Physical Realization of an Optical Automatic Target Recognition System

In the modern warfare there is an active development of a new trend connected with a robotic warfare. One of the critical elements of robotics warfare systems is an automatic target recognition system, allowing to recognize objects, based on the data received from sensors. This work considers aspects of optical realization of such a system by means of NIR target scanning at fixed wavelengths. An algorithm was designed, an experimental setup was built and samples of various modern gear and apparel materials were tested. For pattern testing the samples of actively arm engaged armies camouflages were chosen. Tests were performed both in clear atmosphere and in the artificial extremely humid and hot atmosphere to simulate field conditions.

Automatic recognition of fish from video sequences

The problem of automatic recognition of the fish from the video sequences is discussed in this Master’s Thesis. This is a very urgent issue for many organizations engaged in fish farming in Finland and Russia because the process of automation control and counting of individual species is turning point in the industry. The difficulties and the specific features of the problem have been identified in order to find a solution and propose some recommendations for the components of the automated fish recognition system. Methods such as background subtraction, Kalman filtering and Viola-Jones method were implemented during this work for detection, tracking and estimation of fish parameters. Both the results of the experiments and the choice of the appropriate methods strongly depend on the quality and the type of a video which is used as an input data. Practical experiments have demonstrated that not all methods can produce good results for real data, whereas on synthetic data they operate satisfactorily.

Similarity measure based classification

Luokittelujärjestelmää suunniteltaessa tarkoituksena on rakentaa systeemi, joka pystyy ratkaisemaan mahdollisimman tarkasti tutkittavan ongelma-alueen. Hahmontunnistuksessa tunnistusjärjestelmän ydin on luokitin. Luokittelun sovellusaluekenttä on varsin laaja. Luokitinta tarvitaan mm. hahmontunnistusjärjestelmissä, joista kuvankäsittely toimii hyvänä esimerkkinä. Myös lääketieteen parissa tarkkaa luokittelua tarvitaan paljon. Esimerkiksi potilaan oireiden diagnosointiin tarvitaan luokitin, joka pystyy mittaustuloksista päättelemään mahdollisimman tarkasti, onko potilaalla kyseinen oire vai ei. Väitöskirjassa on tehty similaarisuusmittoihin perustuva luokitin ja sen toimintaa on tarkasteltu mm. lääketieteen paristatulevilla data-aineistoilla, joissa luokittelutehtävänä on tunnistaa potilaan oireen laatu. Väitöskirjassa esitetyn luokittimen etuna on sen yksinkertainen rakenne, josta johtuen se on helppo tehdä sekä ymmärtää. Toinen etu on luokittimentarkkuus. Luokitin saadaan luokittelemaan useita eri ongelmia hyvin tarkasti. Tämä on tärkeää varsinkin lääketieteen parissa, missä jo pieni tarkkuuden parannus luokittelutuloksessa on erittäin tärkeää. Väitöskirjassa ontutkittu useita eri mittoja, joilla voidaan mitata samankaltaisuutta. Mitoille löytyy myös useita parametreja, joille voidaan etsiä juuri kyseiseen luokitteluongelmaan sopivat arvot. Tämä parametrien optimointi ongelma-alueeseen sopivaksi voidaan suorittaa mm. evoluutionääri- algoritmeja käyttäen. Kyseisessä työssä tähän on käytetty geneettistä algoritmia ja differentiaali-evoluutioalgoritmia. Luokittimen etuna on sen joustavuus. Ongelma-alueelle on helppo vaihtaa similaarisuusmitta, jos kyseinen mitta ei ole sopiva tutkittavaan ongelma-alueeseen. Myös eri mittojen parametrien optimointi voi parantaa tuloksia huomattavasti. Kun käytetään eri esikäsittelymenetelmiä ennen luokittelua, tuloksia pystytään parantamaan.

