An Orientation & Mobility Aid for People with Visual Impairments

Orientierung&Mobilität (O&M) umfasst eine Reihe von Techniken für Menschen mit Sehschädigungen, die ihnen helfen, sich im Alltag zurechtzufinden. Dennoch benötigen sie einen umfangreichen und sehr aufwendigen Einzelunterricht mit O&M Lehrern, um diese Techniken in ihre täglichen Abläufe zu integrieren. Während einige dieser Techniken assistive Technologien benutzen, wie zum Beispiel den Blinden-Langstock, Points of Interest Datenbanken oder ein Kompass gestütztes Orientierungssystem, existiert eine unscheinbare Kommunikationslücke zwischen verfügbaren Hilfsmitteln und Navigationssystemen. In den letzten Jahren sind mobile Rechensysteme, insbesondere Smartphones, allgegenwärtig geworden. Dies eröffnet modernen Techniken des maschinellen Sehens die Möglichkeit, den menschlichen Sehsinn bei Problemen im Alltag zu unterstützen, die durch ein nicht barrierefreies Design entstanden sind. Dennoch muss mit besonderer Sorgfalt vorgegangen werden, um dabei nicht mit den speziellen persönlichen Kompetenzen und antrainierten Verhaltensweisen zu kollidieren, oder schlimmstenfalls O&M Techniken sogar zu widersprechen. In dieser Dissertation identifizieren wir eine räumliche und systembedingte Lücke zwischen Orientierungshilfen und Navigationssystemen für Menschen mit Sehschädigung. Die räumliche Lücke existiert hauptsächlich, da assistive Orientierungshilfen, wie zum Beispiel der Blinden-Langstock, nur dabei helfen können, die Umgebung in einem limitierten Bereich wahrzunehmen, während Navigationsinformationen nur sehr weitläufig gehalten sind. Zusätzlich entsteht diese Lücke auch systembedingt zwischen diesen beiden Komponenten — der Blinden-Langstock kennt die Route nicht, während ein Navigationssystem nahegelegene Hindernisse oder O&M Techniken nicht weiter betrachtet. Daher schlagen wir verschiedene Ansätze zum Schließen dieser Lücke vor, um die Verbindung und Kommunikation zwischen Orientierungshilfen und Navigationsinformationen zu verbessern und betrachten das Problem dabei aus beiden Richtungen. Um nützliche relevante Informationen bereitzustellen, identifizieren wir zuerst die bedeutendsten Anforderungen an assistive Systeme und erstellen einige Schlüsselkonzepte, die wir bei unseren Algorithmen und Prototypen beachten. Existierende assistive Systeme zur Orientierung basieren hauptsächlich auf globalen Navigationssatellitensystemen. Wir versuchen, diese zu verbessern, indem wir einen auf Leitlinien basierenden Routing Algorithmus erstellen, der auf individuelle Bedürfnisse anpassbar ist und diese berücksichtigt. Generierte Routen sind zwar unmerklich länger, aber auch viel sicherer, gemäß den in Zusammenarbeit mit O&M Lehrern erstellten objektiven Kriterien. Außerdem verbessern wir die Verfügbarkeit von relevanten georeferenzierten Datenbanken, die für ein derartiges bedarfsgerechtes Routing benötigt werden. Zu diesem Zweck erstellen wir einen maschinellen Lernansatz, mit dem wir Zebrastreifen in Luftbildern erkennen, was auch über Ländergrenzen hinweg funktioniert, und verbessern dabei den Stand der Technik. Um den Nutzen von Mobilitätsassistenz durch maschinelles Sehen zu optimieren, erstellen wir O&M Techniken nachempfundene Ansätze, um die räumliche Wahrnehmung der unmittelbaren Umgebung zu erhöhen. Zuerst betrachten wir dazu die verfügbare Freifläche und informieren auch über mögliche Hindernisse. Weiterhin erstellen wir einen neuartigen Ansatz, um die verfügbaren Leitlinien zu erkennen und genau zu lokalisieren, und erzeugen virtuelle Leitlinien, welche Unterbrechungen überbrücken und bereits frühzeitig Informationen über die nächste Leitlinie bereitstellen. Abschließend verbessern wir die Zugänglichkeit von Fußgängerübergängen, insbesondere Zebrastreifen und Fußgängerampeln, mit einem Deep Learning Ansatz. Um zu analysieren, ob unsere erstellten Ansätze und Algorithmen einen tatsächlichen Mehrwert für Menschen mit Sehschädigung erzeugen, vollziehen wir ein kleines Wizard-of-Oz-Experiment zu unserem bedarfsgerechten Routing — mit einem sehr ermutigendem Ergebnis. Weiterhin führen wir eine umfangreichere Studie mit verschiedenen Komponenten und dem Fokus auf Fußgängerübergänge durch. Obwohl unsere statistischen Auswertungen nur eine geringfügige Verbesserung aufzeigen, beeinflußt durch technische Probleme mit dem ersten Prototypen und einer zu geringen Eingewöhnungszeit der Probanden an das System, bekommen wir viel versprechende Kommentare von fast allen Studienteilnehmern. Dies zeigt, daß wir bereits einen wichtigen ersten Schritt zum Schließen der identifizierten Lücke geleistet haben und Orientierung&Mobilität für Menschen mit Sehschädigung damit verbessern konnten

