Using appearance and context for outdoor scene object classification

We propose a probabilistic object classifier for outdoor scene analysis as a first step in solving the problem of scene context generation. The method begins with a top-down control, which uses the previously learned models (appearance and absolute location) to obtain an initial pixel-level classification. This information provides us the core of objects, which is used to acquire a more accurate object model. Therefore, their growing by specific active regions allows us to obtain an accurate recognition of known regions. Next, a stage of general segmentation provides the segmentation of unknown regions by a bottom-strategy. Finally, the last stage tries to perform a region fusion of known and unknown segmented objects. The result is both a segmentation of the image and a recognition of each segment as a given object class or as an unknown segmented object. Furthermore, experimental results are shown and evaluated to prove the validity of our proposal

Eye Fundus Image Analysis for Automatic Detection of Diabetic Retinopathy

Diabetes is a rapidly increasing worldwide problem which is characterised by defective metabolism of glucose that causes long-term dysfunction and failure of various organs. The most common complication of diabetes is diabetic retinopathy (DR), which is one of the primary causes of blindness and visual impairment in adults. The rapid increase of diabetes pushes the limits of the current DR screening capabilities for which the digital imaging of the eye fundus (retinal imaging), and automatic or semi-automatic image analysis algorithms provide a potential solution. In this work, the use of colour in the detection of diabetic retinopathy is statistically studied using a supervised algorithm based on one-class classification and Gaussian mixture model estimation. The presented algorithm distinguishes a certain diabetic lesion type from all other possible objects in eye fundus images by only estimating the probability density function of that certain lesion type. For the training and ground truth estimation, the algorithm combines manual annotations of several experts for which the best practices were experimentally selected. By assessing the algorithm’s performance while conducting experiments with the colour space selection, both illuminance and colour correction, and background class information, the use of colour in the detection of diabetic retinopathy was quantitatively evaluated. Another contribution of this work is the benchmarking framework for eye fundus image analysis algorithms needed for the development of the automatic DR detection algorithms. The benchmarking framework provides guidelines on how to construct a benchmarking database that comprises true patient images, ground truth, and an evaluation protocol. The evaluation is based on the standard receiver operating characteristics analysis and it follows the medical practice in the decision making providing protocols for image- and pixel-based evaluations. During the work, two public medical image databases with ground truth were published: DIARETDB0 and DIARETDB1. The framework, DR databases and the final algorithm, are made public in the web to set the baseline results for automatic detection of diabetic retinopathy. Although deviating from the general context of the thesis, a simple and effective optic disc localisation method is presented. The optic disc localisation is discussed, since normal eye fundus structures are fundamental in the characterisation of DR.

Identification of croplands of winter cereals in Rio Grande do Sul state, Brazil, through unsupervised classification of normalized difference vegetation index images

This study aimed to propose methods to identify croplands cultivated with winter cereals in the northern region of Rio Grande do Sul State, Brazil. Thus, temporal profiles of Normalized Difference Vegetation Index (NDVI) from MODIS sensor, from April to December of the 2000 to 2008, were analyzed. Firstly, crop masks were elaborated by subtracting the minimum NDVI image (April to May) from the maximum NDVI image (June to October). Then, an unsupervised classification of NDVI images was carried out (Isodata), considering the crop mask areas. According to the results, crop masks allowed the identification of pixels with greatest green biomass variation. This variation might be associated or not with winter cereals areas established to grain production. The unsupervised classification generated classes in which NDVI temporal profiles were associated with water bodies, pastures, winter cereals for grain production and for soil cover. Temporal NDVI profiles of the class winter cereals for grain production were in agree with crop patterns in the region (developmental stage, management standard and sowing dates). Therefore, unsupervised classification based on crop masks allows distinguishing and monitoring winter cereal crops, which were similar in terms of morphology and phenology.

Geostatistics and remote sensing methods in the classification of images of areas cultivated with citrus

This study compares the precision of three image classification methods, two of remote sensing and one of geostatistics applied to areas cultivated with citrus. The 5,296.52ha area of study is located in the city of Araraquara - central region of the state of São Paulo (SP), Brazil. The multispectral image from the CCD/CBERS-2B satellite was acquired in 2009 and processed through the Geographic Information System (GIS) SPRING. Three classification methods were used, one unsupervised (Cluster), and two supervised (Indicator Kriging/IK and Maximum Likelihood/Maxver), in addition to the screen classification taken as field checking.. Reliability of classifications was evaluated by Kappa index. In accordance with the Kappa index, the Indicator kriging method obtained the highest degree of reliability for bands 2 and 4. Moreover the Cluster method applied to band 2 (green) was the best quality classification between all the methods. Indicator Kriging was the classifier that presented the citrus total area closest to the field check estimated by -3.01%, whereas Maxver overestimated the total citrus area by 42.94%.