Probabilistic position and orientation estimation of rigid objects using an RGB-D sensor

The recent emergence of low-cost RGB-D sensors has brought new opportunities for robotics by providing affordable devices that can provide synchronized images with both color and depth information. In this thesis, recent work on pose estimation utilizing RGBD sensors is reviewed. Also, a pose recognition system for rigid objects using RGB-D data is implemented. The implementation uses half-edge primitives extracted from the RGB-D images for pose estimation. The system is based on the probabilistic object representation framework by Detry et al., which utilizes Nonparametric Belief Propagation for pose inference. Experiments are performed on household objects to evaluate the performance and robustness of the system.

Local Binary Patterns in Focal-Plane Processing. Analysis and Applications

Feature extraction is the part of pattern recognition, where the sensor data is transformed into a more suitable form for the machine to interpret. The purpose of this step is also to reduce the amount of information passed to the next stages of the system, and to preserve the essential information in the view of discriminating the data into different classes. For instance, in the case of image analysis the actual image intensities are vulnerable to various environmental effects, such as lighting changes and the feature extraction can be used as means for detecting features, which are invariant to certain types of illumination changes. Finally, classification tries to make decisions based on the previously transformed data. The main focus of this thesis is on developing new methods for the embedded feature extraction based on local non-parametric image descriptors. Also, feature analysis is carried out for the selected image features. Low-level Local Binary Pattern (LBP) based features are in a main role in the analysis. In the embedded domain, the pattern recognition system must usually meet strict performance constraints, such as high speed, compact size and low power consumption. The characteristics of the final system can be seen as a trade-off between these metrics, which is largely affected by the decisions made during the implementation phase. The implementation alternatives of the LBP based feature extraction are explored in the embedded domain in the context of focal-plane vision processors. In particular, the thesis demonstrates the LBP extraction with MIPA4k massively parallel focal-plane processor IC. Also higher level processing is incorporated to this framework, by means of a framework for implementing a single chip face recognition system. Furthermore, a new method for determining optical flow based on LBPs, designed in particular to the embedded domain is presented. Inspired by some of the principles observed through the feature analysis of the Local Binary Patterns, an extension to the well known non-parametric rank transform is proposed, and its performance is evaluated in face recognition experiments with a standard dataset. Finally, an a priori model where the LBPs are seen as combinations of n-tuples is also presented

Optical Character Recognition for Symbian OS

This Master's thesis addresses the design and implementation of the optical character recognition (OCR) system for a mobile device working on the Symbian operating system. The developed OCR system, named OCRCapriccio, emphasizes the modularity, effective extensibility and reuse. The system consists of two parts which are the graphical user interface and the OCR engine that was implemented as a plug-in. In fact, the plug-in includes two implementations of the OCR engine for enabling two types of recognition: the bitmap comparison based recognition and statistical recognition. The implementation results have shown that the approach based on bitmap comparison is more suitable for the Symbian environment because of its nature. Although the current implementation of bitmap comparison is lacking in accuracy, further development should be done in its direction. The biggest challenges of this work were related to developing an OCR scheme that would be suitable for Symbian OS Smartphones that have limited computational power and restricted resources.

Creating dynamic continuous improvement system for Larox Corporation

Today´s organizations must have the ability to react to rapid changes in the market. These rapid changes cause pressure to continuously find new efficient ways to organize work practices. Increased competition requires businesses to become more effective and to pay attention to quality of management and to make people to understand their work's impact on the final result. The fundamentals in continmuois improvement are systematic and agile tackling of indentified individual process constraints and the fact tha nothin finally improves without changes. Successful continuous improvement requires management commitment, education, implementation, measurement, recognition and regeneration. These ingredients form the foundation, both for breakthrough projects and small step ongoing improvement activities. One part of the organization's management system are the quality tools, which provide systematic methodologies for identifying problems, defining their root causes, finding solutions, gathering and sorting of data, supporting decision making and implementing the changes, and many other management tasks. Organizational change management includes processes and tools for managing the people in an organizational level change. These tools include a structured approach, which can be used for effective transition of organizations through change. When combined with the understanding of change management of individuals, these tools provide a framework for managing people in change,

