Adaptive Document Image Binarization with Application in Processing Astronomical Logbooks

ACM Computing Classification System (1998): I.7, I.7.5.Recently, the digitalization of the astronomical scientific heritage has been considered an important task that can facilitate much researches in astronomy. The creation of digital libraries and databases of astronomical photographic plates brings up the problem of digitalization astronomical logbooks, since the data contained in them is crucial for the usage of the plates. An optical character recognition (OCR) system for the handwritten numerical data is needed in order to speed up the process of database creation and extension. In this paper document image binarization is considered since it is a critical stage for the subsequent steps in an OCR software system. A specific method is proposed which outmatches the state-of-the-art techniques in the case of the images of interest.This work has been partially supported by Grant No. DO02-275/2008, Bulgarian NSF, Ministry of Education and Science

Application of Wavelet Decomposition to Document Line Segmentation

ACM Computing Classification System (1998): I.7, I.7.5.In this paper an approach to document line segmentation is presented. The algorithm is based on a wavelet transform of the horizontal projective profile of the document image. The projective profile is examined as a one-dimensional discrete signal which is decomposed using the pyramidal wavelet algorithm up to a precise scale, where local minima and maxima are discovered. These local extrema, projected into the input signal, correspond to the spacing between document lines and to the pivots of the lines. The method has been tested on a broad set of printed and handwritten documents and proven to be stable and efficient

Processing of Byzantine Neume Notation in Ancient Historical Manuscripts

This article presents the principal results of the doctoral thesis “Recognition of neume notation in historical documents” by Lasko Laskov (Institute of Mathematics and Informatics at Bulgarian Academy of Sciences), successfully defended before the Specialized Academic Council for Informatics and Mathematical Modelling on 07 June 2010.Byzantine neume notation is a specific form of note script, used by the Orthodox Christian Church since ancient times until nowadays for writing music and musical forms in sacred documents. Such documents are an object of extensive scientific research and naturally with the development of computer and information technologies the need of a software tool which can assist these efforts is needed. In this paper a set of algorithms for processing and analysis of Byzantine neume notation are presented which include document image segmentation, character feature vector extraction, classifier learning and character recognition. The described algorithms are implemented as an integrated scientific software system.* This work has been partly supported by Grant No. DTK 02/54, Bulgarian Science Fund, Ministry of Education, Youth and Science

MIRIAM: A Multimodal Chat-Based Interface for Autonomous Systems

We present MIRIAM (Multimodal Intelligent inteRactIon for Autonomous systeMs), a multimodal interface to support situation awareness of autonomous vehicles through chat-based interaction. The user is able to chat about the vehicle's plan, objectives, previous activities and mission progress. The system is mixed initiative in that it pro-actively sends messages about key events, such as fault warnings. We will demonstrate MIRIAM using SeeByte's SeeTrack command and control interface and Neptune autonomy simulator.Comment: 2 pages, ICMI'17, 19th ACM International Conference on Multimodal Interaction, November 13-17 2017, Glasgow, U

Intrusion detection in unlabeled data with quarter-sphere Support Vector Machines

Practical application of data mining and machine learning techniques to intrusion detection is often hindered by the difficulty to produce clean data for the training. To address this problem a geometric framework for unsupervised anomaly detection has been recently proposed. In this framework, the data is mapped into a feature space, and anomalies are detected as the entries in sparsely populated regions. In this contribution we propose a novel formulation of a one-class Support Vector Machine (SVM) specially designed for typical IDS data features. The key idea of our ”quarter-sphere” algorithm is to encompass the data with a hypersphere anchored at the center of mass of the data in feature space. The proposed method and its behavior on varying percentages of attacks in the data is evaluated on the KDDCup 1999 dataset

Data Extraction from Carte Du Ciel Triple Images

Carte du Ciel (from French, map of the sky) is a part of a 19th century extensive international astronomical project whose goal was to map the entire visible sky. The results of this vast effort were collected in the form of astrographic plates and their paper representatives that are called astrographic maps and are widely distributed among many observatories and astronomical institutes over the world. Our goal is to design methods and algorithms to automatically extract data from digitized Carte du Ciel astrographic maps. This paper examines the image processing and pattern recognition techniques that can be adopted for automatic extraction of astronomical data from stars’ triple expositions that can aid variable stars detection in Carte du Ciel maps

A Natural Language Interface with Relayed Acoustic Communications for Improved Command and Control of AUVs

Autonomous underwater vehicles (AUVs) are being tasked with increasingly complex missions. The acoustic communications required for AUVs are, by the nature of the medium, low bandwidth while adverse environmental conditions underwater often mean they are also intermittent. This has motivated development of highly autonomous systems, which can operate independently of their operators for considerable periods of time. These missions often involve multiple vehicles leading not only to challenges in communications but also in command and control (C2). Specifically operators face complexity in controlling multi-objective, multi-vehicle missions, whilst simultaneously facing uncertainty over the current status and safety of several remote high value assets. Additionally, it may be required to perform command and control of these complex missions in a remote control room. In this paper, we propose a combination of an intuitive, natural language operator interface combined with communications that use platforms from multiple domains to relay data over different mediums and transmission modes, improving command and control of collaborative and fully autonomous missions. In trials, we have demonstrated an integrated system combining working prototypes with established commercial C2 software that enables the use of a natural language interface to monitor an AUV survey mission in an on-shore command and control centre.Comment: The definitive version of this preprint is to be Published in AUV2018 Keywords: Conversational agent, Natural Language Understanding, Chatbot, AUV, USV, Communication Relay, Acoustic, Communicatio

As-Rigid-As-Possible Optical Flow Estimation on the GPU using Dual Numbers

Výpočet optického toku je jedním ze základních úkolů v oblasti počítačového vidění. Během posledních tří desetiletí byla navržena různá řešení - od klasických algoritmů až po neuronové sítě. V této práci popisujeme několik metod k výpočtu optického toku. Zaměřujeme se na dva algoritmy, které explicitně zachovávají lokální tuhost výsledného vektorového pole. Kromě toho, že poskytujeme podrobný popis samotných algoritmů a souvisejících matematických nástrojů, optimalizujeme navíc výkon jedné vybrané metody s využitím GPU. V teoretické části práce rozebíráme několik algoritmů pro výpočet optického toku a popisujeme specifické pro GPU optimalizací jednoho z nich. V praktické části hodnotíme a porovnáváme kvalitu výsledků získaných každou z prozkoumaných metod. Také měříme výpočetní rychlost GPU verze vybraného algoritmu a porovnáváme ji s rychlostí výchozí sekvenční implementace. Testování se provádí na reálném produkčním videu.The problem of optical flow estimation is among fundamental objectives in the field of computer vision. During the past three decades, various solutions to it have been proposed - from classic algorithms to neural nets. In this thesis, we describe several methods and frameworks for optical flow estimation. We focus on the two algorithms that explicitly preserve local rigidity of the resulting flow field. In addition to providing the detailed description of the algorithms proper and a few related auxiliary concepts, we optimize the performance of a particular method - one of those that keep the flow locally rigid - by implementing it to run on the GPU. In the theoretical part, we study several optical flow estimation algorithms and describe GPU-specific optimizations for one of them. In the practical part, we evaluate and compare the quality of results produced by each of the reviewed methods. We also measure computational speed of the GPU-based version of the selected algorithm and compare it with the performance of the default sequential implementation. The testing is done on a real production video