Sky Detection in Images and Video

Práce pojednává o problému detekce oblohy v obraze a videu. Je zde popsána vybraná metoda, použitá k vytvoření aplikace. Pozornost je také věnována jejímu možnému vylepšení. Výsledkem je aplikace, která je schopná detekovat více typů oblohy. Práce je zaměřena hlavně na detekci v obraze, ale je zde i možnost práce s videem. Správná funkce detekce je ověřena na testovací sadě a v práci jsou prezentovány výsledky testování.This work is about the sky detection in images and video. This paper describes the selected method of detection, which is used to create application. The focus is also on possible improvement the selected method. The result is an application, which is able to detect more types of sky. The work is mainly focused on the detection in image, but it is also possible to work with video. The correct detection is verified on the test set and the results of testing are presented in this paper.

Sky View Factor footprints for urban climate modeling

Urban morphology is an important multidimensional variable to consider in climate modeling and observations, because it significantly drives the local and micro-scale climatic variability in cities. Urban form can be described through urban canopy parameters (UCPs) that resolve the spatial heterogeneity of cities by specifying the 3-dimensional geometry, arrangement, and materials of urban features. The sky view factor (SVF) is a dimension-reduced UCP capturing 3-dimensional form through horizon limitation fractions. SVF has become a popular metric to parameterize urban morphology, but current approaches are difficult to scale up to global coverage. This study introduces a Big-Data approach to calculate SVFs for urban areas from Google Street View (GSV). 90-degree field-of-view GSV photos are retrieved and converted into hemispherical views through equiangular projection. The fisheyes are segmented into sky and non-sky pixels using image processing, and the SVF is calculated using an annulus method. Results are compared to SVFs retrieved from GSV images segmented using deep learning. SVF footprints are presented for urban areas around the world tallying 15,938,172 GSV locations. Two use cases are introduced: (1) an evaluation of a Google Earth Engine classified Local Climate Zone map for Singapore; (2) hourly sun duration maps for New York and San Francisco

SIGS: Synthetic Imagery Generating Software for the development and evaluation of vision-based sense-and-avoid systems

Unmanned Aerial Systems (UASs) have recently become a versatile platform for many civilian applications including inspection, surveillance and mapping. Sense-and-Avoid systems are essential for the autonomous safe operation of these systems in non-segregated airspaces. Vision-based Sense-and-Avoid systems are preferred to other alternatives as their price, physical dimensions and weight are more suitable for small and medium-sized UASs, but obtaining real flight imagery of potential collision scenarios is hard and dangerous, which complicates the development of Vision-based detection and tracking algorithms. For this purpose, user-friendly software for synthetic imagery generation has been developed, allowing to blend user-defined flight imagery of a simulated aircraft with real flight scenario images to produce realistic images with ground truth annotations. These are extremely useful for the development and benchmarking of Vision-based detection and tracking algorithms at a much lower cost and risk. An image processing algorithm has also been developed for automatic detection of the occlusions caused by certain parts of the UAV which carries the camera. The detected occlusions can later be used by our software to simulate the occlusions due to the UAV that would appear in a real flight with the same camera setup. Additionally this algorithm could be used to mask out pixels which do not contain relevant information of the scene for the visual detection, making the image search process more efficient. Finally an application example of the imagery obtained with our software for the benchmarking of a state-of-art visual tracker is presented