Utilization of Particle Image velocimetry in PPOOLEX condensation experiments

Particle Image Velocimetry, PIV, is an optical measuring technique to obtain velocity information of a flow in interest. With PIV it is possible to achieve two or three dimensional velocity vector fields from a measurement area instead of a single point in a flow. Measured flow can be either in liquid or in gas form. PIV is nowadays widely applied to flow field studies. The need for PIV is to obtain validation data for Computational Fluid Dynamics calculation programs that has been used to model blow down experiments in PPOOLEX test facility in the Lappeenranta University of Technology. In this thesis PIV and its theoretical background are presented. All the subsystems that can be considered to be part of a PIV system are presented as well with detail. Emphasis is also put to the mathematics behind the image evaluation. The work also included selection and successful testing of a PIV system, as well as the planning of the installation to the PPOOLEX facility. Already in the preliminary testing PIV was found to be good addition to the measuring equipment for Nuclear Safety Research Unit of LUT. The installation to PPOOLEX facility was successful even though there were many restrictions considering it. All parts of the PIV system worked and they were found out to be appropriate for the planned use. Results and observations presented in this thesis are a good background to further PIV use.

Evaluation of follicular lymphoid depletion in the Bursa of Fabricius: an alternative methodology using digital image analysis and artificial neural networks

Fifty Bursa of Fabricius (BF) were examined by conventional optical microscopy and digital images were acquired and processed using Matlab® 6.5 software. The Artificial Neuronal Network (ANN) was generated using Neuroshell® Classifier software and the optical and digital data were compared. The ANN was able to make a comparable classification of digital and optical scores. The use of ANN was able to classify correctly the majority of the follicles, reaching sensibility and specificity of 89% and 96%, respectively. When the follicles were scored and grouped in a binary fashion the sensibility increased to 90% and obtained the maximum value for the specificity of 92%. These results demonstrate that the use of digital image analysis and ANN is a useful tool for the pathological classification of the BF lymphoid depletion. In addition it provides objective results that allow measuring the dimension of the error in the diagnosis and classification therefore making comparison between databases feasible.

Image analysis and definition of nuclear phenotypes

Some basic topics concerned with the extraction of textural and geometric information from cell nucleus images as well as description and characterization of chromatin supraorganization and consequent classification of nuclear phenotypes are presented.

Manual and semi-automatic quantification of in vivo ¹H-MRS data for the classification of human primary brain tumors

In vivo proton magnetic resonance spectroscopy (¹H-MRS) is a technique capable of assessing biochemical content and pathways in normal and pathological tissue. In the brain, ¹H-MRS complements the information given by magnetic resonance images. The main goal of the present study was to assess the accuracy of ¹H-MRS for the classification of brain tumors in a pilot study comparing results obtained by manual and semi-automatic quantification of metabolites. In vivo single-voxel ¹H-MRS was performed in 24 control subjects and 26 patients with brain neoplasms that included meningiomas, high-grade neuroglial tumors and pilocytic astrocytomas. Seven metabolite groups (lactate, lipids, N-acetyl-aspartate, glutamate and glutamine group, total creatine, total choline, myo-inositol) were evaluated in all spectra by two methods: a manual one consisting of integration of manually defined peak areas, and the advanced method for accurate, robust and efficient spectral fitting (AMARES), a semi-automatic quantification method implemented in the jMRUI software. Statistical methods included discriminant analysis and the leave-one-out cross-validation method. Both manual and semi-automatic analyses detected differences in metabolite content between tumor groups and controls (P < 0.005). The classification accuracy obtained with the manual method was 75% for high-grade neuroglial tumors, 55% for meningiomas and 56% for pilocytic astrocytomas, while for the semi-automatic method it was 78, 70, and 98%, respectively. Both methods classified all control subjects correctly. The study demonstrated that ¹H-MRS accurately differentiated normal from tumoral brain tissue and confirmed the superiority of the semi-automatic quantification method.