Recognition of Eye Characteristics

This chapter deals with the recognition of features contained within the human eye, namely the iris and retina. The great advantage is that both the iris and retina contain a large amount of information, that is, they can be used for a larger group of users. The disadvantage, on the other hand, is the fear from users in regard to possible eye injury. Both of these features cannot be easily acquired and misused to cheat a biometric system. This chapter also explains how to capture and process these two biometric characteristics. However, the number of biometric industrial solutions dealing with retina recognition is very limited—it is practically not possible to find an available biometric device for identity recognition on the market based on this biometric characteristic

Implementing early vision algorithms in analog hardware: an overview

In the last ten years, significant progress has been made in understanding the first steps in visual processing. Thus, a large number of algorithms exist that locate edges, compute disparities, estimate motion fields and find discontinuities in depth, motion, color and intensity. However, the application of these algorithms to real-life vision problems has been less successful, mainly because the associated computational cost prevents real-time machine vision implementations on anything but large-scale expensive digital computers. We here review the use of analog, special-purpose vision hardware, integrating image acquisition with early vision algorithms on a single VLSI chip. Such circuits have been designed and successfully tested for edge detection, surface interpolation, computing optical flow and sensor fusion. Thus, it appears that real-time, small, power-lean and robust analog computers are making a limited comeback in the form of highly dedicated, smart vision chips

Analysis of Image Sequence Data with Applications to Two-Dimensional Echocardiography

Digital two-dimensional echocardiography is an ultrasonic imaging technique that is used as an increasingly important noninvasive technique in the comprehensive characterization of the left ventricular structure and function. Quantitative analysis often uses heart wall motion and other shape attributes such as the heart wall thickness, heart chamber area, and the variation of these attributes throughout the cardiac cycle. These analyses require the complete determination of the heart wall boundaries. Poor image quality and large amount of noise makes computer detection of the boundaries difficult. An algorithm to detect both the inner and outer heart wall boundaries is presented. The algorithm was applied to images acquired from animal studies and from a tissue equivalent phantom to verify the performance. Different approaches to exploiting the temporal redundancy of the image data without making use of results from image segmentation and scene interpretation are explored. A new approach to perform image flow analysis is developed based on the Total Least Squares method. The result of this processing is an estimate of the velocities in the image plane. In an image understanding system, information acquired from related domains by other sensors are often useful to the analysis of images. Electrocardiogram signals measure the change of electrical potential changes in the heart muscle an d provide important information such as the timing data for image sequence analysis. These signals are frequently plagued by impulsive muscle noise and background drift due to patient movement. A new approach to solving these problems is presented using mathematical morphology. Experiments addressing various aspects of the problem, such as algorithm performance, choice of operator parameters, and response to sinusoidal inputs, are reported

Recognition of Eye Characteristics

Children grow and develop in a life colored by the violation of others right, crime, compulsion, ignorance, unclearness between right and wrong, good and bad, allowed and not allowed behaviors. Building moral intelligence is very important to do in order that the childrens intuition is able to differentiate the right and the wrong. Thus, they can reject the bad influences from outside. One of the ways used to give moral value to the children is sociodrama.The research aims to know the sociodrama method in improving the moral intelligence of children. Subject of the research is the student of elementary school. The number of subject in the experiment and control groups is same that is 14 students.The research is design using model of The Untreated Control Group Design with Pretest and Posttest. The design uses two groups examined which consist of an experiment group and a control group. The measurement is conducted twice using moral intelligence measurement instrument, namely before it is given treatment (pre-test) and after it has been given treatment (post-test).The result of analysis using T-Test shows that there is a difference of moral intelligence achievement level of the children between those who receive moral value guidance through sociodrama method and those who do not receive moral value guidance through sociodrama method p = 0,009 (p<0,05). The result of analysis also shows that there is difference of moral intelligence achievement level of th children before receiving moral value guidance through sociodrama method and after they have receive the moral value guidance through sociodrama method p = 0,033 (p<0,05). The result of analysis shows the great contribution of sociodrama method towards the moral intelligence of children is 30,9%

rTsfNet: a DNN model with Multi-head 3D Rotation and Time Series Feature Extraction for IMU-based Human Activity Recognition

