Yarn hairiness is one of the essential parameters for assessing yarn quality. Most photoelectric yarn measurement systems are likely to underestimate hairiness because hairy fibers on a yarn surface are often projected or occluded in these two-dimensional (2D) systems. This paper presents a three-dimension (3D) test method for hairiness measurement using a multi-perspective imaging system. The system was developed to reconstruct a 3D yarn model for tracing the actual length of hairy fibers on a yarn surface. Five views of a yarn from different perspectives were created by two angled mirrors, and simultaneously captured in one panoramic picture by a camera. A 3D model was built by extracting the yarn silhouettes in the five views separating and transferring the silhouettes into a common coordinate system. From the 3D model, curved hair fibers were traced spatially so that projection and occlusion occurred in the current systems could be avoided. In the experiment, the proposed method was compared with two commercial instruments, i.e., the Uster Tester and Zweigle Tester. It is demonstrated that the length distribution of hairy fibers measured from the 3D model showed an exponential growth when the fiber length is sorted from shortest to longest. The H-value and S3 value measured by the multi-perspective method are larger than those obtained from Uster Tester and Zweigle Tester, respectively. The H-values of the proposed method have high consistency with those of Uster Tester (r = 0.992). It is indicated that the proposed method allows more accurate and comprehensive hairiness index measurement.
Yarn hairiness is one of the essential parameters for assessing yarn quality. Most of the currently used yarn measurement systems are based on two-dimensional (2-D) photoelectric measurements, which are likely to underestimate levels of yarn hairiness because hairy fibers on a yarn surface are often projected or occluded in these 2-D systems. A three-dimensional (3-D) test method for hairiness measurement using a multiperspective imaging system is presented. The system was developed to reconstruct a 3-D yarn model for tracing the actual length of hairy fibers on a yarn surface. Five views of a yarn from different perspectives were created by two angled mirrors and simultaneously captured in one panoramic picture by a camera. A 3-D model was built by extracting the yarn silhouettes in the five views and transferring the silhouettes into a common coordinate system. From the 3-D model, curved hair fibers were traced spatially so that projection and occlusion occurring in the current systems could be avoided. In the experiment, the proposed method was compared with two commercial instruments, i.e., the Uster Tester and Zweigle Tester. It is demonstrated that the length distribution of hairy fibers measured from the 3-D model showed an exponential growth when the fiber length is sorted from shortest to longest. The hairiness measurements, such as H-value, measured by the multiperspective method were highly consistent with those of Uster Tester (r=0.992) but had larger values than those obtained from Uster Tester and Zweigle Tester, proving that the proposed method corrected underestimated hairiness measurements in the commercial systems.
Yarn hairiness has been an important indication of yarn quality that affects weaving production and fabric appearance. In addition to many dedicated instruments, various image analysis systems have been adopted to measure yarn hairiness for potential values of high accuracy and low cost. However, there is a common problem in acquiring yarn images; that is, hairy fibers protruding beyond the depth of field of the imaging system cannot be fully focused. Fuzzy fibers in the image inevitably introduce errors to the hairiness data. This paper presents a project that attempts to solve the off-focus problem of hairy fibers by applying a new imaging scheme—multifocus image fusion. This new scheme uses compensatory information in sequential images taken at the same position but different depths to construct a new image whose pixels have the highest sharpness among the sequential images. The fused image possesses clearer fiber edges, permitting more complete fiber segmentation and tracing. In the experiments, we used six yarns of different fiber contents and spinning methods to compare the hairiness measurements from the fused images with those from unfused images and from the Uster tester.
We present an innovative calibration method for line-scan cameras to estimate the intrinsic parameters. The calibration involves using a stationary planar pattern that consists of repeated vertical and slanted lines, and constructing a two-dimensional (2-D) calibration framework with one-dimensional (1-D) data. A feature point reconstruction method is applied to transform the 1-D camera calibration problem into the 2-D scope. Camera parameters are then solved by using a 2-D camera model with constraints unique to 1-D geometry. In our tests over 12 calibrations with images of 2048×2048 pixels , the average of the reprojection errors is 0.46 pixels. As opposed to other line-scan camera calibration techniques, this method does not require the camera to progressively scan a pattern, thus eliminating the need for additional mechanical devices to assist the calibration. This method does not need a three-dimensional pattern as a calibration target, either. The stationary planar target makes the calibration more suitable for an application that has to be done in a nonlaboratory setting, such as highway pavement inspection.
We introduce a stereovision system and the three-dimensional (3-D) image analysis algorithms for fabric pilling measurement. Based on the depth information available in the 3-D image, the pilling detection process starts from the seed searching at local depth maxima to the region growing around the selected seeds using both depth and distance criteria. After the pilling detection, the density, height, and area of individual pills in the image can be extracted to describe the pilling appearance. According to the multivariate regression analysis on the 3-D images of 30 cotton fabrics treated by the random-tumble and home-laundering machines, the pilling grade is highly correlated with the pilling density (R 2 =0.923 ) but does not consistently change with the pilling height and area. The pilling densities measured from the 3-D images also correlate well with those counted manually from the samples (R 2 =0.985 ).
KEYWORDS: 3D image processing, Imaging systems, Cameras, 3D modeling, Whole body imaging, 3D metrology, Human subjects, Stereo vision systems, Projection systems, Head
The increasing prevalence of obesity suggests a need to develop a convenient, reliable, and economical tool for assessment of this condition. Three-dimensional (3-D) body surface imaging has emerged as an exciting technology for the estimation of body composition. We present a new 3-D body imaging system, which is designed for enhanced portability, affordability, and functionality. In this system, stereo vision technology is used to satisfy the requirement for a simple hardware setup and fast image acquisition. The portability of the system is created via a two-stand configuration, and the accuracy of body volume measurements is improved by customizing stereo matching and surface reconstruction algorithms that target specific problems in 3-D body imaging. Body measurement functions dedicated to body composition assessment also are developed. The overall performance of the system is evaluated in human subjects by comparison to other conventional anthropometric methods, as well as air displacement plethysmography, for body fat assessment.