Institutional environment in North Korea: market structure and overcoming institutional voids

Tutkimus sai innoituksensa, kun tutkija huomasi tarpeen liiketaloudelliselle, ajantasaiselle ja realistiselle tutkimukselle Pohjois-Korean markkinoista, joka kuvailisi markkinoiden olemassaolevia ja puuttuvia rakenteita sekä tutkisi mahdollisuuksia ylittää puuttuvat rakenteet. Institutionaalinen teoria valittiin sopivaksi viitekehykseksi kuvailla ja tutkia markkinarakennetta. Tutkimuskysymys muotoiltiin seuraavasti: “Miten ulkomaiset yritykset voivat reagoida puuttuviin markkinarakenteisiin Pohjois-Koreassa?”. Tutkimuskysymys jaettiin kolmeen osakysymykseen: (1) Millainen on Pohjois-Korean markkinoiden institutionaalinen ympäristö? (2) Mitkä ovat merkittävimmät puuttuvat markkinarakenteet Pohjois-Koreassa? (3) Mitä mahdollisuuksia ulkomaisilla yrityksillä voisi olla reagoida puuttuviin markkinarakenteisiin? Tutkimus toteutettiin kvalitatiivisena, koska tutkimuskysymys on deskriptiivinen. Aineisto kerättiin asiantuntijahaastattelun ja kvalitatiivisen sisällönanalyysin keinoin. Primääriaineiston muodostavat 2 asiantuntijahaastattelua ja sekundääriaineiston muodostavat 95 artikkelia, jotka kerättiin 40 lähteestä. Aineisto analysoitiin kvalitatiivisen sisällönanalyysin keinoin. Aineisto koodattiin, luokiteltiin ja esitettiin kokonaisuuksina luokittelurungon avulla, joka laadittiin tutkimusta varten muodostetun teoreettisen viitekehyksen mukaan. Tulokset ja johtopäätökset voidaan tiivistää seuraavasti. (1) Pohjois-Korean markkinan instituutioihin vaikuttaa kaksoisrakenne, jossa muodollinen, sosialistinen rakenne ja epämuodollinen, markkinalähtöinen rakenne toimivat päällekkäin. (2) Puuttuvia rakenteita on sekä markkinan kontekstissa että markkinatasolla. Puutteet ovat osittain seurausta vanhojen rakenteiden korvaantumisesta uusilla, jotka eivät ole institutionalisoituneet. (3) Yritykset voivat käyttää samoja mahdollisuuuksia reagoida puuttuviin markkinarakenteisiin Pohjois-Koreassa, joita kehittyvien markkinoiden yhteydessä on esitetty. Sen tulkittiin vähentävän käsitystä, jonka mukaan Pohjois-Korean markkina on liian erikoinen yritystoiminnalle. (4) Kasvava keskiluokka sekä yrittäjyyden ja naisten yhä merkittävämpi rooli liike-elämässä aiheuttavat alhaalta ylöspäin suuntautuvaa kehitystä markkinoilla. Nämä ovat merkkejä viimeaikaisesta kehityksestä, jotka eivät ole saaneet laajaa huomiota länsimaisessa mediassa. Se korostaa tarvetta liiketaloudelliselle, ajantasaiselle jatkotutkimukselle Pohjois-Korean markkinoista.

Institutional environment in North Korea: market structure and overcoming institutional voids

The research was sparked by an exchange in South Korea, as the author identified a gap in research that provides economic, up to date and realistic information about the North Korean market in English language. A need for a research was identified that would describe the market’s existing and missing market structures and explore possibilities to overcome the missing market structures. Institutional theory was chosen as a suitable framework to describe and explore the market. The research question was formulated as follows: “How can foreign companies overcome institutional voids in the North Korean market?”. To answer the research question, it was divided into three sub-questions as follows: (1) What is the institutional environment in North Korea like? (2) What are the major institutional voids in the North Korean market? (3) What possibilities do foreign companies have to overcome institutional voids? The research is qualitative by nature due to the descriptive and exploratory nature of the research question. Data collection consisted of expert interview and content analysis, resulting in primary data of two interviews and secondary data of 95 articles from 40 different sources. The data was analyzed with the systematical technique of content analysis. The data was coded, classified and presented as concepts with the help of a classification system that was build following the theoretical framework adapted for this study. The findings can be summarized as follows. (1) The market institutions are characterized by an overlapping dual system of formal, socialist structures and informal, market-oriented structures. (2) Institutional voids prevail on both the market’s contextual and on the market level. They are partly result of old institutions being replaced by new institutions that lack institutionalization. (3) Identified possibilities to overcome institutional voids correspond with possibilities drawn from previous research. This decreases the image of North Korea as an impossibly unique market to operate in. (4) Emerging middle class, rapidly growing entrepreneurial activities and women’s increasing role in business drive a down-to-up change in the market. This signals the recent development of the market, yet has been overlooked in the Western media. Thus there is a need for further economic, up to date research concerning North Korea.

