Laser Line Image Research Articles

Image-Based Tool Characterization and DEM Simulation of Abrasive Brushing Processes

Brushing with bonded abrasives is a finishing process used for deburring, edge rounding, and roughness reduction. However, due to the complex motion, chipping, and wear behavior of abrasive filaments, industrial brushing processes have historically relied on empirical knowledge. To gain a better understanding of filament interactions, a physical model based on the discrete element method was developed to simulate process forces and contact areas. Filament patterns of round brushes were determined through the use of laser line triangulation and image processing. These filament patterns showed interlocked filaments and yielded more accurate results when used in brushing simulations than the oversimplified square patterns, which were used in previous research. Simulation confirms the occurrence of filament interactions, distinguishes between sweeping and striking filament motions, and reveals dynamic behavior at high brushing velocities that may increase undesirable tool wear.

Measuring vertical displacement using laser lines and cameras

Measuring displacements in model tests typically involves contact-based sensors such as linear potentiometers, where contact between two moving parts occurs at the sensing point. The sensor's finite mass, the limited stiffness of the beams and the clamping mechanism, and the slippage and hinging of the sensor body could affect the object's response and lead to measurement errors. Also, the physical mounting rack required to hold these sensors often obstructs the view and makes significant areas unavailable for conducting some other essential investigations. The advancement in high-speed, high-resolution and reasonably priced rugged cameras makes it feasible to obtain better displacement measurements by image analysis. This paper introduces a non-contact method that works by video recording the projection of laser lines on a test object to measure static and dynamic vertical displacements. The technique produces a continuous settlement distribution along the laser line passing through multiple objects of interest. This paper presents the theory for converting laser line images to displacements. The new method's validity is demonstrated by comparing the results from other measurement techniques: hand measurements, linear potentiometers and three-dimensional stereophotogrammetry.

Line‐laser‐based visual measurement for pavement 3D rut depth in driving state

This Letter develops a line-laser-based real-time visual measurement method to inspect pavement rut depths. The detection vehicle equipped with line-laser projector continuously sends, in its normal driving state, laser light onto the pavement, which is captured by a high-resolution camera on the vehicle, thus a sequence of pavement rut laser line images are created. The pavement rut laser line image is transformed to the convexity index image, in which the peak points of the pavement rut laser line are rapidly sought to obtain the rut laser curve with a single-pixel width. The relationship between pavement rut depth and the visual deformation value of measurement points on the rut laser curve is determined by the calibration blocks, and then the pavement three-dimensional (3D) rut depths can be automatically measured, 3D rut depth curves and 3D rut surface can be reconstructed. Some experimental results verify the effectiveness of the proposed method.

A Flexible and Robust Threshold Selection Method

Despite the great prosperity and fast development of image segmentation technology, threshold selection method is still the best choice in many practical applications. The state-of-the-art threshold selection methods perform poorly in segmenting many images with different modalities, such as the magnetic resonance images, cell images, and laser line images. Thus, it is desirable to come up with a more robust method that could segment images with different modalities with the optimum accuracy. To this end, the method should be flexible and its parameters should be adjustable for different types of images. In this paper, we propose to compute the threshold based on the slope difference distribution, which is computed from the image histogram with adjustable parameters. First, the pixels are clustered based on the peaks of the slope difference distribution into different pixel classes. Second, the threshold is selected based on the valleys of the slope difference distribution to separate the pixel classes. The robustness of this threshold selection method relies on the adjustable parameters that could be calibrated to achieve the optimum segmentation accuracy for each specific type of images. The proposed threshold selection method is tested on both the synthesized images and the real images. Experimental results show that the proposed method outperforms the state-of-the-art methods as a whole.