Rutting and pothole are the common pavement distress problems that need to be timely inspected and
repaired to ensure ride quality and safe traffic. This paper introduces a real-time, automated inspection system
devoted for detecting these distress features using high-speed transverse scanning. The detection principle is based
on the dynamic generation and characterization of 3D pavement profiles obtained from structured light
measurements. The system implementation mainly involves three tasks: multi-view coplanar calibration, sub-pixel
laser stripe location, and pavement distress recognition. The multi-view coplanar scheme was employed in the
calibration procedure to increase the feature points and to make the points distributed across the field of view of the
camera, which greatly improves the calibration precision. The laser stripe locating method was implemented in four
steps: median filtering, coarse edge detection, fine edge adjusting, stripe curve mending and interpolation by cubic
splines. The pavement distress recognition algorithms include line segment approximation of the profile, searching
for the feature points, and parameters calculations. The parameter data of a curve segment between two feature
points, such as width, depth and length, were used to differentiate rutting, pothole, and pothole under different
constraints. The preliminary experiment results show that the system is capable of locating these pavement
distresses, and meets the needs for real-time and accurate pavement inspection.
KEYWORDS: 3D metrology, 3D image processing, Imaging systems, Cameras, Whole body imaging, 3D modeling, Stereo vision systems, Human subjects, Head, 3D acquisition
The increasing prevalence of obesity suggests a need to develop a convenient, reliable and economical tool for
assessment of this condition. Three-dimensional (3D) body surface imaging has emerged as an exciting technology
for estimation of body composition. This paper presents a new 3D body imaging system, which was designed for
enhanced portability, affordability, and functionality. In this system, stereo vision technology was used to satisfy the
requirements for a simple hardware setup and fast image acquisitions. The portability of the system was created via
a two-stand configuration, and the accuracy of body volume measurements was improved by customizing stereo
matching and surface reconstruction algorithms that target specific problems in 3D body imaging. Body
measurement functions dedicated to body composition assessment also were developed. The overall performance of
the system was evaluated in human subjects by comparison to other conventional anthropometric methods, as well
as air displacement plethysmography, for body fat assessment.
This paper introduces a high-resolution 3-D scanning system for objective evaluation of fabric fuzziness using laser range-sensing technology. This system consists of a laser range sensor, a 2-D mechanical stage, and a computer with dedicated software. The paper covers the process of surface digitization from 3-D scanning, image-processing techniques for surface feature description, and methods for characterization of fabric fuzziness. It also reports a preliminary test on a set of fabrics that were treated in different laundering cycles to gain different levels of fuzziness. The test demonstrates that this system has great potential for discriminating among fabric fuzziness levels in a quantitative and reliable manner.
We present an image processing algorithm customized for high-speed, real-time inspection of pavement cracking. In the algorithm, a pavement image is divided into grid cells of 8×8 pixels, and each cell is classified as a noncrack or crack cell using the grayscale information of the border pixels. Whether a crack cell can be regarded as a basic element (or seed) depends on its contrast to the neighboring cells. A number of crack seeds can be called a crack cluster if they fall on a linear string. A crack cluster corresponds to a dark strip in the original image that may or may not be a section of a real crack. Additional conditions to verify a crack cluster include the requirements in the contrast, width, and length of the strip. If verified crack clusters are oriented in similar directions, they will be joined to become one crack. Because many operations are performed on crack seeds rather than on the original image, crack detection can be executed simultaneously when the frame grabber is forming a new image, permitting real-time, online pavement surveys. The trial test results show a good repeatability and accuracy when multiple surveys were conducted at different driving conditions.
This paper presents the image-processing algorithm customized for high-speed, real-time inspection of pavement cracking. In the algorithm, a pavement image is divided into grid cells of 8x8 pixels and each cell is classified as a non-crack or crack cell using the grayscale information of the border pixels. Whether a crack cell can be regarded as a basic element (or seed) depends on its contrast to the neighboring cells. A number of crack seeds can be called a crack cluster if they fall on a linear string. A crack cluster corresponds to a dark strip in the original image that may or may not be a section of a real crack. Additional conditions to verify a crack cluster include the requirements in the contrast, width and length of the strip. If verified crack clusters are oriented in similar directions, they will be joined to become one crack. Because many operations are performed on crack seeds rather than on the original image, crack detection can be executed simultaneously when the frame grabber is forming a new image, permitting real-time, online pavement survey. The trial test results show a good repeatability and accuracy when multiple surveys were conducted at different driving conditions.
This paper presents a stereo matching algorithm which combines a dense B-spline representation and an adaptive regularization technique to produce a detailed and stable depth field. We demonstrate that splines may fail in representing some regions in a disparity map due to occlusions. To address this problem, we propose to perform spline representation in object space and directly carry out surface reconstruction from stereo images. The effectiveness of this algorithm has been demonstrated by experimental results on real images.
An automatic body measurement system is essential for apparel mass customization. This paper introduces the development of a body-scanning system using the multi-line triangulation technique, and methods for body size extraction and body modeling. The scanning system can rapidly acquire the surface data of a body, provide accurate body dimensions, many of which are not measurable with conventional methods, and also construct a body form based on the scanned data as a digital model of the body for 3D garment design and for virtual try-on of a designed garment.
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