A New Sensor System for Accurate 3D Surface Measurements and Modeling of Underwater Objects

Featured Application A potential application of the work is the underwater 3D inspection of industrial structures, such as oil and gas pipelines, offshore wind turbine foundations, or anchor chains. Abstract A new underwater 3D scanning device based on structured illumination and designed for continuous capture of object data in motion for deep sea inspection applications is introduced. The sensor permanently captures 3D data of the inspected surface and generates a 3D surface model in real time. Sensor velocities up to 0.7 m/s are directly compensated while capturing camera images for the 3D reconstruction pipeline. The accuracy results of static measurements of special specimens in a water basin with clear water show the high accuracy potential of the scanner in the sub-millimeter range. Measurement examples with a moving sensor show the significance of the proposed motion compensation and the ability to generate a 3D model by merging individual scans. Future application tests in offshore environments will show the practical potential of the sensor for the desired inspection tasks

Underwater 3D measurements with advanced camera modelling

A novel concept of camera modelling for underwater 3D measurements based on stereo camera utilisation is introduced. The geometrical description of the ray course subject to refraction in underwater cameras is presented under assumption of conditions, which are typically satisfied or can be achieved approximately. Possibilities of simplification are shown, which allow an approximation of the ray course by classical pinhole modelling. It is shown how the expected measurement errors can be estimated, as well as its influence on the expected 3D measurement result. Final processing of the 3D measurement data according to the requirements regarding accuracy is performed using several kinds of refinement. For example, calibration parameters can be refined, or systematic errors can be decreased by subsequent compensation by suitable error correction functions. Experimental data of simulations and real measurements obtained by two different underwater 3D scanners are presented and discussed. If inverse image magnification is larger than about one hundred, remaining errors caused by refraction effects can be usually neglected and the classical pinhole model can be used for stereo camera-based underwater 3D measurement systems

Equine body weight estimation using three-dimensional images

Includes bibliographical references.2015 Summer.Accurately estimating the body weight (BW) of a horse is important in order to make appropriate management and treatment decisions. Most field equine veterinarians and experienced equine people, however, visually estimate BW because large animal scales are impractical for field use due to the weight (>80 kg), size (length >200 cm), and cost (>$1,000). There are some alternative BW estimation methods such as a weight tape or BW estimation using a combination of heart girth and body length measurements. These methods, however, have 5 - 15% or even higher margin of error. According to human studies, there is a high correlation between BW and body volume (BV). Correlation coefficient (R) between these two variables is 0.996-0.998. Our study was designed to develop methods to estimate the BW of horses by using 3D image based BV measurement. 3D imaging technology allows easy and accurate measurement of diverse indices of an object, including the volume. Recent development of Structure-light 3D scanning technology allows 3D scanning of an object as large as 3 by 3 square meter in a short time. In this study, 3D images of 22 and 11 horses were obtained by using 3D scanning (3DScan) and photogrammetry (2Dto3D), respectively. BV and trunk volume (TV) of the horses were measured from the obtained 3D images. Measurements of BW using five conventional methods (visual estimation, 2 weight tapes (Purina, Shell), estimated BW by using heart girth and body length (Carroll’s formula), and a large animal scale) were also conducted, and the data of body condition score (BCS), sex, coat color, and coat type of the horses were collected. Linear regression models to estimate the BW of the horse based on the volume and other independent variables were developed using regression model stepwise selection procedures (P<0.05). Variables selected in 3DScan method were BV, sex, and coat type, and, in 2Dto3D method, BV (TV) was selected. The coefficient of determination of the developed regression models were 0.95 and 0.78-0.82, respectively, and the average percent errors of the predicted BW compared to the true BW of horses were 2.07 % and 2.67 %, respectively. The accuracy of the 3DScan method was significantly more accurate than WT, Carroll’s formual, and VE (P<0.05). 3D image based BW measurement method had higher accuracy and convenience compared to conventional alternative BW measuring methods. Accurate and easy determination of BW using 3D images will allow for regular BW measurement in the field and allow optimal equine health management by equine stakeholders and practitioners. The 3D images obtained in this study were highly detailed. Further graphical analysis of the obtained 3D images will make it possible to use this technology on automatic evaluation of body condition score, equine conformation evaluation, breed registration, and the study of pharmacokinetics and dynamics of newly developed drugs. This research findings may also have utility for application to wild or zoo animals such as the elephant, rhinoceros, or even the tiger where hands on collection of body weight would be challenging

Possible ways to improve the comfort, fit and visual performance of swimming goggles

The primary aim of this thesis was to provide a comprehensive evaluation of the human face so that it can be used to improve the design of swimming goggles with regards to comfort and fit. The secondary aim was concerned with vision and was to identify whether a curved lens could be plausible for use in a performance racing goggle. [Continues.

Three Dimensional Shape Reconstruction with Dual-camera Measurement Fusion

Recently, three-dimensional (3D) shape measurement technologies have been extensively researched in the fields such as computer science and medical engineering. They have been applied in various industries and commercial uses, including robot navigation, reverser engineering and face and gesture recognition. Optical 3D shape measurement is one of the most popular methods, which can be divided into two categories: passive 3D shape reconstruction and active 3D shape imaging. Passive 3D shape measurement techniques use cameras to capture the object with only ambient light. Stereo vision (SV) is one of the typical methods in passive 3D measurement approaches. This method uses two cameras to take photos of the scene from different viewpoints and extract the 3D information by establishing the correspondence between the photos captured. To translate the correspondence to the depth map, epipolar geometry is applied to determine the depth of each pixel. Active 3D shape imaging methods add diverse active light sources to project on the object and use the camera to capture the scene with pre-defined patterns on the object’s surface. The fringe projection profilometry (FPP) is a representative technique among active 3D reconstruction methods. It replaces one of the cameras in stereo vision with a projector, and projects the fringe patterns onto the object before the camera captures it. The depth map can be built via triangulations by analysing the phase difference between patterns distorted by the object’s surface and the original one. Those two mainstream techniques work alone in different scenarios and have various advantages and disadvantages. Active stereo vision (ASV) has excellent dynamic performance, yet its accuracy and spatial resolution are limited. On the other hand, 3D shape measurement methods like FPP have higher accuracy and speed; however, their dynamic performance varies depending on the codification schemes chosen. This thesis presents the research on developing a fusion method that contains both passive and active 3D shape reconstruction algorithms in one system to combine their advantages and reduce the budget of building a high-precision 3D shape measurement system with good dynamic performance. Specifically, in the thesis, we propose a fusion method that combines the epipolar geometry in ASV and triangulations in the FPP system by a specially designed cost function. This way, the information obtained from each system alone is combined, leading to better accuracy. Furthermore, the correlation of object surface is exploited with the autoregressive model to improve the precision of the fusion system. In addition, the expectation maximization framework is employed to address the issue of estimating variables with unknown parameters introduced by AR. Moreover, the fusion cost function derived before is embedded into the EM framework. Next, the message passing algorithm is applied to implement the EM efficiently on large image sizes. A factor graph is derived from fitting the EM approach. To implement belief propagation to solve the problem, it is divided into two sub-graphs: the E-Step factor graph and the M-Step factor graph. Based on two factor graphs, belief propagation is implemented on each of them to estimate the unknown parameters and EM messages. In the last iteration, the height of the object surface can be obtained with the forward and backward messages. Due to the consideration of the object’s surface correlation, the fusion system’s precision is further improved. Simulation and experimental results are presented at last to examine the performance of the proposed system. It is found that the accuracy of the depth map of the fusion method is improved compared to fringe projection profilometry or stereo vision system alone. The limitations of the current study are discussed, and potential future work is presented

3D Shape Measurement of Objects in Motion and Objects with Complex Surfaces

This thesis aims to address the issues caused by high reflective surface and object with motion in the three dimensional (3D) shape measurement based on phase shifting profilometry (PSP). Firstly, the influence of the reflectivity of the object surface on the fringe patterns is analysed. One of the essential factors related to phase precision is modulation index, which has a direct relationship with the surface reflectivity. A comparative study focusing on the modulation index of different materials is presented. The distribution of modulation index for different material samples is statistically analysed, which leads to the conclusion that the modulation index is determined by the diffuse reflectivity. Then the method based on optimized combination of multiple reflected image patterns is proposed to address the saturation issue and improve the accuracy for the reconstruction of object with high reflectivity.A set of phase shifted sinusoidal fringe patterns with different exposure time are projected to the object and then captured by camera. Then a set of masks are generated to select the data for the compositing. Maximalsignal-to-noise ratio combining model is employed to form the composite images pattern. The composite images are then used to phase mapping.Comparing to the method only using the highest intensity of pixels for compositing image, the signal noise ratio (SNR) of composite image is increased due to more efficient use of information carried by the images

