Classification of textures seen from different distances and under varying illumination direction

Changes in the angle of illumination incident upon a 3D surface texture can significantly alter its appearance, implying variations in the image texture. These texture variations produce displacements of class members in the feature space, increasing the failure rates of texture classifiers. To avoid this problem, a model-based texture recognition system which classifies textures seen from different distances and under different illumination directions is presented in this paper. The system works on the basis of a surface model obtained by means of 4-source colour photometric stereo, used to generate 2D image textures under different illumination directions. The recognition system combines coocurrence matrices for feature extraction with a Nearest Neighbour classifier. Moreover, the recognition allows one to guess the approximate direction of the illumination used to capture the test image

A new approach to the classification of mammographic masses and normal breast tissue

A new approach to mammographic mass detection is presented in this paper. Although different algorithms have been proposed for such a task, most of them are application dependent. In contrast, our approach makes use of a kindred topic in computer vision adapted to our particular problem. In this sense, we translate the eigenfaces approach for face detection/classification problems to a mass detection. Two different databases were used to show the robustness of the approach. The first one consisted on a set of 160 regions of interest (RoIs) extracted from the MIAS database, being 40 of them with confirmed masses and the rest normal tissue. The second set of RoIs was extracted from the DDSM database, and contained 196 RoIs containing masses and 392 with normal, but suspicious regions. Initial results demonstrate the feasibility of using such approach with performances comparable to other algorithms, with the advantage of being a more general, simple and cost-effective approach

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

A Method for Sky-Condition Classification from Ground-Based Solar Radiation Measurements

Identification of clouds from satellite images is now a routine task. Observation of clouds from the ground, however, is still needed to acquire a complete description of cloud conditions. Among the standard meteorologicalvariables, solar radiation is the most affected by cloud cover. In this note, a method for using global and diffuse solar radiation data to classify sky conditions into several classes is suggested. A classical maximum-likelihood method is applied for clustering data. The method is applied to a series of four years of solar radiation data and human cloud observations at a site in Catalonia, Spain. With these data, the accuracy of the solar radiation method as compared with human observations is 45% when nine classes of sky conditions are to be distinguished, and it grows significantly to almost 60% when samples are classified in only five different classes. Most errors are explained by limitations in the database; therefore, further work is under way with a more suitable database

Optimizing of intelligence level in welding

The productivity, quality and cost efficiency of welding work are critical for metal industry today. Welding processes must get more effective and this can be done by mechanization and automation. Those systems are always expensive and they have to pay the investment back. In this case it is really important to optimize the needed intelligence and this way needed automation level, so that a company will get the best profit. This intelligence and automation level was earlier classified in several different ways which are not useful for optimizing the process of automation or mechanization of welding. In this study the intelligence of a welding system is defined in a new way to enable the welding system to produce a weld good enough. In this study a new way is developed to classify and select the internal intelligence level of a welding system needed to produce the weld efficiently. This classification contains the possible need of human work and its effect to the weld and its quality but does not exclude any different welding processes or methods. In this study a totally new way is developed to calculate the best optimization for the needed intelligence level in welding. The target of this optimization is the best possible productivity and quality and still an economically optimized solution for several different cases. This new optimizing method is based on grounds of product type, economical productivity, the batch size of products, quality and criteria of usage. Intelligence classification and optimization were never earlier made by grounds of a made product. Now it is possible to find the best type of welding system needed to welddifferent types of products. This calculation process is a universal way for optimizing needed automation or mechanization level when improving productivity of welding. This study helps the industry to improve productivity, quality and cost efficiency of welding workshops.