System-level characterization of Th2 cell development and immune cell responses to ZnO and TiO2 nanoparticles

Asthma and allergy are common diseases and their prevalence is increasing. One of the hypotheses that explains this trend is exposure to inhalable chemicals such as traffi c-related air pollution. Epidemiological research supports this theory, as a correlation between environmental chemicals and allergic respiratory diseases has been found. In addition to ambient airborne particles, one may be exposed to engineered nanosized materials that are actively produced due to their favorable physico-chemical properties compared to their bulk size counterparts. On the cellular level, improper activity of T helper (Th) cells has been connected to allergic reactions. Th cells can differentiate into functionally different effector subsets, which are identifi ed according to their characteristic cytokine profi les resulting in specifi c ability to communicate with other cells. Th2 cells activate humoral immunity and stimulate eradication of extracellular pathogens. However, persistent predominance of Th2 cells is involved in a development of number of allergic diseases. The cytokine environment at the time of antigen recognition is the major factor determining the polarization of a naïve Th cell. Th2 cell differentiation is initiated by IL4, which signals via transcription factor STAT6. Although the importance of this pathway has been evaluated in the mouse studies, the signaling components involved have been largely unknown. The aim of this thesis was to identify molecules, which are under the control of IL4 and STAT6 in Th cells. This was done by using system-level analysis of STAT6 target genes at genome, mRNA and protein level resulting in identifi cation of various genes previously not connected to Th2 cell phenotype acquisition. In the study, STAT6-mediated primary and secondary target genes were dissection from each other and a detailed transcriptional kinetics of Th2 cell polarization of naïve human CD4+ T cells was collected. Integration of these data revealed the hierarchy of molecular events that mediates the differentiation towards Th2 cell phenotype. In addition, the results highlighted the importance of exploiting proteomics tools to complement the studies on STAT6 target genes identifi ed through transcriptional profi ling. In the last subproject, the effects of the exposure with ZnO and TiO2 nanoparticles was analyzed in Jurkat T cell line and in primary human monocyte-derived macrophages and dendritic cells to evaluate their toxicity and potential to cause infl ammation. Identifi cation of ZnO-derived gene expression showed that the same nanoparticles may elicit markedly distinctive responses in different cell types, thus underscoring the need for unbiased profi ling of target genes and pathways affected. The results gave additional proof that the cellular response to nanosized ZnO is due to leached Zn2+ ions. The approach used in ZnO and TiO2 nanoparticle study demonstrated the value of assessing nanoparticle responses through a toxicogenomics approach. The increased knowledge of Th2 cell signaling will hopefully reveal new therapeutic nodes and eventually improve our possibilities to prevent and tackle allergic infl ammatory diseases.

PDM system functions and utilizations analysis to improve efficiency of sheet metal product design and manufacturing

This study will concentrate on Product Data Management (PDM) systems, and sheet metal design features and classification. In this thesis, PDM is seen as an individual system which handles all product-related data and information. The meaning of relevant data is to take the manufacturing process further with fewer errors. The features of sheet metals are giving more information and value to the designed models. The possibility of implementing PDM and sheet metal features recognition are the core of this study. Their integration should make the design process faster and manufacturing-friendly products easier to design. The triangulation method is the basis for this research. The sections of this triangle are: scientific literature review, interview using the Delphi method and the author’s experience and observations. The main key findings of this study are: (1) the area of focus in triangle (the triangle of three different point of views: business, information exchange and technical) depends on the person’s background and their role in the company, (2) the classification in the PDM system (and also in the CAD system) should be done using the materials, tools and machines that are in use in the company and (3) the design process has to be more effective because of the increase of industrial production, sheet metal blank production and the designer’s time spent on actual design and (4) because Design For Manufacture (DFM) integration can be done with CAD-programs, DFM integration with the PDM system should also be possible.

Semantic and fuzzy modelling for human behaviour recognition in smart spaces : a case study on ambient assisted living.

