Facial Geometry Identification through Fuzzy Patterns with RGBD Sensor

Automatic human facial recognition is an important and complicated task; it is necessary to design algorithms capable of recognizing the constant patterns in the face and to use computing resources efficiently. In this paper we present a novel algorithm to recognize the human face in real time; the systems input is the depth and color data from the Microsoft KinectTM device. The algorithm recognizes patterns/shapes on the point cloud topography. The template of the face is based in facial geometry; the forensic theory classifies the human face with respect to constant patterns: cephalometric points, lines, and areas of the face. The topography, relative position, and symmetry are directly related to the craniometric points. The similarity between a point cloud cluster and a pattern description is measured by a fuzzy pattern theory algorithm. The face identification is composed by two phases: the first phase calculates the face pattern hypothesis of the facial points, configures each point shape, the related location in the areas, and lines of the face. Then, in the second phase, the algorithm performs a search on these face point configurations

Efficient Image-Based Localization Using Context

Image-Based Localization (IBL) is the problem of computing the position and orientation of a camera with respect to a geometric representation of the scene. A fundamental building block of IBL is searching the space of a saved 3D representation of the scene for correspondences to a query image. The robustness and accuracy of the IBL approaches in the literature are not objective and quantifiable. First, this thesis presents a detailed description and study of three different 3D modeling packages based on SFM to reconstruct a 3D map of an environment. The packages tested are VSFM, Bundler and PTAM. The objective is to assess the mapping ability of each of the techniques and choose the best one to use for reconstructing the IBL 3D map. The study results show that image matching which is the bottleneck of SFM, SLAM and IBL plays the major role in favour of VSFM. This will result in using wrong matches in building the 3D map. It is crucial for IBL to choose the software that provides the best quality of points, \textit{i.e.} the largest number of correct 3D points. For this reason, VSFM will be chosen to reconstruct the 3D maps for IBL. Second, this work presents a comparative study of the main approaches, namely Brute Force Matching, Tree-Based Approach, Embedded Ferns Classification, ACG Localizer, Keyframe Approach, Decision Forest, Worldwide Pose Estimation and MPEG Search Space Reduction. The objective of the comparative analysis was to first uncover the specifics of each of these techniques and thereby understand the advantages and disadvantages of each of them. The testing was performed on Dubrovnik Dataset where the localization is determined with respect to a 3D cloud map which was computed using a Structure-from-Motion approach. The study results show that the current state of the art IBL solutions still face challenges in search space reduction, feature matching, clustering, and the quality of the solution is not consistent across all query images. Third, this work addresses the search space problem in order to solve the IBL problem. The Gist-based Search Space Reduction (GSSR), an efficient alternative to the available search space solutions, is proposed. It relies on GIST descriptors to considerably reduce search space and computational time, while at the same exceeding the state of the art in localization accuracy. Experiments on the 7 scenes datasets of Microsoft Research reveal considerable speedups for GSSR versus tree-based approaches, reaching a 4 times faster speed for the Heads dataset, and reducing the search space by an average of 92% while maintaining a better accuracy

CAD2Render: A Modular Toolkit for GPU-accelerated Photorealistic Synthetic Data Generation for the Manufacturing Industry

The use of computer vision for product and assembly quality control is becoming ubiquitous in the manufacturing industry. Lately, it is apparent that machine learning based solutions are outperforming classical computer vision algorithms in terms of performance and robustness. However, a main drawback is that they require sufficiently large and labeled training datasets, which are often not available or too tedious and too time consuming to acquire. This is especially true for low-volume and high-variance manufacturing. Fortunately, in this industry, CAD models of the manufactured or assembled products are available. This paper introduces CAD2Render, a GPU-accelerated synthetic data generator based on the Unity High Definition Render Pipeline (HDRP). CAD2Render is designed to add variations in a modular fashion, making it possible for high customizable data generation, tailored to the needs of the industrial use case at hand. Although CAD2Render is specifically designed for manufacturing use cases, it can be used for other domains as well. We validate CAD2Render by demonstrating state of the art performance in two industrial relevant setups. We demonstrate that the data generated by our approach can be used to train object detection and pose estimation models with a high enough accuracy to direct a robot. The code for CAD2Render is available at https://github.com/EDM-Research/CAD2Render.Comment: Accepted at the Workshop on Photorealistic Image and Environment Synthesis for Computer Vision (PIES-CV) at WACV2

Hyperspectral benthic mapping from underwater robotic platforms

We live on a planet of vast oceans; 70% of the Earth's surface is covered in water. They are integral to supporting life, providing 99% of the inhabitable space on Earth. Our oceans and the habitats within them are under threat due to a variety of factors. To understand the impacts and possible solutions, the monitoring of marine habitats is critically important. Optical imaging as a method for monitoring can provide a vast array of information however imaging through water is complex. To compensate for the selective attenuation of light in water, this thesis presents a novel light propagation model and illustrates how it can improve optical imaging performance. An in-situ hyperspectral system is designed which comprised of two upward looking spectrometers at different positions in the water column. The downwelling light in the water column is continuously sampled by the system which allows for the generation of a dynamic water model. In addition to the two upward looking spectrometers the in-situ system contains an imaging module which can be used for imaging of the seafloor. It consists of a hyperspectral sensor and a trichromatic stereo camera. New calibration methods are presented for the spatial and spectral co-registration of the two optical sensors. The water model is used to create image data which is invariant to the changing optical properties of the water and changing environmental conditions. In this thesis the in-situ optical system is mounted onboard an Autonomous Underwater Vehicle. Data from the imaging module is also used to classify seafloor materials. The classified seafloor patches are integrated into a high resolution 3D benthic map of the surveyed site. Given the limited imaging resolution of the hyperspectral sensor used in this work, a new method is also presented that uses information from the co-registered colour images to inform a new spectral unmixing method to resolve subpixel materials

Development of new intelligent autonomous robotic assistant for hospitals

Continuous technological development in modern societies has increased the quality of life and average life-span of people. This imposes an extra burden on the current healthcare infrastructure, which also creates the opportunity for developing new, autonomous, assistive robots to help alleviate this extra workload. The research question explored the extent to which a prototypical robotic platform can be created and how it may be implemented in a hospital environment with the aim to assist the hospital staff with daily tasks, such as guiding patients and visitors, following patients to ensure safety, and making deliveries to and from rooms and workstations. In terms of major contributions, this thesis outlines five domains of the development of an actual robotic assistant prototype. Firstly, a comprehensive schematic design is presented in which mechanical, electrical, motor control and kinematics solutions have been examined in detail. Next, a new method has been proposed for assessing the intrinsic properties of different flooring-types using machine learning to classify mechanical vibrations. Thirdly, the technical challenge of enabling the robot to simultaneously map and localise itself in a dynamic environment has been addressed, whereby leg detection is introduced to ensure that, whilst mapping, the robot is able to distinguish between people and the background. The fourth contribution is geometric collision prediction into stabilised dynamic navigation methods, thus optimising the navigation ability to update real-time path planning in a dynamic environment. Lastly, the problem of detecting gaze at long distances has been addressed by means of a new eye-tracking hardware solution which combines infra-red eye tracking and depth sensing. The research serves both to provide a template for the development of comprehensive mobile assistive-robot solutions, and to address some of the inherent challenges currently present in introducing autonomous assistive robots in hospital environments.Open Acces