Review and Comparison of High-Dynamic Range Three-Dimensional Shape Measurement Techniques

In the last decade, a significant number of techniques for three-dimensional (3D) shape measurement have been proposed. There are a large number of measurement demands for metallic workpieces with shiny surfaces in industrial applications; however, such shiny surfaces cannot be directly measured using the conventional structured light method. Therefore, various techniques have been investigated to solve this problem over the last few years. Some reviews summarize the different 3D imaging techniques; however, no comprehensive review exists that provides an insight into high-dynamic range (HDR) 3D shape measurement techniques used for shiny surfaces. We present a survey of recent HDR techniques for the digitization of shiny surfaces and classify and discuss the advantages and drawbacks of different techniques with respect to each other

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

Anomaly detection & object classification using multi-spectral LiDAR and sonar

In this thesis, we present the theory of high-dimensional signal approximation of multifrequency signals. We also present both linear and non-linear compressive sensing (CS) algorithms that generate encoded representations of time-correlated single photon counting (TCSPC) light detection and ranging (LiDAR) data, side-scan sonar (SSS) and synthetic aperture sonar (SAS). The main contributions of this thesis are summarised as follows: 1. Research is carried out studying full-waveform (FW) LiDARs, in particular, the TCSPC data, capture, storage and processing. 2. FW-LiDARs are capable of capturing large quantities of photon-counting data in real-time. However, the real-time processing of the raw LiDAR waveforms hasn’t been widely exploited. This thesis answers some of the fundamental questions: • can semantic information be extracted and encoded from raw multi-spectral FW-LiDAR signals? • can these encoded representations then be used for object segmentation and classification? 3. Research is carried out into signal approximation and compressive sensing techniques, its limitations and the application domains. 4. Research is also carried out in 3D point cloud processing, combining geometric features with material spectra (spectral-depth representation), for object segmentation and classification. 5. Extensive experiments have been carried out with publicly available datasets, e.g. the Washington RGB Image and Depth (RGB-D) dataset [108], YaleB face dataset1 [110], real-world multi-frequency aerial laser scans (ALS)2 and an underwater multifrequency (16 wavelengths) TCSPC dataset collected using custom-build targets especially for this thesis. 6. The multi-spectral measurements were made underwater on targets with different shapes and materials. A novel spectral-depth representation is presented with strong discrimination characteristics on target signatures. Several custom-made and realistically scaled exemplars with known and unknown targets have been investigated using a multi-spectral single photon counting LiDAR system. 7. In this work, we also present a new approach to peak modelling and classification for waveform enabled LiDAR systems. Not all existing approaches perform peak modelling and classification simultaneously in real-time. This was tested on both simulated waveform enabled LiDAR data and real ALS data2 . This PhD also led to an industrial secondment at Carbomap, Edinburgh, where some of the waveform modelling algorithms were implemented in C++ and CUDA for Nvidia TX1 boards for real-time performance. 1http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ 2This dataset was captured in collaboration with Carbomap Ltd. Edinburgh, UK. The data was collected during one of the trials in Austria using commercial-off-the-shelf (COTS) sensors

Proxies: the cultural work of standing in

How those with the power to design technology, in the very moment of design, are allowed to imagine who is included—and who is excluded—in the future. The open access edition of this book was made possible by generous funding from Arcadia – a charitable fund of Lisbet Rausing and Peter Baldwin. Our world is built on an array of standards we are compelled to share. In Proxies, Dylan Mulvin examines how we arrive at those standards, asking, “To whom and to what do we delegate the power to stand in for the world?” Mulvin shows how those with the power to design technology, in the very moment of design, are allowed to imagine who is included—and who is excluded—in the future. For designers of technology, some bits of the world end up standing in for other bits, standards with which they build and calibrate. These “proxies” carry specific values, even as they disappear from view. Mulvin explores the ways technologies, standards, and infrastructures inescapably reflect the cultural milieus of their bureaucratic homes. Drawing on archival research, he investigates some of the basic building-blocks of our shared infrastructures. He tells the history of technology through the labor and communal practices of, among others, the people who clean kilograms to make the metric system run, the women who pose as test images, and the actors who embody disease and disability for medical students. Each case maps the ways standards and infrastructure rely on prototypical ideas of whiteness, able-bodiedness, and purity to control and contain the messiness of reality. Standards and infrastructures, Mulvin argues, shape and distort the possibilities of representation, the meaning of difference, and the levers of change and social justice

Natural Parameterization

The objective of this project has been to develop an approach for imitating physical objects with an underlying stochastic variation. The key assumption is that a set of “natural parameters” can be extracted by a new subdivision algorithm so they reflect what is called the object’s “geometric DNA”. A case study on one hundred wheat grain crosssections (Triticum aestivum) showed that it was possible to extract thirty-six such parameters and to reuse them for Monte Carlo simulation of “new” stochastic phantoms which possessthe same stochastic behavior as the “original” cross-sections

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition