Rotation Invariant Object Recognition from One Training Example

Local descriptors are increasingly used for the task of object recognition because of their perceived robustness with respect to occlusions and to global geometrical deformations. Such a descriptor--based on a set of oriented Gaussian derivative filters-- is used in our recognition system. We report here an evaluation of several techniques for orientation estimation to achieve rotation invariance of the descriptor. We also describe feature selection based on a single training image. Virtual images are generated by rotating and rescaling the image and robust features are selected. The results confirm robust performance in cluttered scenes, in the presence of partial occlusions, and when the object is embedded in different backgrounds.

Support Vector Machines: Training and Applications

The Support Vector Machine (SVM) is a new and very promising classification technique developed by Vapnik and his group at AT&T Bell Labs. This new learning algorithm can be seen as an alternative training technique for Polynomial, Radial Basis Function and Multi-Layer Perceptron classifiers. An interesting property of this approach is that it is an approximate implementation of the Structural Risk Minimization (SRM) induction principle. The derivation of Support Vector Machines, its relationship with SRM, and its geometrical insight, are discussed in this paper. Training a SVM is equivalent to solve a quadratic programming problem with linear and box constraints in a number of variables equal to the number of data points. When the number of data points exceeds few thousands the problem is very challenging, because the quadratic form is completely dense, so the memory needed to store the problem grows with the square of the number of data points. Therefore, training problems arising in some real applications with large data sets are impossible to load into memory, and cannot be solved using standard non-linear constrained optimization algorithms. We present a decomposition algorithm that can be used to train SVM's over large data sets. The main idea behind the decomposition is the iterative solution of sub-problems and the evaluation of, and also establish the stopping criteria for the algorithm. We present previous approaches, as well as results and important details of our implementation of the algorithm using a second-order variant of the Reduced Gradient Method as the solver of the sub-problems. As an application of SVM's, we present preliminary results we obtained applying SVM to the problem of detecting frontal human faces in real images.

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.

Implementation of training programs in self-regulated learning strategies in Moodle format : Results of a experience in higher education

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How to improve the formal analytic reasoning and scientific motivation in the intellectual training of medical students

A review article of the The New England Journal of Medicine refers that almost a century ago, Abraham Flexner, a research scholar at the Carnegie Foundation for the Advancement of Teaching, undertook an assessment of medical education in 155 medical schools in operation in the United States and Canada. Flexner’s report emphasized the nonscientific approach of American medical schools to preparation for the profession, which contrasted with the university-based system of medical education in Germany. At the core of Flexner’s view was the notion that formal analytic reasoning, the kind of thinking integral to the natural sciences, should hold pride of place in the intellectual training of physicians. This idea was pioneered at Harvard University, the University of Michigan, and the University of Pennsylvania in the 1880s, but was most fully expressed in the educational program at Johns Hopkins University, which Flexner regarded as the ideal for medical education. (...)

Attention training in Attention Deficit Hyperactivity Disorder

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Training and pre-decisional bias in a multiattribute decision task. 'Formaci??n y sesgo pre-decisional en una tarea de decisi??n multiatributiva'

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Inputs for the incorporation of the UNESCO guidelines on ICT competency standards for teachers : the training of teachers of mathematics in Central America

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Teacher Training Videos

Free online training resources on using web 2.0 tools for busy lecturers. - 'Outstanding ICT initiative of the year' winner of the JISC award is commended for 'commitment to open access to online content' A wealth of openly available multimedia content won the JISC/Times Higher Award. Created by University of Westminster lecturer Russell Stannard's websites build upon pioneering work using video to mark students' work. Using screen recording software, Stannard recorded himself walking through various Web 2.0 technologies with a voice-over, which were then uploaded to a website - www.teachertrainingvideos.com. The site quickly proved popular and rapidly built into a bank of over 30 videos.

Multimedia Training Videos

Multimedia Training Videos is a series of free learning videos to show anyone interested in learning packages like Flash, Director and Photoshop.

PowerPoint - basic skills training

This step-by-step guided worksheet and accompanying PowerPoint file introduce some key skills: - reorganising slides and bullets - creating speaker notes - printing slide handouts - including hyperlinks - adding images Simple stuff, but many find it useful - it uses Office 2004 (XP)

Animal Biosafety Level 1 Training Video

This training video is intended to familiarise researchers and technicians working with Hazard Group 1 pathogens in Containment Level 1 animal facilities. It is in Flash video format which will require a free media player such as VLC Media Player (http://www.videolan.org/vlc/) to watch.

Animal Biosafety Level 3 Training Video

This training video is intended to familiarise researchers and technicians working with Hazard Group 3 pathogens in Containment Level 3 animal facilities. It is in Flash video format which will require a free media player such as VLC Media Player (http://www.videolan.org/vlc/) to watch.

HSE - Microbiological Safety Cabinet Training Video

This training video is intended to familiarise researchers and technicians, working with potentially airborne pathogens, on the correct and safe use of Microbiological Safety Cabinets. The video also provides instruction on cleaning, disinfection and fumigation regimes; maintenance and testing regimes; and commissioning and decommissioning requirements of such Local Exhaust Ventilation (LEV) systems. It is in Windows Media Video format which will require a free media player such as Windows Media Player or VLC Media Player (http://www.videolan.org/vlc/) to watch.

Animal Biosafety Level 2 Training Video

This training video is intended to familiarise researchers and technicians working with Hazard Group 2 pathogens in Containment Level 2 animal facilities. It is in Flash video format which will require a free media player such as VLC Media Player (http://www.videolan.org/vlc/) to watch.