Region-dependent vehicle classification using PCA features

Video-based vehicle detection is the focus of increasing interest due to its potential towards collision avoidance. In particular, vehicle verification is especially challenging due to the enormous variability of vehicles in size, color, pose, etc. In this paper, a new approach based on supervised learning using Principal Component Analysis (PCA) is proposed that addresses the main limitations of existing methods. Namely, in contrast to classical approaches which train a single classifier regardless of the relative position of the candidate (thus ignoring valuable pose information), a region-dependent analysis is performed by considering four different areas. In addition, a study on the evolution of the classification performance according to the dimensionality of the principal subspace is carried out using PCA features within a SVM-based classification scheme. Indeed, the experiments performed on a publicly available database prove that PCA dimensionality requirements are region-dependent. Hence, in this work, the optimal configuration is adapted to each of them, rendering very good vehicle verification results.

Bayesian network modeling of the consensus between experts: an application to neuron classification

Neuronal morphology is hugely variable across brain regions and species, and their classification strategies are a matter of intense debate in neuroscience. GABAergic cortical interneurons have been a challenge because it is difficult to find a set of morphological properties which clearly define neuronal types. A group of 48 neuroscience experts around the world were asked to classify a set of 320 cortical GABAergic interneurons according to the main features of their three-dimensional morphological reconstructions. A methodology for building a model which captures the opinions of all the experts was proposed. First, one Bayesian network was learned for each expert, and we proposed an algorithm for clustering Bayesian networks corresponding to experts with similar behaviors. Then, a Bayesian network which represents the opinions of each group of experts was induced. Finally, a consensus Bayesian multinet which models the opinions of the whole group of experts was built. A thorough analysis of the consensus model identified different behaviors between the experts when classifying the interneurons in the experiment. A set of characterizing morphological traits for the neuronal types was defined by performing inference in the Bayesian multinet. These findings were used to validate the model and to gain some insights into neuron morphology.

Contextual visual localization: cascaded submap classification, optimized saliency detection, and fast view matching

In this paper, we present a novel coarse-to-fine visual localization approach: contextual visual localization. This approach relies on three elements: (i) a minimal-complexity classifier for performing fast coarse localization (submap classification); (ii) an optimized saliency detector which exploits the visual statistics of the submap; and (iii) a fast view-matching algorithm which filters initial matchings with a structural criterion. The latter algorithm yields fine localization. Our experiments show that these elements have been successfully integrated for solving the global localization problem. Context, that is, the awareness of being in a particular submap, is defined by a supervised classifier tuned for a minimal set of features. Visual context is exploited both for tuning (optimizing) the saliency detection process, and to select potential matching views in the visual database, close enough to the query view.

Evaluation of an integrated system for classification, assessment and comparison of services for long-term care in Europe: the eDESDE-LTC study

Background: The harmonization of European health systems brings with it a need for tools to allow the standardized collection of information about medical care. A common coding system and standards for the description of services are needed to allow local data to be incorporated into evidence-informed policy, and to permit equity and mobility to be assessed. The aim of this project has been to design such a classification and a related tool for the coding of services for Long Term Care (DESDE-LTC), based on the European Service Mapping Schedule (ESMS). Methods: The development of DESDE-LTC followed an iterative process using nominal groups in 6 European countries. 54 researchers and stakeholders in health and social services contributed to this process. In order to classify services, we use the minimal organization unit or “Basic Stable Input of Care” (BSIC), coded by its principal function or “Main Type of Care” (MTC). The evaluation of the tool included an analysis of feasibility, consistency, ontology, inter-rater reliability, Boolean Factor Analysis, and a preliminary impact analysis (screening, scoping and appraisal). Results: DESDE-LTC includes an alpha-numerical coding system, a glossary and an assessment instrument for mapping and counting LTC. It shows high feasibility, consistency, inter-rater reliability and face, content and construct validity. DESDE-LTC is ontologically consistent. It is regarded by experts as useful and relevant for evidence-informed decision making. Conclusion: DESDE-LTC contributes to establishing a common terminology, taxonomy and coding of LTC services in a European context, and a standard procedure for data collection and international comparison.

