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 8 x 8 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, which permits a real-time, online pavement survey. The trial test results show good repeatability and accuracy when multiple surveys were conducted in different driving conditions. 17. Key Words pavement cracking distress, automatic inspections, real-time inspection, image-processing algorithm, crack seeds, crack cluster, crack detection 18. Distribution Statement No restrictions. This document is available to the public through the National Technical Information Service, Springfield, Virginia 22161; www.ntis.gov. 19. Security Classif. (of report) Unclassified 20. Security Classif.

Cross-sectional analysis of cotton fibers provides direct, accurate measurements of fiber fineness and maturity, which are often regarded as the reference data for validating or calibrating other indirect measurements of these important cotton properties. Despite the importance, cross-sectional methods of using image analysis have not been broadly applied to cotton quality evaluations because of the tedious procedures for both preparing cotton samples and processing cross-sectional images. This paper illustrates image processing procedures dedicated to cotton cross-sectional analysis for the purpose of increasing the efficiency and accuracy of fiber separation and feature extraction. These procedures greatly improve the automation of processing cotton cross-sectional images and increase the number of analyzable fibers per image. The cross-sectional data of cotton fibers also have good correlations with longitudinal data and data from the Advanced Fiber Information Systems.A cotton cross section contains measurable information directly related to the maturity of the fiber. Crosssectional measurements of cotton maturity may be used as a reference when other methods need to be calibrated.Much research has been conducted with image analysis technology to measure cotton maturity and other parameters from fiber cross sections [4][5][6][11][12][13][14][15]. The success of a cross-sectional method using image analysis largely relies on two techniques: fiber cross-sectioning and image segmentation. Cross-sectioning is the most import step in obtaining analyzable images of fibers, and grinding and cutting are the two general methods for fiber cross-sectioning. In grinding, a bundle of fibers embedded in a polymer resin and hardener mixture is hardened, ground, then polished, and the surface containing the fiber cross sections is imaged on a microscope by reHected light [9]. There are many different ways of cutting a thin slice of fibers perpendicular to the long axes [ 1, 3]. A quick embedding method specifically for cotton fibers was established by researchers at the USDA Southern Regional Research Center (SRRC) [3]. A bundle of fibers is embedded in a methacrylate medium, polymerized in a uv reactor, and cut into 1-3 tkm slices with a microtome. This sectioning method greatly improves the separability and contrast of individual fibers in the image captured by transmitted light. ' Image segmentation is a computational process that separates cotton cross sections from the image background and from one another. Segmentation results directly influence the efficiency and accuracy of crosssectional measurements. Due to variations in the crosssectional shape and thickness of the sliced samples. fibers in different regions may exhibit different levels of contrast and focus in an image. There are always cross sections that contact or overlap others in the image. Some appear to be damaged due to scratching of the cutting knife. Cotton cross sections can have convex or concave boundaries, and hollow or solid cores, making many p...

Pavement distortions, such as rutting and shoving, are the common pavement distress problems that need to be inspected and repaired in a timely manner to ensure ride quality and traffic safety. This paper introduces a real-time, low-cost inspection system devoted to detecting these distress features using high-speed 3D transverse scanning techniques. The detection principle is the dynamic generation and characterization of the 3D pavement profile based on structured light triangulation. To improve the accuracy of the system, a multi-view coplanar scheme is employed in the calibration procedure so that more feature points can be used and distributed across the field of view of the camera. A sub-pixel line extraction method is applied for the laser stripe location, which includes filtering, edge detection and spline interpolation. The pavement transverse profile is then generated from the laser stripe curve and approximated by line segments. The second-order derivatives of the segment endpoints are used to identify the feature points of possible distortions. The system can output the real-time measurements and 3D visualization of rutting and shoving distress in a scanned pavement.

Loom malfunctions are the main cause of faulty fabric production. A fabric inspection system is a specialized computer vision system used to detect fabric defects for quality assurance. In this paper, a deep-learning algorithm was developed for an on-loom fabric defect inspection system by combining the techniques of image pre-processing, fabric motif determination, candidate defect map generation, and convolutional neural networks (CNNs). A novel pairwise-potential activation layer was introduced to a CNN, leading to high accuracy of defect segmentation on fabrics with intricate features and imbalanced dataset. The average precision and recall of detecting defects in the existing images reached, respectively, over 90% and 80% at the pixel level and the accuracy on counting the number of defects from a publicly available dataset exceeded 98%.

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Purpose -This paper presents methods and algorithms to automatically segment and measure the human body. Design/methodology/approach -In the segmentation procedure, two different methods are designed to find the crotch point for the situation of non-contacted thigh and contacted thigh, respectively. Three different methods: minimum distance algorithm, minimum inclination angle algorithm, and directional neighbor identification algorithm are introduced to search the branching points or triangle. In the body measurement procedure, a pre-sorted circling method is designed for circumference measurement, and the basic principle of landmark acquisition has been discussed. These techniques are validated via testing over different type of scanned model. Findings -The results of automatic segmentation and body measurement have verified that our methods are efficient and versatile in processing different type of scanned body.Research limitations/implications -The accurate and automatic locating of wrist, ankle and knees contour can be more difficult than it appears to be. Practical implications -The main usage of scanned body in our research is for 3D garment try-on. Originality/value -This paper introduces the methods for crotch identification, and the methods including minimum distance algorithm, minimum inclination angle algorithm, and directional neighbor identification algorithm for human body segmentation. It also explains the fundamental measuring techniques, and outlines the results of using these techniques in segmentation and measurement.

. Quantitative characterization of fiber cross sections has attracted considerable in terlst, since cross-sectional size and shape have an important impact on the physical and mechanical properties of fibers, as well as the performance of end-use products. We present one application of automated measurement using image processing tech niques that extract basic shape features from fiber cross sections. Cross-sectional shapes are characterized with the aid of geometric and Fourier descriptors. Geometric de scriptors measure attributes such as area, roundness, and ellipticity. Fourier descriptors are derived from the Fourier series for the cumulative angular function of the cross- sectional boundary and are used to characterize shape complexity and other geometric attributes. Shape reconstruction based on Fourier coefficients is also discussed. We present the results of shape analysis for a wide variety of fiber types.

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