Microscope self-calibration based on micro laser line imaging and soft computing algorithms

A technique to perform microscope self-calibration via micro laser line and soft computing algorithms is presented. In this technique, the microscope vision parameters are computed by means of soft computing algorithms based on laser line projection. To implement the self-calibration, a microscope vision system is constructed by means of a CCD camera and a 38 µm laser line. From this arrangement, the microscope vision parameters are represented via Bezier approximation networks, which are accomplished through the laser line position. In this procedure, a genetic algorithm determines the microscope vision parameters by means of laser line imaging. Also, the approximation networks compute the three-dimensional vision by means of the laser line position. Additionally, the soft computing algorithms re-calibrate the vision parameters when the microscope vision system is modified during the vision task. The proposed self-calibration improves accuracy of the traditional microscope calibration, which is accomplished via external references to the microscope system. The capability of the self-calibration based on soft computing algorithms is determined by means of the calibration accuracy and the micro-scale measurement error. This contribution is corroborated by an evaluation based on the accuracy of the traditional microscope calibration.

Fast Obstacle Distance Estimation using Laser Line Imaging Technique for Smart Wheelchair

This paper presents an approach of obstacle distance estimation for smart wheelchair. A smart wheelchair was equipped with a camera and a laser line. The camera was used to capture an image from the environment in order to sense the pathway condition. The laser line was used in combination with camera to recognize an obstacle in the pathway based on the shape of laser line image in certain angle. A blob method detection was then applied on the laser line image to separate and recognize the pattern of the detected obstacles. The laser line projector and camera which was mounted in fixed-certain position ensured a fixed relation between blobs-gap and obstacle-to-wheelchair distance. A simple linear regression from 16 obtained data was used to respresent this relation as the estimated obstacle distance. As a result, the average error between the estimation and the actual distance was 1.25 cm from 7 data testing experiments. Therefore, the experiment results show that the proposed method was able to estimate the distance between wheelchair and the obstacle.

Fast Obstacle Distance Estimation using Laser Line Imaging Technique for Smart Wheelchair

This paper presents an approach of obstacle distance estimation for smart wheelchair. A smart wheelchair was equipped with a camera and a laser line. The camera was used to capture an image from the environment in order to sense the pathway condition. The laser line was used in combination with camera to recognize an obstacle in the pathway based on the shape of laser line image in certain angle. A blob method detection was then applied on the laser line image to separate and recognize the pattern of the detected obstacles. The laser line projector and camera which was mounted in fixed-certain position ensured a fixed relation between blobs-gap and obstacle-to-wheelchair distance. A simple linear regression from 16 obtained data was used to respresent this relation as the estimated obstacle distance. As a result, the average error between the estimation and the actual distance was 1.25 cm from 7 data testing experiments. Therefore, the experiment results show that the proposed method was able to estimate the distance between wheelchair and the obstacle.

ChemInform Abstract: Developments in Spontaneous and Coherent Raman Scattering Microscopic Imaging for Biomedical Applications

AbstractReview: 246 refs.

Binocular self-calibration performed via adaptive genetic algorithm based on laser line imaging

An accurate technique to perform binocular self-calibration by means of an adaptive genetic algorithm based on a laser line is presented. In this calibration, the genetic algorithm computes the vision parameters through simulated binary crossover (SBX). To carry it out, the genetic algorithm constructs an objective function from the binocular geometry of the laser line projection. Then, the SBX minimizes the objective function via chromosomes recombination. In this algorithm, the adaptive procedure determines the search space via line position to obtain the minimum convergence. Thus, the chromosomes of vision parameters provide the minimization. The approach of the proposed adaptive genetic algorithm is to calibrate and recalibrate the binocular setup without references and physical measurements. This procedure leads to improve the traditional genetic algorithms, which calibrate the vision parameters by means of references and an unknown search space. It is because the proposed adaptive algorithm avoids errors produced by the missing of references. Additionally, the three-dimensional vision is carried out based on the laser line position and vision parameters. The contribution of the proposed algorithm is corroborated by an evaluation of accuracy of binocular calibration, which is performed via traditional genetic algorithms.

Self-calibration of vision parameters via genetic algorithms with simulated binary crossover and laser line projection

A self-calibration technique based on genetic algorithms (GAs) with simulated binary crossover (SBX) and laser line imaging is presented. In this technique, the GA determines the vision parameters based on perspective projection geometry. The GA is constructed by means of an objective function, which is deduced from the equations of the laser line projection. To minimize the objective function, the GA performs a recombination of chromosomes through the SBX. This procedure provides the vision parameters, which are represented as chromosomes. The approach of the proposed GA is to achieve calibration and recalibration without external references and physical measurements. Thus, limitations caused by the missing of references are overcome to make self-calibration and three-dimensional (3-D) vision. Therefore, the proposed technique improves the self-calibration obtained by GAs with references. Additionally, 3-D vision is carried out via laser line position and vision parameters. The contribution of the proposed method is elucidated based on the accuracy of the self-calibration, which is performed with GAs.