Although many deep learning (DL) algorithms have been proposed for the IMU-based HAR domain, traditional machine learning that utilizes handcrafted time series features (TSFs) still often performs well. It is not rare that combinations among DL and TSFs show better accuracy than DL-only approaches. However, there is a problem with time series features in IMU-based HAR. The amount of derived features can vary greatly depending on the method used to select the 3D basis. Fortunately, DL's strengths include capturing the features of input data and adaptively deriving parameters. Thus, as a new DNN model for IMU-based human activity recognition (HAR), this paper proposes rTsfNet, a DNN model with Multi-head 3D Rotation and Time Series Feature Extraction. rTsfNet automatically selects 3D bases from which features should be derived by extracting 3D rotation parameters within the DNN. Then, time series features (TSFs), based on many researchers' wisdom, are derived to achieve HAR using MLP. Although rTsfNet is a model that does not use CNN, it achieved higher accuracy than existing models under well-managed benchmark conditions and multiple datasets: UCI HAR, PAMAP2, Daphnet, and OPPORTUNITY, all of which target different activities.Comment: Updating abstract length to clear a submission target's requirement. Updating English quality. Updating the best results of OPPORTUNITY (not iSPL version) and PAMAP

Part Description and Segmentation Using Contour, Surface and Volumetric Primitives

The problem of part definition, description, and decomposition is central to the shape recognition systems. The Ultimate goal of segmenting range images into meaningful parts and objects has proved to be very difficult to realize, mainly due to the isolation of the segmentation problem from the issue of representation. We propose a paradigm for part description and segmentation by integration of contour, surface, and volumetric primitives. Unlike previous approaches, we have used geometric properties derived from both boundary-based (surface contours and occluding contours), and primitive-based (quadric patches and superquadric models) representations to define and recover part-whole relationships, without a priori knowledge about the objects or object domain. The object shape is described at three levels of complexity, each contributing to the overall shape. Our approach can be summarized as answering the following question : Given that we have all three different modules for extracting volume, surface and boundary properties, how should they be invoked, evaluated and integrated? Volume and boundary fitting, and surface description are performed in parallel to incorporate the best of the coarse to fine and fine to coarse segmentation strategy. The process involves feedback between the segmentor (the Control Module) and individual shape description modules. The control module evaluates the intermediate descriptions and formulates hypotheses about parts. Hypotheses are further tested by the segmentor and the descriptors. The descriptions thus obtained are independent of position, orientation, scale, domain and domain properties, and are based purely on geometric considerations. They are extremely useful for the high level domain dependent symbolic reasoning processes, which need not deal with tremendous amount of data, but only with a rich description of data in terms of primitives recovered at various levels of complexity

A novel monitoring system for the training of elite swimmers

Swimming performance is primarily judged on the overall time taken for a swimmer to complete a specified distance performing a stroke that complies with current regulations defined by the Fédération Internationale de Natation (FINA), the International governing body of swimming. There are three contributing factors to this overall time; the start, free swimming and turns. The contribution of each of these factors is event dependent; for example, in a 50m event there are no turns, however, the start can be a significant contributor. To improve overall performance each of these components should be optimised in terms of skill and execution. This thesis details the research undertaken towards improving performance-related feedback in swimming. The research included collaboration with British Swimming, the national governing body for swimming in the U.K., to drive the requirements and direction of research. An evaluation of current methods of swimming analysis identified a capability gap in real-time, quantitative feedback. A number of components were developed to produce an integrated system for comprehensive swim performance analysis in all phases of the swim, i.e. starts, free swimming and turns. These components were developed to satisfy two types of stakeholder requirements. Firstly, the measurement requirements, i.e. what does the end user want to measure? Secondly, the process requirements, i.e. how would these measurements be achieved? The components developed in this research worked towards new technologies to facilitate a wider range of measurement parameters using automated methods as well as the application of technologies to facilitate the automation of current techniques. The development of the system is presented in detail and the application of these technologies is presented in case studies for starts, free swimming and turns. It was found that developed components were able to provide useful data indicating levels of performance in all aspects of swimming, i.e. starts, free swimming and turns. For the starts, an integrated solution of vision, force plate technology and a wireless iii node enabled greater insight into overall performance and quantitative measurements of performance to be captured. Force profiles could easily identify differences in swimmer ability or changes in technique. The analysis of free swimming was predominantly supported by the wireless sensor technology, whereby signal analysis was capable of automatically determining factors such as lap times variations within strokes. The turning phase was also characterised in acceleration space, allowing the phases of the turn to be individually assessed and their contribution to total turn time established. Each of the component technologies were not used in isolation but were supported by other synchronous data capture. In all cases a vision component was used to increase understanding of data outputs and provide a medium that coaches and athletes were comfortable with interpreting. The integrated, component based system has been developed and tested to prove its ability to produce useful, quantitative feedback information for swimmers. The individual components were found to be capable of providing greater insight into swimming performance, that has not been previously possible using the current state of the art techniques. Future work should look towards the fine-tuning of the prototype system into a useable solution for end users. This relies on the refinement of components and the development of an appropriate user interface to enable ease of data collection, analysis, presentation and interpretation