An evaluation of the pedestrian classification in a multi-domain multi-modality setup

The objective of this article is to study the problem of pedestrian classification across different light spectrum domains (visible and far-infrared (FIR)) and modalities (intensity, depth and motion). In recent years, there has been a number of approaches for classifying and detecting pedestrians in both FIR and visible images, but the methods are difficult to compare, because either the datasets are not publicly available or they do not offer a comparison between the two domains. Our two primary contributions are the following: (1) we propose a public dataset, named RIFIR , containing both FIR and visible images collected in an urban environment from a moving vehicle during daytime; and (2) we compare the state-of-the-art features in a multi-modality setup: intensity, depth and flow, in far-infrared over visible domains. The experiments show that features families, intensity self-similarity (ISS), local binary patterns (LBP), local gradient patterns (LGP) and histogram of oriented gradients (HOG), computed from FIR and visible domains are highly complementary, but their relative performance varies across different modalities. In our experiments, the FIR domain has proven superior to the visible one for the task of pedestrian classification, but the overall best results are obtained by a multi-domain multi-modality multi-feature fusion.

Benchmark Evaluation of HOG Descriptors as Features for Classification of Traffic Signs

The purpose of this paper is to analyze the performance of the Histograms of Oriented Gradients (HOG) as descriptors for traffic signs recognition. The test dataset consists of speed limit traffic signs because of their high inter-class similarities.   HOG features of speed limit signs, which were extracted from different traffic scenes, were computed and a Gentle AdaBoost classifier was invoked to evaluate the different features. The performance of HOG was tested with a dataset consisting of 1727 Swedish speed signs images. Different numbers of HOG features per descriptor, ranging from 36 features up 396 features, were computed for each traffic sign in the benchmark testing. The results show that HOG features perform high classification rate as the Gentle AdaBoost classification rate was 99.42%, and they are suitable to real time traffic sign recognition. However, it is found that changing the number of orientation bins has insignificant effect on the classification rate. In addition to this, HOG descriptors are not robust with respect to sign orientation.

A Methodology for Building Service-Oriented Applications in the Presence of Non-Functional Properties

This thesis presents ⇡SOD-M (Policy-based Service Oriented Development Methodology), a methodology for modeling reliable service-based applications using policies. It proposes a model driven method with: (i) a set of meta-models for representing non-functional constraints associated to service-based applications, starting from an use case model until a service composition model; (ii) a platform providing guidelines for expressing the composition and the policies; (iii) model-to-model and model-to-text transformation rules for semi-automatizing the implementation of reliable service-based applications; and (iv) an environment that implements these meta-models and rules, and enables the application of ⇡SOD-M. This thesis also presents a classification and nomenclature for non-functional requirements for developing service-oriented applications. Our approach is intended to add value to the development of service-oriented applications that have quality requirements needs. This work uses concepts from the service-oriented development, non-functional requirements design and model-driven delevopment areas to propose a solution that minimizes the problem of reliable service modeling. Some examples are developed as proof of concepts

A comparison of pixel and object-based land cover classification. A case study of the Asmara region, Eritrea

In this paper we compare the performance of two image classification paradigms (object- and pixel-based) for creating a land cover map of Asmara, the capital of Eritrea and its surrounding areas using a Landsat ETM+ imagery acquired in January 2000. The image classification methods used were maximum likelihood for the pixel-based approach and Bhattacharyya distance for the object-oriented approach available in, respectively, ArcGIS and SPRING software packages. Advantages and limitations of both approaches are presented and discussed. Classifications outputs were assessed using overall accuracy and Kappa indices. Pixel- and object-based classification methods result in an overall accuracy of 78% and 85%, respectively. The Kappa coefficient for pixel- and object-based approaches was 0.74 and 0.82, respectively. Although pixel-based approach is the most commonly used method, assessment and visual interpretation of the results clearly reveal that the object-oriented approach has advantages for this specific case-study.