Context Transformations of Range Images Using Directional Data Back Fill Algorithm

This paper discusses the subject of filtering range images constructed using vision systems. Construction of a range image using the laser triangulation method LTM is burdened by noise resulting from the specificity of the adopted method. This method uses structural illumination, usually in the form of a laser line from the object illumination. The image of the object is captured by a camera, whereupon the laser line image is transformed into height profiles, which are then used to construct a range image of the object. Yet, such images fail to describe the entire surface of the object, due to occlusion. Occlusion of image fragments occurs in two forms. The first one stems from the impossibility to fully illuminate all surfaces of the object, due to occlusion of the laser lines. The second form occurs when the surface is illuminated properly, but the image of this surface cannot be captured by the camera. In both cases, there is an occlusion which usually results from the shape of the object. This paper describes the method of backfilling the data in undefined areas of range images caused to the phenomenon of occlusion. It suggests a method of defining the direction of data backfilling, identification of undefined areas, as well as methods of calculating and backfilling height differences at undefined spots. The purpose of these transformations is to prepare the image for measurement and control tasks. Select examples are presented with the results of image transformation using the developed data backfilling method.

Effect of experimental parameters and image noise on the error levels in simultaneous velocity and temperature measurements using molecular tagging velocimetry/thermometry (MTV/T)

In this work, the effect of experimental parameters on the error levels associated with simultaneous measurement of displacement and temperature using Molecular Tagging Velocimetry/Thermometry (MTV/T) was quantified via simulated images. Images were simulated using Gaussian profile laser lines. Noise was added to the images using a uniform random distribution and a Gaussian random distribution to simulate electronic noise and shot noise, respectively. The results showed that the error levels in the displacement and temperature measurements were inversely related for most experimental parameters including the laser line thickness, fluid temperature and image delay times. It is concluded that the dynamic range of the technique depends on the flow speeds and temperatures and must be determined for each experiment individually. Error levels, for 95% confidence, were found to be better than 0.3°C for temperature and 0.2 pixels for displacement for typical real-world experimental parameters.

New laryngoscope for quantitative high-speed imaging of human vocal folds vibration in the horizontal and vertical direction

We report the design of a novel laser line-triangulation laryngoscope for the quantitative visualization of the three-dimensional movements of human vocal folds during phonation. This is the first successful in vivo recording of the three-dimensional movements of human vocal folds in absolute values. Triangulation images of the vocal folds are recorded at the rate of 4000 fps with a resolution of 256x256 pixels. A special image-processing algorithm is developed to precisely follow the subpixel movements of the laser line image. Vibration profiles in both horizontal and vertical directions are calibrated and measured in absolute SI units with a resolution of +/-50 microm. We also present a movie showing the vocal folds dynamics in vertical cross section.

Effect of aberrations and scatter on image resolution assessed by adaptive optics retinal section imaging

The effect of increased high-order wavefront aberrations on image resolution was investigated, and the performance of adaptive optics (AO) for correcting wavefront error in the presence of increased light scatter was assessed in a model eye. An AO section imaging system provided an oblique view of a model retina and incorporated a wavefront sensor and deformable mirror for measurement and compensation of wavefront aberrations. Image resolution was quantified by the width of a Lorentzian curve fitted to a laser line image. Wavefront aberrations were significantly reduced with AO, resulting in improvement of image resolution. In the model eye, image resolution was degraded with increased high-order wavefront aberrations (horizontal coma and spherical) and improved with AO correction of wavefront error in the presence of increased light scatter. The findings of the current study suggest that AO imaging systems can potentially improve image resolution in aging eyes with increased aberrations and scatter.

