A connectionist computational method for face recognition

In this work, a modified version of the elastic bunch graph matching (EBGM) algorithm for face recognition is introduced. First, faces are detected by using a fuzzy skin detector based on the RGB color space. Then, the fiducial points for the facial graph are extracted automatically by adjusting a grid of points to the result of an edge detector. After that, the position of the nodes, their relation with their neighbors and their Gabor jets are calculated in order to obtain the feature vector defining each face. A self-organizing map (SOM) framework is shown afterwards. Thus, the calculation of the winning neuron and the recognition process are performed by using a similarity function that takes into account both the geometric and texture information of the facial graph. The set of experiments carried out for our SOM-EBGM method shows the accuracy of our proposal when compared with other state-of the-art methods.

On-line character recognition /

Bibliography: p. 14.

Greening operations management:an online sustainable procurement course for practitioners

In the Operations Management field, sustainable procurement has emerged as a way to green the purchasing and supply process. This paper explores issues in sustainable procurement training. The authors formed an interdisciplinary team to design, deliver and evaluate a training programme to promote and develop sustainable procurement in the United Kingdom health sector. Particular features of the project were its engagement with evolving and contested understandings of sustainable procurement and of the underlying concept of sustainable development and its recognition that relevant knowledge in the field is both incomplete and widely diffused through the procurement community. Eight practitioner groups worked together on themes to develop their understanding of sustainable procurement using the Blackboard virtual learning environment. Group interviews were conducted upon completion of the course and again three months later to explore qualitatively participants' experience of learning and implementing sustainable procurement. Although the course was delivered to practitioners, it might be modified for undergraduate and graduate students as it comprised the use of online activities in virtual learning environments, case studies and a broad range of literature. The course was also particularly significant in the context of contemporary policy moves in the United Kingdom and elsewhere to promote the role of higher education institutions in delivering workplace-based, high-skills education consistent with strategic policy considerations (see, for example, DIUS, 2008).

Online sensorless position estimation for switched reluctance motors using one current sensor

This paper proposes an online sensorless rotor position estimation technique for switched reluctance motors (SRMs) using just one current sensor. It is achieved by first decoupling the excitation current from the bus current. Two phase-shifted pulse width modulation signals are injected into the relevant lower transistors in the asymmetrical half-bridge converter for short intervals during each current fundamental cycle. Analog-to-digital converters are triggered in the pause middles of the dual pulse to separate the bus current for excitation current recognition. Next, the rotor position is estimated from the excitation current, by a current-rise-time method in the current-chopping-control mode in a low-speed operation and a current-gradient method in the voltage-pulse-control mode in a high-speed operation. The proposed scheme requires only a bus current sensor and a minor change to the converter circuit, without a need for individual phase current sensors or additional detection devices, achieving a more compact and cost-effective drive. The performance of the sensorless SRM drive is fully investigated. The simulation and experiments on a 750-W three-phase 12/8-pole SRM are carried out to verify the effectiveness of the proposed scheme.