Land Cover Mapping by an Optimized Object-Oriented Approach. Case of Study Mediterranean Landscapes

Remote sensing information from spaceborne and airborne platforms continues to provide valuable data for different environmental monitoring applications. In this sense, high spatial resolution im-agery is an important source of information for land cover mapping. For the processing of high spa-tial resolution images, the object-based methodology is one of the most commonly used strategies. However, conventional pixel-based methods, which only use spectral information for land cover classification, are inadequate for classifying this type of images. This research presents a method-ology to characterise Mediterranean land covers in high resolution aerial images by means of an object-oriented approach. It uses a self-calibrating multi-band region growing approach optimised by pre-processing the image with a bilateral filtering. The obtained results show promise in terms of both segmentation quality and computational efficiency.

Histograms of oriented gradients for fast on-board vehicle verification

Histograms of Oriented Gradients (HoGs) provide excellent results in object detection and verification. However, their demanding processing requirements bound their applicability in some critical real-time scenarios, such as for video-based on-board vehicle detection systems. In this work, an efficient HOG configuration for pose-based on-board vehicle verification is proposed, which alleviates both the processing requirements and required feature vector length without reducing classification performance. The impact on classification of some critical configuration and processing parameters is in depth analyzed to propose a baseline efficient descriptor. Based on the analysis of its cells contribution to classification, new view-dependent cell-configuration patterns are proposed, resulting in reduced descriptors which provide an excellent balance between performance and computational requirements, rendering higher verification rates than other works in the literature.

Internal molecular motions of bacteriorhodopsin: hydration-induced flexibility studied by quasielastic incoherent neutron scattering using oriented purple membranes.

Quasielastic incoherent neutron scattering from hydrogen atoms, which are distributed nearly homogeneously in biological molecules, allows the investigation of diffusive motions occurring on the pico- to nanosecond time scale. A quasielastic incoherent neutron scattering study was performed on the integral membrane protein bacteriorhodopsin (BR), which is a light-driven proton pump in Halobacterium salinarium. BR is embedded in lipids, forming patches in the cell membrane of the organism, which are the so called purple membranes (PMs). Measurements were carried out at room temperature on oriented PM-stacks hydrated at two different levels (low hydration, h = 0.03 g of D2O per g of PM; high hydration, h = 0.28 g of D2O per g of PM) using time-of-flight spectrometers. From the measured spectra, different diffusive components were identified and analyzed with respect to the influence of hydration. This study supports the idea that a decrease in hydration results in an appreciable decrease in internal molecular flexibility of the protein structure. Because it is known from studies on the function of BR that the pump activity is reduced if the hydration level of the protein is insufficient, we conclude that the observed diffusive motions are essential for the function of this protein. A detailed analysis and classification of the different kinds of diffusive motions, predominantly occurring in PMs under physiological conditions, is presented.

A classification of concurrency failures in java components

The Java programming language supports concurrency. Concurrent programs are hard to test due to their inherent non-determinism. This paper presents a classification of concurrency failures that is based on a model of Java concurrency. The model and failure classification is used to justify coverage of synchronization primitives of concurrent components. This is achieved by constructing concurrency flow graphs for each method call. A producer-consumer monitor is used to demonstrate how the approach can be used to measure coverage of concurrency primitives and thereby assist in determining test sequences for deterministic execution.

Blood type classification using computer vision and machine learning

In emergency situations, where time for blood transfusion is reduced, the O negative blood type (the universal donor) is administrated. However, sometimes even the universal donor can cause transfusion reactions that can be fatal to the patient. As commercial systems do not allow fast results and are not suitable for emergency situations, this paper presents the steps considered for the development and validation of a prototype, able to determine blood type compatibilities, even in emergency situations. Thus it is possible, using the developed system, to administer a compatible blood type, since the first blood unit transfused. In order to increase the system’s reliability, this prototype uses different approaches to classify blood types, the first of which is based on Decision Trees and the second one based on support vector machines. The features used to evaluate these classifiers are the standard deviation values, histogram, Histogram of Oriented Gradients and fast Fourier transform, computed on different regions of interest. The main characteristics of the presented prototype are small size, lightweight, easy transportation, ease of use, fast results, high reliability and low cost. These features are perfectly suited for emergency scenarios, where the prototype is expected to be used.