Astrofüüsikaliste struktuuride uurimine klasteranalüüsi meetoditega

Väitekirja elektrooniline versioon ei sisalda publikatsiooneAntud doktoritöös uurime klasteranalüüsi meetodite abil kahte tüüpi astrofüüsikalisi andmeid – suureskaalalisi galaktikate punanihke vaatluseid ja suuri superarvuti simulatsioone turbulentsest kineetilisest plasmast. Töö esimeses pooles uurime Universumi struktuuri kõige domineerivamat elementi – galaktilisi filamente. Enamus Universumi galaktikaid asuvad nendes pikkades sildades, mis ühendavad sfäärilisi galaktikate parvi ja peaaegu tühjasid hoomamatuid tühikuid. Filamentvõrgustiku kaardistamine on väga olulise tähtsusega, sest see aitab meil mõista selles leiduvate galaktikate evolutsiooni ja galaktikatevahelist ainet. Antud töös leiame senini varjatud mustri galaktikate paiknemises piki filamente, mis viitab galaktikate evolutsiooni mõjutavatele keskkonna teguritele. Seejärel kinnitame uue galaktikateandmestiku ja filamentvõrgustiku ruumilise klasterdumise, mis kinnitab antud võrgustiku õigsust ja motiveerib neid uusi galaktikaid tuleviku modelleerimisel kasutama. Töö teises pooles uurime pilte, mis on saadud magneetiliselt domineeritud astrofüüsikalise plasma simulatsioonist. Antud mudel simuleerib füüsikalist fenomeni, mis leidub galaktikate klastrites, mustade aukude akretsiooniketastes, Päikese koroonas ja isegi tuumasünteesi reaktorites. Kõrgelt laetud osakesed väljuvad antud plasmast teatud füüsikaliste protsesside käigus, mida pole veel täielikult mõistetud. Selle mõistmiseks tuleb detekteerida erinevad füüsikalised struktuurid, mis plasmas leiduvad. Antud töös rakendame juhendamata masinõppe meetodit ning kaardistame plasmas olevad struktuurid piksli täpsusega. Sealhulgas need objektid, mis kiirendavad osakesi plasmast lahkuma. Töös arendatakse ka ansambelõppe raamistik, mis tõstab oluliselt struktuuride kaardistamise täpsust. Antud töö demonstreerib klasteranalüüsi algoritmide võimekust füüsikaliste fenomenide uurimisel.In this PhD thesis, two classes of astrophysical datasets – large scale galaxy redshift surveys and large supercomputer simulations of fully-kinetic turbulent plasma – are studied with clustering algorithms. In the first part we investigate the most dominant structure element of the Universe: the galaxy filaments. Majority of galaxies in the Universe reside in these galaxy filaments, which are long bridges connecting spherical high-density regions of galaxies and border immense voids almost without galaxies. Mapping the structure from observational galaxy datasets is of utmost importance for understanding the objects residing inside them, that is, galaxies and the intergalactic medium. In this work, we reveal a hidden pattern in the locations of galaxies residing inside these structures, which sheds light on environmental effects governing the evolution of galaxies. Then, we trace the detected galaxy filaments with a new observational dataset of galaxies, and prove the detected network. This motivates the use of these new datasets in the future modeling of the Universe. In the second part of this thesis we study images originating from simulations of turbulent magnetically dominated plasma, which models the physical phenomena observed in galaxy clusters, black hole accretion disks, solar corona, and even in fusion reactors. Physical phenomena responsible for the excitation of particles inside the plasma are not yet fully understood. In order to understand the underlying physics, the physical structures inside the plasma need to be detected. We apply an unsupervised machine learning algorithm on these images; and detect the physical structures pixel-by-pixel, including those responsible for the ejection of particles. We also develop an ensemble framework to improve the accuracy of the results. This thesis demonstrates the great potential and value of clustering analysis tools, from a wide spectrum of concepts, for revealing and understanding physical phenomena.https://www.ester.ee/record=b539540

A fruit recognition method for automatic harvesting

Automation of harvesting is always one of the hottest topics in greenhouse operation. But before this, a reliable method of identifying mature fruit clusters on plants is required. This thesis presents a method to detect and recognize mature tomato fruit clusters on a complex-structured tomato plant containing clutter and occlusion in a tomato greenhouse. A color stereo vision camera is applied as the vision sensor. The proposed method performs a 3D reconstruction with the data collected by the stereo camera to create a 3D environment for further processing. The Color Layer Growing (CLG) method is introduced to segment the mature fruits from the leaves, stalks, background and noise. Target fruit clusters can then be located by depth segmentation. The experimental data was collected from a tomato greenhouse and the method is justified by the experimental results

Calculating Staircase Slope from a Single Image

Realistic modeling of a 3D environment has grown in popularity due to the increasing realm of practical applications. Whether for practical navigation purposes, entertainment value, or architectural standardization, the ability to determine the dimensions of a room is becoming more and more important. One of the trickier, but critical, features within any multistory environment is the staircase. Staircases are difficult to model because of their uneven surface and various depth aspects. Coupling this need is a variety of ways to reach this goal. Unfortunately, many such methods rely upon specialized sensory equipment, multiple calibrated cameras, or other such impractical setups. Here, we propose a simpler approach. This paper outlines a method for extracting the slope dimensions of a staircase using a single monocular image. By relying on only a single image, we negate the need for extraneous accessories and glean as much information from common pictures. We do not hope to achieve the high level of accuracy seen from laser scanning methods but seek to produce a viable result that can both be helpful for current applications and serve as a building block that contributes to later development. When constructing our pipeline, we take into account several options. Each step can be achieved with different techniques which we evaluate and compare on either a qualitative or quantitative level. This leads to our final result which can accurately determine the slope of a staircase with an error rate of 31.1%. With a small amount of previous knowledge or preprocessing, this drops down to an average of 18.7% Overall, we deem this an acceptable and optimal result given the limited information and processing resources which the program was allowed to utilize

Image Segmentation methods for fine-grained OCR Document Layout Analysis

Digitization has changed history research. The materials are available, and online archives make it easier to find the correct information and speed up the search for information. The remaining challenge is how to use modern digital methods to analyze the text of historical documents in more detail. This is an active research topic in digital humanities and computer science areas. Document layout analysis is where computer vision object detection methods can be applied to historical documents to identify the document pages’ present objects (i.e., page elements). The recent development in deep learning based computer vision provides excellent tools for this purpose. However, most reviewed systems focus on coarse-grained methods, where only the high-level page elements are detected (e.g., text, figures, tables). Fine-grained detection methods are required to be able to analyze texts on a more detailed level; for example, footnotes and marginalia are distinguished from the body text to enable proper analysis. The thesis studies how image segmentation techniques can be used for fine-grained OCR document layout analysis. How to implement fine-grained page segmentation and region classification systems in practice, and what are the accuracy and the main challenges of such a system? The thesis includes implementing a layout analysis model that uses the instance segmentation method (Mask R-CNN). This implementation is compared against another existing layout analysis using the semantic segmentation method (U-net based P2PaLA implementation)