Datasets, features, learning, and models in visual recognition

Visual recognition is a fundamental research topic in computer vision. This dissertation explores datasets, features, learning, and models used for visual recognition. In order to train visual models and evaluate different recognition algorithms, this dissertation develops an approach to collect object image datasets on web pages using an analysis of text around the image and of image appearance. This method exploits established online knowledge resources (Wikipedia pages for text; Flickr and Caltech data sets for images). The resources provide rich text and object appearance information. This dissertation describes results on two datasets. The first is Berg’s collection of 10 animal categories; on this dataset, we significantly outperform previous approaches. On an additional set of 5 categories, experimental results show the effectiveness of the method. Images are represented as features for visual recognition. This dissertation introduces a text-based image feature and demonstrates that it consistently improves performance on hard object classification problems. The feature is built using an auxiliary dataset of images annotated with tags, downloaded from the Internet. Image tags are noisy. The method obtains the text features of an unannotated image from the tags of its k-nearest neighbors in this auxiliary collection. A visual classifier presented with an object viewed under novel circumstances (say, a new viewing direction) must rely on its visual examples. This text feature may not change, because the auxiliary dataset likely contains a similar picture. While the tags associated with images are noisy, they are more stable when appearance changes. The performance of this feature is tested using PASCAL VOC 2006 and 2007 datasets. This feature performs well; it consistently improves the performance of visual object classifiers, and is particularly effective when the training dataset is small. With more and more collected training data, computational cost becomes a bottleneck, especially when training sophisticated classifiers such as kernelized SVM. This dissertation proposes a fast training algorithm called Stochastic Intersection Kernel Machine (SIKMA). This proposed training method will be useful for many vision problems, as it can produce a kernel classifier that is more accurate than a linear classifier, and can be trained on tens of thousands of examples in two minutes. It processes training examples one by one in a sequence, so memory cost is no longer the bottleneck to process large scale datasets. This dissertation applies this approach to train classifiers of Flickr groups with many group training examples. The resulting Flickr group prediction scores can be used to measure image similarity between two images. Experimental results on the Corel dataset and a PASCAL VOC dataset show the learned Flickr features perform better on image matching, retrieval, and classification than conventional visual features. Visual models are usually trained to best separate positive and negative training examples. However, when recognizing a large number of object categories, there may not be enough training examples for most objects, due to the intrinsic long-tailed distribution of objects in the real world. This dissertation proposes an approach to use comparative object similarity. The key insight is that, given a set of object categories which are similar and a set of categories which are dissimilar, a good object model should respond more strongly to examples from similar categories than to examples from dissimilar categories. This dissertation develops a regularized kernel machine algorithm to use this category dependent similarity regularization. Experiments on hundreds of categories show that our method can make significant improvement for categories with few or even no positive examples.

Football Video Annotation Based on Player Motion Recognition Using Enhanced Entropy

This paper presents a semi-parametric Algorithm for parsing football video structures. The approach works on a two interleaved based process that closely collaborate towards a common goal. The core part of the proposed method focus perform a fast automatic football video annotation by looking at the enhance entropy variance within a series of shot frames. The entropy is extracted on the Hue parameter from the HSV color system, not as a global feature but in spatial domain to identify regions within a shot that will characterize a certain activity within the shot period. The second part of the algorithm works towards the identification of dominant color regions that could represent players and playfield for further activity recognition. Experimental Results shows that the proposed football video segmentation algorithm performs with high accuracy.

Acessibilidade e usabilidade em curso online : um desafio para as escolas de governo

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Educação, Programa de Pós-Graduação em Educação, 2016.

Subjetividade e interação nos fóruns online reflexões sobre a permanência em educação a distância

Este artigo foi desenvolvido com objetivo de produzir indicadores que possam melhorar a funcionalidade dos fóruns online e contribuir numa maior permanência dos estudantes da Educação a Distância. Foi realizada uma análise, orientada pela Epistemologia Qualitativa, dos processos subjetivos e interacionais produzidos nos fóruns de apresentação e fóruns temáticos de duas disciplinas de formação pedagógica – (1) Estratégias de Ensino e Aprendizagem e (2) A Psicologia e a Construção do Conhecimento – ofertadas nos cursos de Licenciatura em Teatro, Música e Artes Visuais, UAB/UnB. As informações produzidas apontam para a necessidade de reconhecimento e valorização do estudante como sujeito na aprendizagem, a consolidação da presença pedagógica do tutor, a valorização dos fóruns como espaços de aprendizagem e a produção de espaços sociais de pertencimento. ______________________________________________________________________________ ABSTRACT

Avoiding staff removal stage in optical music recognition: application to scores written in white mensural notation

Staff detection and removal is one of the most important issues in optical music recognition (OMR) tasks since common approaches for symbol detection and classification are based on this process. Due to its complexity, staff detection and removal is often inaccurate, leading to a great number of errors in posterior stages. For this reason, a new approach that avoids this stage is proposed in this paper, which is expected to overcome these drawbacks. Our approach is put into practice in a case of study focused on scores written in white mensural notation. Symbol detection is performed by using the vertical projection of the staves. The cross-correlation operator for template matching is used at the classification stage. The goodness of our proposal is shown in an experiment in which our proposal attains an extraction rate of 96 % and a classification rate of 92 %, on average. The results found have reinforced the idea of pursuing a new research line in OMR systems without the need of the removal of staff lines.