Measuring the Social Identity of Entrepreneurs: Scale Development and International Validation

Recent research indicates that social identity theory offers an important lens to improve our understanding of founders as enterprising individuals, the venture creation process, and its outcomes. Yet, further advances are hindered by the lack of a valid scale that could be used to measure founders' social identities - a problem that is particularly severe because social identity is a multidimensional construct that needs to be assessed properly so that organizational phenomena can be understood. Drawing on social identity theory and the systematic classification of founders' social identities (Darwinians, Communitarians, Missionaries) provided in Fauchart and Gruber (2011), this study develops and empirically validates a 12-item scale that allows scholars to capture the multidimensional nature of social identities of entrepreneurs. Our validation tests are unusually comprehensive and solid, as we not only validate the developed scale in the Alpine region (where it was originally conceived), but also in 12 additional countries and the Anglo-American region. Scholars can use the scale to identify founders' social identities and to relate these identities to micro-level processes and outcomes in new firm creation. Scholars may also link founders' social identities to other levels of analysis such as industries (e.g., industry evolution) or whole economies (e.g., economic growth).

Measuring the Social Identity of Entrepreneurs: Scale Development and International Validation

Social identity theory offers an important lens to improve understanding of founders as enterprising individuals, the venture creation process, and its outcomes. Yet, further advances are hindered by the lack of valid scales to measure founders’ social identities. Drawing on social identity theory and a systematic classification of founders’ social identities (Darwinians, Communitarians, and Missionaries), we develop and test a corresponding 15-item scale in the Alpine region and validate it in 13 additional countries and regions. The scale allows identifying founders’ social identities and relating them to processes and outcomes in entrepreneurship. The scale is available online in 15 languages.

International catalogue of scientific literature.

A schedule of classification and index (in English, French, German and Italian), an author catalogue and a closely classified subject catalogue.

... International exhibition, 1876. [Reports]

v.1. Report of the director-general, A.T. Goshorn. Reports of the bureaus of administration : Installation of Henry Pettit. Art by John Sartain. Machinery by John S. Albert. Agriculture by Burnet Landreth. Horticulture by Charles H. Miller. Engineering by H.J. Schwarzmann. Transportation by Dolphus Torrey. Admissions by David G. Yates. Awards by Francis A. Walker. Classification and catalogue by Dorsey Gardner. Medical service by William Pepper. Protection by A.T. Goshorn. Fire by Atwood Smith. Water-supply by Frederick Graff. Telegraphy by William J. Phillips.--v.2. Reports of the president, secretary, and Executive committee. Together with the Journal of the final session of the commission.--v.3-8. Reports and awards. Groups I-XXXVI and collective exhibits. Ed. by Francis A. Walker.--v.9. Grounds and buildings of the Centennial exhibition... Ed. by Dorsey Gardner.--v.10-11. Report of the Board on behalf of the United States executive departments.

Manual of the international list of causes of death as adopted for use in the United States, based on the fifth decennial revision by the International commission, Paris, October 3-7, 1938. Manual of joint causes of death.

"Prepared by Theodore A. Janssen, chief of the Nosology section."--p.1.

Great Lakes register for the construction and classification of steel and wooden vessels.

Supplements were issued with several of the numbers.

Ecoregion classification using a bayesian approach and centre-focused clusters

Ecological regions are increasingly used as a spatial unit for planning and environmental management. It is important to define these regions in a scientifically defensible way to justify any decisions made on the basis that they are representative of broad environmental assets. The paper describes a methodology and tool to identify cohesive bioregions. The methodology applies an elicitation process to obtain geographical descriptions for bioregions, each of these is transformed into a Normal density estimate on environmental variables within that region. This prior information is balanced with data classification of environmental datasets using a Bayesian statistical modelling approach to objectively map ecological regions. The method is called model-based clustering as it fits a Normal mixture model to the clusters associated with regions, and it addresses issues of uncertainty in environmental datasets due to overlapping clusters.