17/5/20, procès du général Fournier, le général Legrand défenseur technique [procès de la reddition de Maubeuge] : [photographie de presse] / [Agence Rol]

Référence bibliographique : Rol, 59323

Confocal and two-photon microscopy : image enhancement /

Confocal and two-photon microcopy have become essential tools in biological research and today many investigations are not possible without their help. The valuable advantage that these two techniques offer is the ability of optical sectioning. Optical sectioning makes it possible to obtain 3D visuahzation of the structiu-es, and hence, valuable information of the structural relationships, the geometrical, and the morphological aspects of the specimen. The achievable lateral and axial resolutions by confocal and two-photon microscopy, similar to other optical imaging systems, are both defined by the diffraction theorem. Any aberration and imperfection present during the imaging results in broadening of the calculated theoretical resolution, blurring, geometrical distortions in the acquired images that interfere with the analysis of the structures, and lower the collected fluorescence from the specimen. The aberrations may have different causes and they can be classified by their sources such as specimen-induced aberrations, optics-induced aberrations, illumination aberrations, and misalignment aberrations. This thesis presents an investigation and study of image enhancement. The goal of this thesis was approached in two different directions. Initially, we investigated the sources of the imperfections. We propose methods to eliminate or minimize aberrations introduced during the image acquisition by optimizing the acquisition conditions. The impact on the resolution as a result of using a coverslip the thickness of which is mismatched with the one that the objective lens is designed for was shown and a novel technique was introduced in order to define the proper value on the correction collar of the lens. The amoimt of spherical aberration with regard to t he numerical aperture of the objective lens was investigated and it was shown that, based on the purpose of our imaging tasks, different numerical apertures must be used. The deformed beam cross section of the single-photon excitation source was corrected and the enhancement of the resolution and image quaUty was shown. Furthermore, the dependency of the scattered light on the excitation wavelength was shown empirically. In the second part, we continued the study of the image enhancement process by deconvolution techniques. Although deconvolution algorithms are used widely to improve the quality of the images, how well a deconvolution algorithm responds highly depends on the point spread function (PSF) of the imaging system applied to the algorithm and the level of its accuracy. We investigated approaches that can be done in order to obtain more precise PSF. Novel methods to improve the pattern of the PSF and reduce the noise are proposed. Furthermore, multiple soiu'ces to extract the PSFs of the imaging system are introduced and the empirical deconvolution results by using each of these PSFs are compared together. The results confirm that a greater improvement attained by applying the in situ PSF during the deconvolution process.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and determinis- tic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel meta–heuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS meta–heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Mesure du Progres Technique: Theories et Methodes

Ce Texte Constitue un Survol des Differentes Approches Destines a Mesurer le Progres Technique. Nous Utilisons une Notation Uniforme Tout au Long des Demonstrations Mathematiques et Nous Faisons Ressortir les Hypotheses Qui Rendent L'application des Methodes Proposees Envisageable et Qui En Limitent la Portee. les Diverses Approches Sont Regroupees D'apres une Classification Suggeree Par Diewert (1981) Selon Laquelle Deux Groupes Sont a Distinguer. le Premier Groupe Contient Toutes les Methodes Definissant le Progres Technique Comme le Taux de Croissance D'un Indice des Outputs Divise Par un Indice des Inputs (Approche de Divisia). L'autre Groupe Inclut Toutes les Methodes Definissant le Progres Technique Comme Etant le Deplacement D'une Fonction Representant la Technologie (Production, Cout, Distance). Ce Second Groupe Est Subdivise Entre L'approche Econometrique,La Theorie des Nombres Indices et L 'Approche Non Parametrique. une Liste des Pricipaux Economistes a Qui L'on Doit les Diverses Approches Est Fournie. Cependant Ce Survol Est Suffisamment Detaille Pour Etre Lu Sans Se Referer aux Articles Originaux.