Text Annotation Handbook: A Practical Guide for Machine Learning Projects

This handbook is a hands-on guide on how to approach text annotation tasks. It provides a gentle introduction to the topic, an overview of theoretical concepts as well as practical advice. The topics covered are mostly technical, but business, ethical and regulatory issues are also touched upon. The focus lies on readability and conciseness rather than completeness and scientific rigor. Experience with annotation and knowledge of machine learning are useful but not required. The document may serve as a primer or reference book for a wide range of professions such as team leaders, project managers, IT architects, software developers and machine learning engineers.Comment: 30 pages, white pape

Utvärdering av probabilistiska representationer för modellering och förståelse av form baserat på syntetisk och verklig sensordata

The advancements in robotic perception in the recent years have empowered robots to better execute tasks in various environments. The perception of objects in the robot work space significantly relies on how sensory data is represented. In this context, 3D models of object’s surfaces have been studied as a means to provide useful insights on shape of objects and ultimately enhance robotic perception. This involves several challenges, because sensory data generally presents artifacts, such as noise and incompleteness. To tackle this problem, we employ Gaussian Process Implicit Surface (GPIS), a non-parametric probabilistic reconstruction of object’s surfaces from 3D data points. This thesis investigates different configurations for GPIS, as a means to tackle the extraction of shape information. In our approach we interpret an object’s surface as the level-set of an underlying sparse Gaussian Process (GP) with variational formulation. Results show that the variational formulation for sparse GP enables a reliable approximation to the full GP solution. Experiments are performed on a synthetic and a real sensory data set. We evaluate results by assessing how close the reconstructed surfaces are to the ground-truth correspondences, and how well objects from different categories are clustered based on the obtained representation. Finally we conclude that the proposed solution derives adequate surface representations to reason about object shape and to discriminate objects based on shape information.Framsteg inom robotperception de senaste åren har resulterat i robotar som är bättre på attutföra uppgifter i olika miljöer. Perception av objekt i robotens arbetsmiljö är beroende avhur sensorisk data representeras. I det här sammanhanget har 3D-modeller av objektytorstuderats för att ge användbar insikt om objektens form och i slutändan bättre robotperception. Detta innebär flera utmaningar, eftersom sensoriska data ofta innehåller artefakter, såsom brus och brist på data. För att hantera detta problem använder vi oss av Gaussian Process Implicit Surface (GPIS), som är en icke-parametrisk probabilistisk rekonstruktion av ett objekts yta utifrån 3D-punkter. Detta examensarbete undersöker olika konfigurationer av GPIS för att på detta sätt kunna extrahera forminformation. I vår metod tolkar vi ett objekts yta som nivåkurvor hos en underliggande gles variational Gaussian Process (GP) modell. Resultat visar att en gles variational GP möjliggör en tillförlitlig approximation av en komplett GP-lösningen. Experiment utförs på ett syntetisk och ett reellt sensorisk dataset. Vi utvärderar resultat genom att bedöma hur nära de rekonstruerade ytorna är till grundtruth- korrespondenser, och hur väl objektkategorier klustras utifrån den erhållna representationen. Slutligen konstaterar vi att den föreslagna lösningen leder till tillräckligt goda representationer av ytor för tolkning av objektens form och för att diskriminera objekt utifrån forminformation

Shape Modeling based on Sparse Gaussian Process Implicit Surfaces

Reconstructing, modeling, and accounting for uncertainty in three-dimensional shapes is important in a largenumber of areas, such as biometrics, biomedical imaging, data mining, robotics. However, it is challenging to build accurate models of novel objects based on real sensory data as the measurements are often incomplete and noisy. Besides, imperfect sensory data requires explicit uncertainty modeling that can enable action planning with maximum information gain and efficient use of data. We present a probabilistic approach for learning object models based on visual and tactile data. We study Gaussian Process Implicit Surface (GPIS) representation, a non-parametric probabilistic reconstruction of object surfaces from 3D data points which provides a principled approach to encode uncertainty in the data, and investigate different configurations for GPIS. We interpret an object surface as the level-set of an underlying sparse GP. Experiments are performed on synthetic and real data sets obtained from physical interaction with objects. We evaluate results by assessing how close the reconstructed surfaces are to the ground truth, and how well objects from different categories are clustered based on the obtained representation. Results show that sparse GPs enable a reliable approximation to the full GP solution and the proposed method yields adequate surface representations to distinguish objects

Visual and Tactile 3D Point Cloud Data from Real Robots for Shape Modeling and Completion

If you use this data, please cite "Y. Bekiroglu, M. Björkman, G. Zarzar Gandler, J. Exner, C. H. Ek, D. Kragic. Visual and Tactile 3D Point Cloud Data from Real Robots for Shape Modeling and Completion, Data in Brief (2020), https://doi.org/10.1016/j.dib.2020.105335". The data was used for shape completion and modeling via Implicit Surface representation and Gaussian-Process-based regression, in the work “G. Zarzar Gandler, C. H. Ek, M. Björkman, R. Stolkin, Y. Bekiroglu. Object shape estimation and modeling, based on sparse Gaussian process implicit surfaces, combining visual data and tactile exploration, Robotics and Autonomous Systems (2020), https://doi.org/10.1016/j.robot.2020.103433”, and also used partially in “M. Björkman, Y. Bekiroglu, V. Högman, D. Kragic. Enhancing visual perception of shape through tactile glances, in IEEE/RSJ International Conference on Intelligent Robots and Systems (2013)"