Persistence Bag-of-Words for Topological Data Analysis

Persistent homology (PH) is a rigorous mathematical theory that provides a robust descriptor of data in the form of persistence diagrams (PDs). PDs exhibit, however, complex structure and are difficult to integrate in today's machine learning workflows. This paper introduces persistence bag-of-words: a novel and stable vectorized representation of PDs that enables the seamless integration with machine learning. Comprehensive experiments show that the new representation achieves state-of-the-art performance and beyond in much less time than alternative approaches.Comment: Accepted for the Twenty-Eight International Joint Conference on Artificial Intelligence (IJCAI-19). arXiv admin note: substantial text overlap with arXiv:1802.0485

Persistence codebooks for topological data analysis

Persistent homology is a rigorous mathematical theory that provides a robust descriptor of data in the form of persistence diagrams (PDs) which are 2D multisets of points. Their variable size makes them, however, difficult to combine with typical machine learning workflows. In this paper we introduce persistence codebooks, a novel expressive and discriminative fixed-size vectorized representation of PDs that adapts to the inherent sparsity of persistence diagrams. To this end, we adapt bag-of-words, vectors of locally aggregated descriptors and Fischer vectors for the quantization of PDs. Persistence codebooks represent PDs in a convenient way for machine learning and statistical analysis and have a number of favorable practical and theoretical properties including 1-Wasserstein stability. We evaluate the presented representations on several heterogeneous datasets and show their (high) discriminative power. Our approach yields comparable-and partly even higher-performance in much less time than alternative approaches

A deep learning approach for the recognition of urban ground pavements in historical sites

Urban management is a topic of great interest for local administrators, particularly because it is strongly connected to smart city issues and can have a great impact on making cities more sustainable. In particular, thinking about the management of the physical accessibility of cities, the possibility of automating data collection in urban areas is of great interest. Focusing then on historical centres and urban areas of cities and historical sites, it can be noted that their ground surfaces are generally characterised by the use of a multitude of different pavements. To strengthen the management of such urban areas, a comprehensive mapping of the different pavements can be very useful. In this paper, the survey of a historical city (Sabbioneta, in northern Italy) carried out with a Mobile Mapping System (MMS) was used as a starting point. The approach here presented exploit Deep Learning (DL) to classify the different pavings. Firstly, the points belonging to the ground surfaces of the point cloud were selected and the point cloud was rasterised. Then the raster images were used to perform a material classification using the Deep Learning approach, implementing U-Net coupled with ResNet 18. Five different classes of materials were identified, namely sampietrini, bricks, cobblestone, stone, asphalt. The average accuracy of the result is 94%.Xunta de Galicia | Ref. ED481B-2019-061Xunta de Galicia | Ref. ED431C 2020/01Ministerio de Ciencia e Innovación | Ref. PID2019-105221RB-C43Ministerio de Ciencia e Innovación | Ref. RYC2020-029193-

A DEEP LEARNING APPROACH FOR THE RECOGNITION OF URBAN GROUND PAVEMENTS IN HISTORICAL SITES

Urban management is a topic of great interest for local administrators, particularly because it is strongly connected to smart city issues and can have a great impact on making cities more sustainable. In particular, thinking about the management of the physical accessibility of cities, the possibility of automating data collection in urban areas is of great interest. Focusing then on historical centres and urban areas of cities and historical sites, it can be noted that their ground surfaces are generally characterised by the use of a multitude of different pavements. To strengthen the management of such urban areas, a comprehensive mapping of the different pavements can be very useful. In this paper, the survey of a historical city (Sabbioneta, in northern Italy) carried out with a Mobile Mapping System (MMS) was used as a starting point. The approach here presented exploit Deep Learning (DL) to classify the different pavings. Firstly, the points belonging to the ground surfaces of the point cloud were selected and the point cloud was rasterised. Then the raster images were used to perform a material classification using the Deep Learning approach, implementing U-Net coupled with ResNet 18. Five different classes of materials were identified, namely sampietrini, bricks, cobblestone, stone, asphalt. The average accuracy of the result is 94%

