The goal of this thesis is to develop an algorithm to process infrared images and achieve automatic identification of moving subjects with fever. The identification is based on two main features: the distinction between the geometry of a human face and other objects in the field of view of the camera, and the temperature of the radiating object. Infrared thermography is a remote sensing technique used to measure temperatures based on emitted infrared radiation. Applications include fever screening in major public places such as airports and hospitals. Current accepted practice of screening requires people to stay in a line and temperature measurements are carried out for one person at a time. However in the case of mass screening of moving people the accuracy of the measurements is still under investigation. An algorithm constituting of image processing to threshold objects based on the temperature, template matching and hypothesis testing is proposed to achieve automatic identification of fever subjects. The algorithm was first tested on training data to obtain a threshold value (used to discriminate between face and non face shapes) corresponding to a false detection rate of 5%, which in turn corresponds to 85% probability of detection using Neyman-Pearson criterion. By testing the algorithm on several simulated and experimental images (which reflect relevant scenarios characterizing crowded places) it is observed that it can be beneficially implemented to introduce automation in the process of detecting moving subjects with fever.

The goal of this thesis is to develop an algorithm to process infrared images and achieve automatic identification of moving subjects with fever. The identification is based on two main features: the distinction between the geometry of a human face and other objects in the field of view of the camera, and the temperature of the radiating object. Infrared thermography is a remote sensing technique used to measure temperatures based on emitted infrared radiation. Applications include fever screening in major public places such as airports and hospitals. Current accepted practice of screening requires people to stay in a line and temperature measurements are carried out for one person at a time. However in the case of mass screening of moving people the accuracy of the measurements is still under investigation. An algorithm constituting of image processing to threshold objects based on the temperature, template matching and hypothesis testing is proposed to achieve automatic identification of fever subjects. The algorithm was first tested on training data to obtain a threshold value (used to discriminate between face and non face shapes) corresponding to a false detection rate of 5%, which in turn corresponds to 85% probability of detection using Neyman-Pearson criterion. By testing the algorithm on several simulated and experimental images (which reflect relevant scenarios characterizing crowded places) it is observed that it can be beneficially implemented to introduce automation in the process of detecting moving subjects with fever.

Large digital libraries typically contain large collections of heterogeneous resources intended to be delivered to a variety of user communities. One key challenge for these libraries is providing tight integration between resources both within a single collection and across the several collections of the library with out requiring hand coding. One key tool in doing this is elucidating the internal structure of the digital resources and using that structure to form connections between the resources. The heterogeneous nature of the collections and the diversity of the needs in the user communities complicates this task. Accordingly, in this thesis, I describe an approach to implementing a feature identification system to support digital collections that provides a general framework for applications while allowing decisions about the details of document representation and features identification to be deferred to domain specific implementations of that framework. These deferred decisions include details of the semantics and syntax of markup, the types of metadata to be attached to documents, the types of features to be identified, the feature identification algorithms to be applied, and which features should be indexed. This approach results in strong support for the general aspects of developing a feature identification system allowing future work to focus on the details of applying that system to the specific needs of individual collections and user communities.

The goal of this thesis is to develop an algorithm to process infrared images and achieve automatic identification of moving subjects with fever. The identification is based on two main features: the distinction between the geometry of a human face and other objects in the field of view of the camera, and the temperature of the radiating object. Infrared thermography is a remote sensing technique used to measure temperatures based on emitted infrared radiation. Applications include fever screening in major public places such as airports and hospitals. Current accepted practice of screening requires people to stay in a line and temperature measurements are carried out for one person at a time. However in the case of mass screening of moving people the accuracy of the measurements is still under investigation. An algorithm constituting of image processing to threshold objects based on the temperature, template matching and hypothesis testing is proposed to achieve automatic identification of fever subjects. The algorithm was first tested on training data to obtain a threshold value (used to discriminate between face and non face shapes) corresponding to a false detection rate of 5%, which in turn corresponds to 85% probability of detection using Neyman-Pearson criterion. By testing the algorithm on several simulated and experimental images (which reflect relevant scenarios characterizing crowded places) it is observed that it can be beneficially implemented to introduce automation in the process of detecting moving subjects with fever.

Made available in DSpace on 2014-12-17T14:56:08Z (GMT). No. of bitstreams: 1 XiankleberCB_DISSERT.pdf: 1439530 bytes, checksum: cc79fa3fc529cac979cdaba813d4af67 (MD5) Previous issue date: 2012-07-30 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / This work uses computer vision algorithms related to features in the identification of medicine boxes for the visually impaired. The system is for people who have a disease that compromises his vision, hindering the identification of the correct medicine to be ingested. We use the camera, available in several popular devices such as computers, televisions and phones, to identify the box of the correct medicine and audio through the image, showing the poor information about the medication, such: as the dosage, indication and contraindications of the medication. We utilize a model of object detection using algorithms to identify the features in the boxes of drugs and playing the audio at the time of detection of feauteres in those boxes. Experiments carried out with 15 people show that where 93 % think that the system is useful and very helpful in identifying drugs for boxes. So, it is necessary to make use of this technology to help several people with visual impairments to take the right medicine, at the time indicated in advance by the physician / Este trabalho utiliza algoritmos de vis?o computacional relacionados ?s features na identifica??o de caixas de medicamentos para deficientes visuais. O sistema ? para pessoas que apresentam alguma enfermidade que comprometa sua vis?o, prejudicando a identifica??o do medicamento correto a ser ingerido. Utilizamos a c?mera, dispon?vel em v?rios dispositivos populares como computadores, televisores e celulares, para identificar a caixa do medicamento correto atrav?s da imagem e ?udio, mostrando ao deficiente as informa??es sobre a medica??o, tais como: a posologia, indica??o e contra indica??es da medica??o. Para isso, utilizamos um modelo de detec??o de objetos, usando algoritmos, para identificar as features nas caixas dos medicamentos e tocando o ?udio na hora da detec??o das feauteres nas referidas caixas. Os experimentos realizados com 15 pessoas mostram que onde 93% acreditam que o sistema ? ?til e muito ?til para identificar os medicamentos pelas caixas. Portanto, torna-se necess?rio fazer uso dessa tecnologia para ajudar v?rias pessoas com defici?ncia visual a tomarem o medicamento certo, na hora indicada, previamente pelo m?dico

