A Trainable Object Detection System: Car Detection in Static Images

This paper describes a general, trainable architecture for object detection that has previously been applied to face and peoplesdetection with a new application to car detection in static images. Our technique is a learning based approach that uses a set of labeled training data from which an implicit model of an object class -- here, cars -- is learned. Instead of pixel representations that may be noisy and therefore not provide a compact representation for learning, our training images are transformed from pixel space to that of Haar wavelets that respond to local, oriented, multiscale intensity differences. These feature vectors are then used to train a support vector machine classifier. The detection of cars in images is an important step in applications such as traffic monitoring, driver assistance systems, and surveillance, among others. We show several examples of car detection on out-of-sample images and show an ROC curve that highlights the performance of our system.

Example Based Learning for View-Based Human Face Detection

We present an example-based learning approach for locating vertical frontal views of human faces in complex scenes. The technique models the distribution of human face patterns by means of a few view-based "face'' and "non-face'' prototype clusters. At each image location, the local pattern is matched against the distribution-based model, and a trained classifier determines, based on the local difference measurements, whether or not a human face exists at the current image location. We provide an analysis that helps identify the critical components of our system.

Verb Classes and Alternations in Bangla, German, English, and Korean

In this report, we investigate the relationship between the semantic and syntactic properties of verbs. Our work is based on the English Verb Classes and Alternations of (Levin, 1993). We explore how these classes are manifested in other languages, in particular, in Bangla, German, and Korean. Our report includes a survey and classification of several hundred verbs from these languages into the cross-linguistic equivalents of Levin's classes. We also explore ways in which our findings may be used to enhance WordNet in two ways: making the English syntactic information of WordNet more fine-grained, and making WordNet multilingual.

Object Detection in Images by Components

In this paper we present a component based person detection system that is capable of detecting frontal, rear and near side views of people, and partially occluded persons in cluttered scenes. The framework that is described here for people is easily applied to other objects as well. The motivation for developing a component based approach is two fold: first, to enhance the performance of person detection systems on frontal and rear views of people and second, to develop a framework that directly addresses the problem of detecting people who are partially occluded or whose body parts blend in with the background. The data classification is handled by several support vector machine classifiers arranged in two layers. This architecture is known as Adaptive Combination of Classifiers (ACC). The system performs very well and is capable of detecting people even when all components of a person are not found. The performance of the system is significantly better than a full body person detector designed along similar lines. This suggests that the improved performance is due to the components based approach and the ACC data classification structure.

Validating Co-Training Models for Web Image Classification

Co-training is a semi-supervised learning method that is designed to take advantage of the redundancy that is present when the object to be identified has multiple descriptions. Co-training is known to work well when the multiple descriptions are conditional independent given the class of the object. The presence of multiple descriptions of objects in the form of text, images, audio and video in multimedia applications appears to provide redundancy in the form that may be suitable for co-training. In this paper, we investigate the suitability of utilizing text and image data from the Web for co-training. We perform measurements to find indications of conditional independence in the texts and images obtained from the Web. Our measurements suggest that conditional independence is likely to be present in the data. Our experiments, within a relevance feedback framework to test whether a method that exploits the conditional independence outperforms methods that do not, also indicate that better performance can indeed be obtained by designing algorithms that exploit this form of the redundancy when it is present.

Modeling and Classifying Breast Tissue Density in Mammograms

We present a new approach to model and classify breast parenchymal tissue. Given a mammogram, first, we will discover the distribution of the different tissue densities in an unsupervised manner, and second, we will use this tissue distribution to perform the classification. We achieve this using a classifier based on local descriptors and probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature. We studied the influence of different descriptors like texture and SIFT features at the classification stage showing that textons outperform SIFT in all cases. Moreover we demonstrate that pLSA automatically extracts meaningful latent aspects generating a compact tissue representation based on their densities, useful for discriminating on mammogram classification. We show the results of tissue classification over the MIAS and DDSM datasets. We compare our method with approaches that classified these same datasets showing a better performance of our proposal

Use of decision trees in colour feature selection: application to object recognition in outdoor scenes

A new method for the automated selection of colour features is described. The algorithm consists of two stages of processing. In the first, a complete set of colour features is calculated for every object of interest in an image. In the second stage, each object is mapped into several n-dimensional feature spaces in order to select the feature set with the smallest variables able to discriminate the remaining objects. The evaluation of the discrimination power for each concrete subset of features is performed by means of decision trees composed of linear discrimination functions. This method can provide valuable help in outdoor scene analysis where no colour space has been demonstrated as being the most suitable. Experiment results recognizing objects in outdoor scenes are reported