Towards vision based robots for monitoring built environments

In construction, projects are typically behind schedule and over budget, largely due to the difficulty of progress monitoring. Once a structure (e.g. a bridge) is built, inspection becomes an important yet dangerous and costly job. We can provide a solution to both problems if we can simplify or automate visual data collection, monitoring, and analysis. In this work, we focus specifically on improving autonomous image collection, building 3D models from the images, and recognizing materials for progress monitoring using the images and 3D models. Image capture can be done manually, but the process is tedious and better suited for autonomous robots. Robots follow a set trajectory to collect data of a site, but it is unclear if 3D reconstruction will be successful using the images captured by following this trajectory. We introduce a simulator that synthesizes feature tracks for 3D reconstruction to predict if images collected from a planned path will result in a successful 3D reconstruction. This can save time, money, and frustration because robot paths can be altered prior to the real image capture. When executing a planned trajectory, the robot needs to understand and navigate the environment autonomously. Robot navigation algorithms struggle in environments with few distinct features. We introduce a new fiducial marker that can be added to these scenes to increase the number of distinct features and a new detection algorithm that detects the marker with negligible computational overhead. Adding markers prior to data collection does not guarantee that the algorithms for 3D model generation will succeed. In fact, out of the box, these algorithms do not take advantage of the unique characteristics of markers. Thus, we introduce an improved structure from motion approach that takes advantage of marker detections when they are present. We also create a dataset of challenging indoor image collections with markers placed throughout and show that previous methods often fail to produce accurate 3D models. However, our approach produces complete, accurate 3D models for all of these new image collections. Recognizing materials on construction sites is useful for monitoring usage and tracking construction progress. However, it is difficult to recognize materials in real world scenes because shape and appearance vary considerably. Our solution is to introduce the first dataset of material patches that include both image data and 3D geometry. We then show that both independent and joint modeling of geometry are useful alongside image features to improve material recognition. Lastly, we use our material recognition with material priors from building plans to accurately identify progress on construction sites