Protestant Churches and the making of the Universal Declaration of Human Rights

Kirjallisuusarvostelu

RT-PCR and Dot Blot hybridization methods for a universal detection of tospoviruses

Transcriptase reverse - polymerase chain reaction (RT-PCR) and dot blot hybridization with digoxigenin-labeled probes were applied for the universal detection of Tospovirus species. The virus species tested were Tomato spotted wilt virus, Tomato chlorotic spot virus, Groundnut ringspot virus, Chrysanthemum stem necrosis virus, Impatiens necrotic spot virus, Zucchini lethal chlorosis virus, Iris yellow spot virus. Primers for PCR amplification were designed to match conserved regions of the tospovirus genome. RT-PCR using distinct primer combinations was unable to simultaneously amplify all tospovirus species and consistently failed to detect ZLCV and IYSV in total RNA extracts. However, all tospovirus species were detected by RT-PCR when viral RNA was used as template. RNA-specific PCR products were used as probes for dot hybridization. This assay with a M probe (directed to the G1/G2 gene) detected at low stringency conditions all Tospovirus species, except IYSV. At low stringency conditions, the L non-radioactive probe detected the seven Tospovirus species in a single assay. This method for broad spectrum detection can be potentially employed in quarantine services for indexing in vitro germplasm.

Identification of Races of Selected Isolates of Uromyces appendiculatus from Minas Gerais (Brazil) Based on the new international classification system

Twelve single-pustule isolates of Uromyces appendiculatus, the etiological agent of common bean rust, were collected in the state of Minas Gerais, Brazil, and classified according to the new international differential series and the binary nomenclature system proposed during the 3rd Bean Rust Workshop. These isolates have been used to select rust-resistant genotypes in a bean breeding program conducted by our group. The twelve isolates were classified into seven different physiological races: 21-3, 29-3, 53-3, 53-19, 61-3, 63-3 and 63-19. Races 61-3 and 63-3 were the most frequent in the area. They were represented by five and two isolates, respectively. The other races were represented by just one isolate. This is the first time the new international classification procedure has been used for U. appendiculatus physiological races in Brazil. The general adoption of this system will facilitate information exchange, allowing the cooperative use of the results obtained by different research groups throughout the world. The differential cultivars Mexico 309, Mexico 235 and PI 181996 showed resistance to all of the isolates that were characterized. It is suggested that these cultivars should be preferentially used as sources for resistance to rust in breeding programs targeting development lines adapted to the state of Minas Gerais.

A Survey and Classification of Software Testing Tools

Software testing is one of the essential parts in software engineering process. The objective of the study was to describe software testing tools and the corresponding use. The thesis contains examples of software testing tools usage. The study was conducted as a literature study, with focus on current software testing practices and quality assurance standards. In the paper a tool classifier was employed, and testing tools presented in study were classified according to it. We found that it is difficult to distinguish current available tools by certain testing activities as many of them contain functionality that exceeds scopes of a single testing type.

Financier’s credit risk: A Performance-Based Classification Model

The purpose of this study is to view credit risk from the financier’s point of view in a theoretical framework. Results and aspects of the previous studies regarding measuring credit risk with accounting based scoring models are also examined. The theoretical framework and previous studies are then used to support the empirical analysis which aims to develop a credit risk measure for a bank’s internal use or a risk management tool for a company to indicate its credit risk to the financier. The study covers a sample of Finnish companies from 12 different industries and four different company categories and employs their accounting information from 2004 to 2008. The empirical analysis consists of six stage methodology process which uses measures of profitability, liquidity, capital structure and cash flow to determine financier’s credit risk, define five significant risk classes and produce risk classification model. The study is confidential until 15.10.2012.

Sobre o princípio e a lei universal do Direito em Kant

O artigo distingue duas fórmulas do princípio do Direito em Kant; mostra que na primeira delas o Princípio Universal do Direito é formulado como um principium diiudicationis e na segunda a Lei Universal do Direito como um principium executionis das ações conforme ao Direito; examina as dificuldades suscitadas para ambas as formulações, quais sejam, a base para a definição do que é direito e a questão se as leis jurídicas têm e, caso tenham, qual é a sua força prescritiva; e, finalmente, propõe uma solução baseada na consideração de que as leis jurídicas constituem para Kant uma subclasse das leis morais e se baseiam no conceito de uma autorização ou faculdade moral de fazer o que é moralmente lícito ou obrigatório e de não fazer o que é moralmente proibido.