Human activity recognition in everyday environments is a critical, but challenging task in Ambient Intelligence applications to achieve proper Ambient Assisted Living, and key challenges still remain to be dealt with to realize robust methods. One of the major limitations of the Ambient Intelligence systems today is the lack of semantic models of those activities on the environment, so that the system can recognize the speci c activity being performed by the user(s) and act accordingly. In this context, this thesis addresses the general problem of knowledge representation in Smart Spaces. The main objective is to develop knowledge-based models, equipped with semantics to learn, infer and monitor human behaviours in Smart Spaces. Moreover, it is easy to recognize that some aspects of this problem have a high degree of uncertainty, and therefore, the developed models must be equipped with mechanisms to manage this type of information. A fuzzy ontology and a semantic hybrid system are presented to allow modelling and recognition of a set of complex real-life scenarios where vagueness and uncertainty are inherent to the human nature of the users that perform it. The handling of uncertain, incomplete and vague data (i.e., missing sensor readings and activity execution variations, since human behaviour is non-deterministic) is approached for the rst time through a fuzzy ontology validated on real-time settings within a hybrid data-driven and knowledgebased architecture. The semantics of activities, sub-activities and real-time object interaction are taken into consideration. The proposed framework consists of two main modules: the low-level sub-activity recognizer and the high-level activity recognizer. The rst module detects sub-activities (i.e., actions or basic activities) that take input data directly from a depth sensor (Kinect). The main contribution of this thesis tackles the second component of the hybrid system, which lays on top of the previous one, in a superior level of abstraction, and acquires the input data from the rst module's output, and executes ontological inference to provide users, activities and their in uence in the environment, with semantics. This component is thus knowledge-based, and a fuzzy ontology was designed to model the high-level activities. Since activity recognition requires context-awareness and the ability to discriminate among activities in di erent environments, the semantic framework allows for modelling common-sense knowledge in the form of a rule-based system that supports expressions close to natural language in the form of fuzzy linguistic labels. The framework advantages have been evaluated with a challenging and new public dataset, CAD-120, achieving an accuracy of 90.1% and 91.1% respectively for low and high-level activities. This entails an improvement over both, entirely data-driven approaches, and merely ontology-based approaches. As an added value, for the system to be su ciently simple and exible to be managed by non-expert users, and thus, facilitate the transfer of research to industry, a development framework composed by a programming toolbox, a hybrid crisp and fuzzy architecture, and graphical models to represent and con gure human behaviour in Smart Spaces, were developed in order to provide the framework with more usability in the nal application. As a result, human behaviour recognition can help assisting people with special needs such as in healthcare, independent elderly living, in remote rehabilitation monitoring, industrial process guideline control, and many other cases. This thesis shows use cases in these areas.

Design and implementation of machine vision system for the mobile assembly robot

The review of intelligent machines shows that the demand for new ways of helping people in perception of the real world is becoming higher and higher every year. This thesis provides information about design and implementation of machine vision for mobile assembly robot. The work has been done as a part of LUT project in Laboratory of Intelligent Machines. The aim of this work is to create a working vision system. The qualitative and quantitative research were done to complete this task. In the first part, the author presents the theoretical background of such things as digital camera work principles, wireless transmission basics, creation of live stream, methods used for pattern recognition. Formulas, dependencies and previous research related to the topic are shown. In the second part, the equipment used for the project is described. There is information about the brands, models, capabilities and also requirements needed for implementation. Although, the author gives a description of LabVIEW software, its add-ons and OpenCV which are used in the project. Furthermore, one can find results in further section of considered thesis. They mainly represented by screenshots from cameras, working station and photos of the system. The key result of this thesis is vision system created for the needs of mobile assembly robot. Therefore, it is possible to see graphically what was done on examples. Future research in this field includes optimization of the pattern recognition algorithm. This will give less response time for recognizing objects. Presented by author system can be used also for further activities which include artificial intelligence usage.