Communication of Digital Material Appearance Based on Human Perception

Im alltägliche Leben begegnen wir digitalen Materialien in einer Vielzahl von Situationen wie beispielsweise bei Computerspielen, Filmen, Reklamewänden in zB U-Bahn Stationen oder beim Online-Kauf von Kleidungen. Während einige dieser Materialien durch digitale Modelle repräsentiert werden, welche das Aussehen einer bestimmten Oberfläche in Abhängigkeit des Materials der Fläche sowie den Beleuchtungsbedingungen beschreiben, basieren andere digitale Darstellungen auf der simplen Verwendung von Fotos der realen Materialien, was zB bei Online-Shopping häufig verwendet wird. Die Verwendung von computer-generierten Materialien ist im Vergleich zu einzelnen Fotos besonders vorteilhaft, da diese realistische Erfahrungen im Rahmen von virtuellen Szenarien, kooperativem Produkt-Design, Marketing während der prototypischen Entwicklungsphase oder der Ausstellung von Möbeln oder Accesoires in spezifischen Umgebungen erlauben. Während mittels aktueller Digitalisierungsmethoden bereits eine beeindruckende Reproduktionsqualität erzielt wird, wird eine hochpräzise photorealistische digitale Reproduktion von Materialien für die große Vielfalt von Materialtypen nicht erreicht. Daher verwenden viele Materialkataloge immer noch Fotos oder sogar physikalische Materialproben um ihre Kollektionen zu repräsentieren. Ein wichtiger Grund für diese Lücke in der Genauigkeit des Aussehens von digitalen zu echten Materialien liegt darin, dass die Zusammenhänge zwischen physikalischen Materialeigenschaften und der vom Menschen wahrgenommenen visuellen Qualität noch weitgehend unbekannt sind. Die im Rahmen dieser Arbeit durchgeführten Untersuchungen adressieren diesen Aspekt. Zu diesem Zweck werden etablierte digitalie Materialmodellen bezüglich ihrer Eignung zur Kommunikation von physikalischen und sujektiven Materialeigenschaften untersucht, wobei Beobachtungen darauf hinweisen, dass ein Teil der fühlbaren/haptischen Informationen wie z.B. Materialstärke oder Härtegrad aufgrund der dem Modell anhaftenden geometrische Abstraktion verloren gehen. Folglich wird im Rahmen der Arbeit das Zusammenspiel der verschiedenen Sinneswahrnehmungen (mit Fokus auf die visuellen und akustischen Modalitäten) untersucht um festzustellen, welche Informationen während des Digitalisierungsprozesses verloren gehen. Es zeigt sich, dass insbesondere akustische Informationen in Kombination mit der visuellen Wahrnehmung die Einschätzung fühlbarer Materialeigenschaften erleichtert. Eines der Defizite bei der Analyse des Aussehens von Materialien ist der Mangel bezüglich sich an der Wahnehmung richtenden Metriken die eine Beantwortung von Fragen wie z.B. "Sind die Materialien A und B sich ähnlicher als die Materialien C und D?" erlauben, wie sie in vielen Anwendungen der Computergrafik auftreten. Daher widmen sich die im Rahmen dieser Arbeit durchgeführten Studien auch dem Vergleich von unterschiedlichen Materialrepräsentationen im Hinblick auf. Zu diesem Zweck wird eine Methodik zur Berechnung der wahrgenommenen paarweisen Ähnlichkeit von Material-Texturen eingeführt, welche auf der Verwendung von Textursyntheseverfahren beruht und sich an der Idee/dem Begriff der geradenoch-wahrnehmbaren Unterschiede orientiert. Der vorgeschlagene Ansatz erlaubt das Überwinden einiger Probleme zuvor veröffentlichter Methoden zur Bestimmung der Änhlichkeit von Texturen und führt zu sinnvollen/plausiblen Distanzen von Materialprobem. Zusammenfassend führen die im Rahmen dieser Dissertation dargestellten Inhalte/Verfahren zu einem tieferen Verständnis bezüglich der menschlichen Wahnehmung von digitalen bzw. realen Materialien über unterschiedliche Sinne, einem besseren Verständnis bzgl. der Bewertung der Ähnlichkeit von Texturen durch die Entwicklung einer neuen perzeptuellen Metrik und liefern grundlegende Einsichten für zukünftige Untersuchungen im Bereich der Perzeption von digitalen Materialien.In daily life, we encounter digital materials and interact with them in numerous situations, for instance when we play computer games, watch a movie, see billboard in the metro station or buy new clothes online. While some of these virtual materials are given by computational models that describe the appearance of a particular surface based on its material and the illumination conditions, some others are presented as simple digital photographs of real materials, as is usually the case for material samples from online retailing stores. The utilization of computer-generated materials entails significant advantages over plain images as they allow realistic experiences in virtual scenarios, cooperative product design, advertising in prototype phase or exhibition of furniture and wearables in specific environments. However, even though exceptional material reproduction quality has been achieved in the domain of computer graphics, current technology is still far away from highly accurate photo-realistic virtual material reproductions for the wide range of existing categories and, for this reason, many material catalogs still use pictures or even physical material samples to illustrate their collections. An important reason for this gap between digital and real material appearance is that the connections between physical material characteristics and the visual quality perceived by humans are far from well-understood. Our investigations intend to shed some light in this direction. Concretely, we explore the ability of state-of-the-art digital material models in communicating physical and subjective material qualities, observing that part of the tactile/haptic information (eg thickness, hardness) is missing due to the geometric abstractions intrinsic to the model. Consequently, in order to account for the information deteriorated during the digitization process, we investigate the interplay between different sensing modalities (vision and hearing) and discover that particular sound cues, in combination with visual information, facilitate the estimation of such tactile material qualities. One of the shortcomings when studying material appearance is the lack of perceptually-derived metrics able to answer questions like "are materials A and B more similar than C and D?", which arise in many computer graphics applications. In the absence of such metrics, our studies compare different appearance models in terms of how capable are they to depict/transmit a collection of meaningful perceptual qualities. To address this problem, we introduce a methodology to compute the perceived pairwise similarity between textures from material samples that makes use of patch-based texture synthesis algorithms and is inspired on the notion of Just-Noticeable Differences. Our technique is able to overcome some of the issues posed by previous texture similarity collection methods and produces meaningful distances between samples. In summary, with the contents presented in this thesis we are able to delve deeply in how humans perceive digital and real materials through different senses, acquire a better understanding of texture similarity by developing a perceptually-based metric and provide a groundwork for further investigations in the perception of digital materials