A Novel Breast Tissue Density Classification Methodology

It has been shown that the accuracy of mammographic abnormality detection methods is strongly dependent on the breast tissue characteristics, where a dense breast drastically reduces detection sensitivity. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. Here, we describe the development of an automatic breast tissue classification methodology, which can be summarized in a number of distinct steps: 1) the segmentation of the breast area into fatty versus dense mammographic tissue; 2) the extraction of morphological and texture features from the segmented breast areas; and 3) the use of a Bayesian combination of a number of classifiers. The evaluation, based on a large number of cases from two different mammographic data sets, shows a strong correlation ( and 0.67 for the two data sets) between automatic and expert-based Breast Imaging Reporting and Data System mammographic density assessment

Automatic classification of breast density

A recent trend in digital mammography is computer-aided diagnosis systems, which are computerised tools designed to assist radiologists. Most of these systems are used for the automatic detection of abnormalities. However, recent studies have shown that their sensitivity is significantly decreased as the density of the breast increases. This dependence is method specific. In this paper we propose a new approach to the classification of mammographic images according to their breast parenchymal density. Our classification uses information extracted from segmentation results and is based on the underlying breast tissue texture. Classification performance was based on a large set of digitised mammograms. Evaluation involves different classifiers and uses a leave-one-out methodology. Results demonstrate the feasibility of estimating breast density using image processing and analysis techniques

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

Classification of sags according to their origin based on the waveform similarity

A statistical method for classification of sags their origin downstream or upstream from the recording point is proposed in this work. The goal is to obtain a statistical model using the sag waveforms useful to characterise one type of sags and to discriminate them from the other type. This model is built on the basis of multi-way principal component analysis an later used to project the available registers in a new space with lower dimension. Thus, a case base of diagnosed sags is built in the projection space. Finally classification is done by comparing new sags against the existing in the case base. Similarity is defined in the projection space using a combination of distances to recover the nearest neighbours to the new sag. Finally the method assigns the origin of the new sag according to the origin of their neighbours

Using the Browse Object tool in MS Word 2010

Use the Browse Object tool to quickly navigate through a selected type of object in your file – pages, tables, sections, images, footnotes or headings of your document. For best viewing Download the video.

Using the Browse Object tool in MS Word 2011

Use the Browse Object tool to quickly navigate through a selected type of object in your file – pages, tables, sections, images, footnotes or headings of your document. For best viewing Download the video.

Análisis del Espacio Schengen en materia de seguridad como base para la implementación del Protocolo sobre la Facilitación del Movimiento de Personas de la Comunidad para el Desarrollo de África Austral (SADC)

El fin de la Guerra Fría supuso no sólo el triunfo del capitalismo y de la democracia liberal, sino un cambio significativo en el Sistema Internacional; siendo menos centralizado y más regionalizado, como consecuencia de la proximidad y relaciones de interdependencia entre sus actores (no sólo Estados) y permitiendo la formación de Complejos Regionales de Seguridad (CRS). Los CRS son una forma efectiva de relacionarse y aproximarse a la arena internacional pues a través de sus procesos de securitización y desecuritización consiguen lograr objetivos específicos. Partiendo de ello, tanto la Unión Europea (UE) como la Comunidad para el Desarrollo de África Austral (SADC) iniciaron varios procesos de securitización relacionados con la integración regional; siendo un ejemplo de ello la eliminación de los controles en sus fronteras interiores o libre circulación de personas; pues consideraron que de no hacerse realidad, ello generaría amenazas políticas (su influencia y capacidad de actuación estaban amenazadas), económicas (en cuanto a su competitividad y niveles básicos de bienestar) y societales (en cuanto a la identidad de la comunidad como indispensable para la integración) que pondrían en riesgo la existencia misma de sus CRS. En esta medida, la UE creó el Espacio Schengen, que fue producto de un proceso de securitización desde inicios de la década de los 80 hasta mediados de la década de los 90; y la SADC se encuentra inmersa en tal proceso de securitización desde 1992 hasta la actualidad y espera la ratificación del Protocolo para la Facilitación del Movimiento de personas como primer paso para lograr la eliminación de controles en sus fronteras interiores. Si bien tanto la UE como la SADC consideraron que de no permitir la libre circulación de personas, su integración y por lo tanto, sus CRS estaban en riesgo; la SADC no lo ha logrado. Ello hace indispensable hacer un análisis más profundo de sus procesos de securitización para así encontrar sus falencias con respecto al éxito de la UE. El análisis está basado en la Teoría de los Complejos de Seguridad de Barry Buzan, plasmada en la obra Security a New Framework for Analysis (1998) de Barry Buzan, Ole Waever y Jaap de Wilde y será dividido en cada una de las etapas del proceso de securitización: la identificación de una amenaza existencial a un objeto referente a través de un acto discursivo, la aceptación de una amenaza por parte de una audiencia relevante y las acciones de emergencia para hacer frente a las amenazas existenciales; reconociendo las diferencias y similitudes de un proceso de securitización exitoso frente a otro que aún no lo ha sido.