A Knowledge-Oriented Technology of System-Objective Analysis and Modelling of Business-Systems

A new original method and CASE-tool of system analysis and modelling are represented. They are for the first time consistent with the requirements of object-oriented technology of informational systems design. They essentially facilitate the construction of organisational systems models and increase the quality of the organisational designing and basic technological processes of object application developing.

Classification-Based Method of Linear Multicriteria Optimization

The paper describes a classification-based learning-oriented interactive method for solving linear multicriteria optimization problems. The method allows the decision makers describe their preferences with greater flexibility, accuracy and reliability. The method is realized in an experimental software system supporting the solution of multicriteria optimization problems.

Generation of Testing Source Code for Object-Oriented Programs

Красимир Манев, Антон Желязков, Станимир Бойчев - В статията е представена имплементацията на последната фаза на автоматичен генератор на тестови данни за структурно тестване на софтуер, написан на обектно-ориентиран език за програмиране – генерирането на изходен код на тестващия модул. Някои детайли от имплементацията на останалите фази, които са важни за имплементацията на последната фаза, са представени първо. След това е описан и алгоритъмът за генериране на кода на тестващия модул.

Application of Service-Oriented Architecture in Software Quality Management

ACM Computing Classification System (1998): D.2.5, D.2.9, D.2.11.

Classification multi-échelle d'images à très haute résolution spatiale basée sur une nouvelle approche texturale

Résumé : Face à l’accroissement de la résolution spatiale des capteurs optiques satellitaires, de nouvelles stratégies doivent être développées pour classifier les images de télédétection. En effet, l’abondance de détails dans ces images diminue fortement l’efficacité des classifications spectrales; de nombreuses méthodes de classification texturale, notamment les approches statistiques, ne sont plus adaptées. À l’inverse, les approches structurelles offrent une ouverture intéressante : ces approches orientées objet consistent à étudier la structure de l’image pour en interpréter le sens. Un algorithme de ce type est proposé dans la première partie de cette thèse. Reposant sur la détection et l’analyse de points-clés (KPC : KeyPoint-based Classification), il offre une solution efficace au problème de la classification d’images à très haute résolution spatiale. Les classifications effectuées sur les données montrent en particulier sa capacité à différencier des textures visuellement similaires. Par ailleurs, il a été montré dans la littérature que la fusion évidentielle, reposant sur la théorie de Dempster-Shafer, est tout à fait adaptée aux images de télédétection en raison de son aptitude à intégrer des concepts tels que l’ambiguïté et l’incertitude. Peu d’études ont en revanche été menées sur l’application de cette théorie à des données texturales complexes telles que celles issues de classifications structurelles. La seconde partie de cette thèse vise à combler ce manque, en s’intéressant à la fusion de classifications KPC multi-échelle par la théorie de Dempster-Shafer. Les tests menés montrent que cette approche multi-échelle permet d’améliorer la classification finale dans le cas où l’image initiale est de faible qualité. De plus, l’étude effectuée met en évidence le potentiel d’amélioration apporté par l’estimation de la fiabilité des classifications intermédiaires, et fournit des pistes pour mener ces estimations.

Climatic classification: the semi-arid tropics and its environment a review.

This paper analyses the advantages and limitations in using the Troll, Hargreaves and modified Thornthwaite approaches for the demarcation of the semi-arid tropics. Data from India, Africa, Brazil, Australia and Thailand, were used for the comparison of these three methods. The modified Thornthwaite approach provided the most relevant agriculturally oriented demarcation of the semi-arid tropics. This method in not only simple, tut uses input data that are avaliable for a global network of stations. Using this method the semi-arid tropics include major dryland or rainfed agricultural zones with annual rainfall varying from about 400 to 1,250 mm. Major dryland crops are pearl millet, sorghum, pigeonpea and groundnut. This paper also presents the brief description of climate, soils and farming systems of the semi-arid tropics.