The goal of this thesis is to develop an algorithm to process infrared images and achieve automatic identification of moving subjects with fever. The identification is based on two main features: the distinction between the geometry of a human face and other objects in the field of view of the camera, and the temperature of the radiating object. Infrared thermography is a remote sensing technique used to measure temperatures based on emitted infrared radiation. Applications include fever screening in major public places such as airports and hospitals. Current accepted practice of screening requires people to stay in a line and temperature measurements are carried out for one person at a time. However in the case of mass screening of moving people the accuracy of the measurements is still under investigation. An algorithm constituting of image processing to threshold objects based on the temperature, template matching and hypothesis testing is proposed to achieve automatic identification of fever subjects. The algorithm was first tested on training data to obtain a threshold value (used to discriminate between face and non face shapes) corresponding to a false detection rate of 5%, which in turn corresponds to 85% probability of detection using Neyman-Pearson criterion. By testing the algorithm on several simulated and experimental images (which reflect relevant scenarios characterizing crowded places) it is observed that it can be beneficially implemented to introduce automation in the process of detecting moving subjects with fever.

Einzelzell-Omics-Daten stehen derzeit im Fokus der Entwicklung computergestützter Methoden in der Molekularbiologie und Genetik. Einzelzellexperimenten lieferen dünnbesetzte, hochdimensionale Daten über zehntausende Gene oder hunderttausende regulatorische Regionen in zehntausenden Zellen. Diese Daten bieten den Forschenden die Möglichkeit, Gene und regulatorische Regionen zu identifizieren, welche die Bestimmung und Aufrechterhaltung der Zellidentität koordinieren. Die gängigste Strategie zur Identifizierung von Zellidentitätsmarkern besteht darin, die Zellen zu clustern und dann Merkmale zu finden, welche die Cluster unterscheiden, wobei davon ausgegangen wird, dass die Zellen innerhalb eines Clusters die gleiche Identität haben. Diese Annahme ist jedoch nicht immer zutreffend, insbesondere nicht für Entwicklungsdaten bei denen sich die Zellen in einem Kontinuum befinden und die Definition von Clustergrenzen biologisch gesehen potenziell willkürlich ist. Daher befasst sich diese Dissertation mit Clustering-unabhängigen Strategien zur Identifizierung von Markern aus Einzelzell-Omics-Daten. Der wichtigste Beitrag dieser Dissertation ist SEMITONES, eine auf linearer Regression basierende Methode zur Identifizierung von Markern. SEMITONES identifiziert (Gruppen von) Markern aus verschiedenen Arten von Einzelzell-Omics-Daten, identifiziert neue Marker und übertrifft bestehende Marker-Identifizierungsansätze. Außerdem ermöglicht die Identifizierung von regulatorischen Markerregionen durch SEMITONES neue Hypothesen über die Regulierung der Genexpression während dem Erwerb der Zellidentität. Schließlich beschreibt die Dissertation einen Ansatz zur Identifizierung neuer Markergene für sehr ähnliche, dennoch underschiedliche neurale Vorlauferzellen im zentralen Nervensystem von Drosphila melanogaster. Ingesamt zeigt die Dissertation, wie Cluster-unabhängige Ansätze zur Aufklärung bisher uncharakterisierter biologischer Phänome aus Einzelzell-Omics-Daten beitragen. / Single-cell omics approaches are the current frontier of computational method development in molecular biology and genetics. A single single-cell experiment provides sparse, high-dimensional data on tens of thousands of genes or hundreds of thousands of regulatory regions (i.e. features) in tens of thousands of cells (i.e. samples). This data provides researchers with an unprecedented opportunity to identify those genes and regulatory regions that determine and coordinate cell identity acquisition and maintenance. The most common strategy for identifying cell identity markers consists of clustering the cells and then identifying differential features between these clusters, assuming that cells within a cluster share the same identity. This assumption is, however, not guaranteed to hold, particularly for developmental data where cells lie along a continuum and inferring cluster boundaries becomes non-trivial and potentially biologically arbitrary. In response, this thesis presents clustering-independent strategies for marker feature identification from single-cell omics data. The primary contribution of this thesis is a linear regression-based method for marker feature identification from single-cell omics data called SEMITONES. SEMITONES can identify markers or marker sets from diverse single-cell omics data types, identifies novel markers, outperforms existing marker identification approaches. The thesis also describes how the identification of marker regulatory regions by SEMITONES enables the generation of novel hypotheses regarding gene regulation during cell identity acquisition. Lastly, the thesis describes the clustering-independent identification of novel marker genes for highly similar yet distinct neural progenitor cells in the Drosophila melanogaster central nervous system. Altogether, the thesis demonstrates how clustering-independent approaches aid the elucidation of yet uncharacterised biological patterns from single cell-omics data.