Method to Automatically Register Scattered Point Clouds Based on Principal Pose Estimation

Three dimensional (3-D) modeling is important in applications ranging from manufacturing to entertainment. Multiview registration is one of the crucial steps in 3-D model construction. The automatic establishment of correspondences between overlapping views, without any known initial information, is the main challenge in point clouds registration. An automatic registration algorithm is proposed to solve the registration problem of rigid, unordered, scattered point clouds. This approach is especially suitable for registering datasets that are lacking in features or texture. In general, the existing techniques exhibit significant limitations in the registration of these types of point cloud data. The presented method automatically determines the best coarse registration results by exploiting the statistical technique principal component analysis and outputs translation matrices as the initial estimation for fine registration. Then, the translation matrices obtained from coarse registration algorithms are used to update the original point cloud and the optimal translation matrices are solved using an iterative algorithm. Experimental results show that the proposed algorithm is time efficient and accurate, even if the point clouds are partially overlapped and containing large missing regions

High-quality dense stereo vision for whole body imaging and obesity assessment

textThe prevalence of obesity has necessitated developing safe and convenient tools for timely assessing and monitoring this condition for a broad range of population. Three-dimensional (3D) body imaging has become a new mean for obesity assessment. Moreover, it generates body shape information that is meaningful for fitness, ergonomics, and personalized clothing. In the previous work of our lab, we developed a prototype active stereo vision system that demonstrated a potential to fulfill this goal. But the prototype required four computer projectors to cast artificial textures on the body which facilitate the stereo-matching on texture-deficient images (e.g., skin). This decreases the mobility of the system when used to collect a large population data. In addition, the resolution of the generated 3D~images is limited by both cameras and projectors available during the project. The study reported in this dissertation highlights our continued effort in improving the capability of 3Dbody imaging through simplified hardware for passive stereo and advanced computation techniques. The system utilizes high-resolution single-lens reflex (SLR) cameras, which became widely available lately, and is configured in a two-stance design to image the front and back surfaces of a person. A total of eight cameras are used to form four pairs of stereo units. Each unit covers a quarter of the body surface. The stereo units are individually calibrated with a specific pattern to determine cameras' intrinsic and extrinsic parameters for stereo matching. The global orientation and position of each stereo unit within a common world coordinate system is calculated through a 3Dregistration step. The stereo calibration and 3Dregistration procedures do not need to be repeated for a deployed system if the cameras' relative positions have not changed. This property contributes to the portability of the system, and tremendously alleviates the maintenance task. The image acquisition time is around two seconds for a whole-body capture. The system works in an indoor environment with a moderate ambient light. Advanced stereo computation algorithms are developed by taking advantage of high-resolution images and by tackling the ambiguity problem in stereo matching. A multi-scale, coarse-to-fine matching framework is proposed to match large-scale textures at a low resolution and refine the matched results over higher resolutions. This matching strategy reduces the complexity of the computation and avoids ambiguous matching at the native resolution. The pixel-to-pixel stereo matching algorithm follows a classic, four-step strategy which consists of matching cost computation, cost aggregation, disparity computation and disparity refinement. The system performance has been evaluated on mannequins and human subjects in comparison with other measurement methods. It was found that the geometrical measurements from reconstructed 3Dbody models, including body circumferences and whole volume, are highly repeatable and consistent with manual and other instrumental measurements (CV 0.99). The agreement of percent body fat (%BF) estimation on human subjects between stereo and dual-energy X-ray absorptiometry (DEXA) was found to be improved over the previous active stereo system, and the limits of agreement with 95% confidence were reduced by half. Our achieved %BF estimation agreement is among the lowest ones of other comparative studies with commercialized air displacement plethysmography (ADP) and DEXA. In practice, %BF estimation through a two-component model is sensitive to body volume measurement, and the estimation of lung volume could be a source of variation. Protocols for this type of measurement should still be created with an awareness of this factor.Biomedical Engineerin