    

Mass-spring system (click on the flag to start a demo clip)

    Mesh generation is a process to automatically setup the distributions and connections of particles and springs on a given garment pattern, which may have irregular shapes. In our approach, this procedure is carried out in two steps: the first step is to generate the nodes for the given pattern that represent the mass distribution of the mass-spring system, and the second step is to generate a list of triangles in terms of Delaunay triangulation.

    Wrapping is the 2D-3D transformation that merges the particles on the sewing edges of a pattern with those of the matching pattern, and moves the inner particles of all the sewn patterns towards the body surface. The stiffness coefficients of the tensile and shearing springs can be set purposely to a value much higher than their empirical values. In the wrapping, the gravity, friction, air resistance and other external forces are not considered, and the sewing forces are formed by an adaptive force field distributed on each particle. In each iteration of computing the displacements of the particles, the collisions between the particles and the body surface are detected to prevent surface penetrations. The wrapping ends when all the sewing edges are merged. Wrapping creates an initial status for draping.

    Draping is the further pattern transformation to produce the draping effects by using realistic spring values in the fabric model, and adding all the external forces. Collision detection, and strain control and size stability are the major issues in the draping simulation.

(click on the model to start a demo clip)

6. High-Speed, Real-Time Highway Pavement Distress Inspection
    Since 1997, we have been working on a research project sponsored by the Texas Department of Transportation (TxDOT) to develop a fully automated distress survey system running at real time for image acquisition, distress detection, and data classification. The system, equipped with a line-scan camera and a high-speed frame grabber, is installed in a designated vehicle, and can take up to 44,000 lines or 88 meters pavement surface images per second while it covers full pavement lane (3.66 meters). A multiresolution segmentation algorithm for crack detection was developed to meet the high-speed requirement. The algorithm takes less than 20 milliseconds as running on a 2 GHz Pentium IV processor and can reliably detect a variety of cracking distresses. The system is able to conduct the survey at a vehicle speed from 5 to 112 kmh-1 and report the data to the pavement information management database at a given distance interval. No human interference is required during the entire survey operation. The inspection data from the system have much better repeatability than those from the visual inspections. In addition, the system does not need additional DSP hardware or processors, making the system more cost efficient.