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.
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.
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.
A multi-channel polarization-sensitive Mueller-matrix optical coherence tomography (OCT) was built with single-mode optical fibers in both the sample and reference arms. A new rigorous algorithm was developed to eliminate dynamically the polarization distortions caused by the sampling fiber and consequently retrieve the calibrated polarization properties of the sample. The roundtrip Jones matrix of the sampling fiber used in the algorithm was acquired from the reflecting surface of the sample for each depth scan (A scan). Both this new algorithm and the algorithm used in previous fiber-based polarization-sensitive OCT (PS-OCT) were tested with simulated data, which shows that the only parameter that can be correctly retrieved by the previous algorithm is the phase retardation. The skin of a rat was imaged with this fiber-based system.
An approach for corresponding points matching based on multiresolution wavelet transform and phase matching is
presented in this paper. In binocular vision, disparity is an important cue to reconstruct the 3D structure of the scene from two or more images. Disparity for a stereo pair can be calculated by solving the stereo correspondence problem in image matching. For phase matching, the difference between the complex phases at corresponding points is used to find
binocular disparity. In this approach, we construct a complex-valued wavelet kernel, which satisfied the requirement to the filter used in phase matching, through the Hilbert transform. The multiresolution analysis is combined with this kernel to get multiresolution phases of local spatial frequency. Then the phase matching is done based on these multiresolution phases. According to the relationship between image features and phase congruency, and also the relationship between phase congruency and local energy, we modified the iteration approaches in phase matching so as to give high confidence on image features.