Reconstructing a textured CAD model of an urban environment using vehicle-borne laser range scanners and line cameras

Abstract. In this paper, a novel method is presented for generating a textured CAD model of an outdoor urban environment using a vehicle-borne sensor system. In data measurement, three single-row laser range scanners and six line cameras are mounted on a measurement vehicle, which has been equipped with a GPS/INS/Odometer-based navigation system. Laser range and line images are measured as the vehicle moves forward. They are synchronized with the navigation system so they can be geo-referenced to a world coordinate system. Generation of the CAD model is conducted in two steps. A geometric model is first generated using the geo-referenced laser range data, where urban features, such as buildings, ground surfaces, and trees are extracted in a hierarchical way. Different urban features are represented using different geometric primitives, such as a planar face, a triangulated irregular network (TIN), and a triangle. The texture of the urban features is generated by projecting and resampling line images onto the geometric model. An outdoor experiment is conducted, and a textured CAD model of a real urban environment is reconstructed in a full automatic mode.

A machine vision technique for subpixel estimation of three-dimensional profilometry

A quick and efficient technique for a charge-coupled device (CCD)-based profilometric measurement system is proposed and verified by experiment. When a laser stripe is projected onto an object, the deformed laser line image is detected by a CCD camera with subpixel resolution and is automatically transferred to its space position using a least-squares mapping algorithm. Measurements are executed on each separate surface of a three-dimensional object and the multi-surface data are integrated through a process of coordinate transformation. Large arrays of data points are acquired on each surface by means of a scanning mechanism. Because the measuring data from each laser stripe position can provide two-dimensional information concerning the surface contour, the laser stripe and the CCD camera are integrated into a measurement system for obtaining the entire three-dimensional information. Theoretical analysis shows that this technique can determine the three-dimensional profile of an object with an accuracy below 0.2 mm.

Noncontact profilometric measurement of large‐form parts

A quick and efficient technique for automatic profilometric measurement of a large-form part is proposed and verified by experi- ment. When a laser stripe is projected onto an object, the deformed laser line image is detected by a solid-state array camera and automatically transferred to its space position using a least-squares mapping algo- rithm. Measurements can be executed on any surface of a 3-D object except the base. Large arrays of data points are acquired on each sur- face by means of a scanning mechanism. Since the measuring data from each laser stripe position can provide 2-D information concerning the surface contour, the laser stripe and the solid-state array camera are integrated into a measurement system for obtaining the entire 3-D infor- mation. Experimental results show that this technique can determine the 3-D profile of an object with an accuracy of 0.5 mm. © 1996 Society of Photo-Optical Instrumentation Engineers.

360-deg profile noncontact measurement using a neural network

A new approach to automatic 3-D shape measurement is presented and verified by experiments. This approach, based on neural network theory, can automatically and accurately obtain the profile of diffuse 3-D objects by using a projected laser stripe. When the laser stripe is projected on an object, the line image of the laser light is grasped by a CCD camera. Using neural network theory, a relationship between the laser stripe image in the CCD camera and the related absolute position in space can be established. Thus the spatial coordinates of a measured line image in a CCD camera can be obtained according to the output value of the neural network. By processing a series of laser line images from the discrete angular positions of an object, a complete 3-D profile can be reconstructed. Theoretical analysis and experimental systems are presented. Experimental results show that this approach can determine the 360-deg profile of an object with an accuracy of 0.4 mm.

Encoder and strain gauge using laser speckle

Applications of the electronic correlation of speckle pattern to the measurement of surface displacement and strain are described. Basic principles including speckle displacement properties and experimental results using a laser diode and line image sensors are presented.

Laser Based Inspection And Path CorrectionSystem For Automatic Sewing Apparatus

This paper describes a laser triangulation inspection system which has been integrated into an automatic stitching machine used to stitch overlapping components. The inspection task locates defects that can occur prior to and during the stitching process so that the stitch path can be modified accordingly. The three-dimensional shape of the surface and component edge positions are extracted through the analysis of the laser line images captured by a high frame rate camera. Different stages of the data collection aspect of the inspection process are presented. These include calibration and laser line image processing techniques. The data modelling of the extracted edge points is based on least-square polynomial regression and parametric curve approximation. The obtained data model is used for stitch path planning. These techniques were tested on an automatic stitching machine.