1,721,042 research outputs found
L’educazione familiare nei servizi per la prima infanzia : uno studio delle conoscenze tacite ed esplicite delle educatrici nello sviluppo dell’empowerment genitoriale
Full text linkThe Role of Counselling in Child Care Services as an Inclusive Practice
No full textToday, school is becoming a rapidly changing learning environment. Thinking about students as a homogeneous population is no longer allowed, as diversity - in terms of culture, language, gender, family organization, learning styles and so on - has emerged as a key challenge for education today.
The debate on Special Educational Needs largely reflects this challenge, as working in school implies careful reconsideration of what we mean by "normal" and "special". Current educational intervention is generally based on a deficit and "within-child" model of facing SEN, whereas very little attention is given to the role of learning environment. The focus is on the child more than on the whole class, and on cognition and technical provisions more than on affective, sociocultural and community dimensions of learning. Conversely, regarding students and their needs as "hidden voices" allows us to adopt a transformative approach which sees diversity as a stimulus for the development of educational practices that might benefit all children and help school to become an inclusive and "moving" organization.
The aim of the book is twofold: on one hand we offer a systematic overview of the inclusive education state of art in six countries (Germany, Italy, Norway, Sweden, UK, and USA) based on the contribution of well-know scholars as Christine Ashby, Tony Booth, Barbara Brokamp, Fabio Dovigo, Beth Ferri, Kari Nes, and Mara Westling Allodi; on the other, the book will analyse five cases of good practices of inclusion related to different subjects and school levels
Blending Knowledge: The Role Of Tacit And Explicit Expertise Of Day Nursery Teachers In Empowering Parents
No full textTacit knowledge of teachers and development of parental empowerment in family education services for 0-3 children
No full textBlending Knowledge: The Role Of Tacit And Explicit Expertise Of Day Nursery Teachers In Empowering Parents
No full textA recall or precision oriented skin classifier using binary combining strategies
No full textSkin detection is a preliminary step in several applications, and many different methods are available in the literature. We show that the performance of explicit skin cluster classifiers can be enhanced by preprocessing the images with a white balance algorithm. Different combining strategies are then applied to these binary classifiers to further improve their performance in terms of recall and/or precision. Experimental results on a large and heterogeneous image database are presented
No reference image quality classification for JPEG-distorted images
No full textIn this paper, we address the Image Quality Assessment (IQA) of JPEG-distorted images. We approach the IQA field by focusing on a classification problem that maps different objective metrics into different categorical quality classes. To this end, we adopt a machine learning classification approach, where No Reference (NR) metrics are considered as features, while the assigned classes come from psycho-visual experiments. Eleven NR metrics have been considered: seven specific for blockiness and four general purpose. We evaluate the performance of single metrics and investigate if a pool of metrics can reach better performances than each of the single ones. Five as well as three quality classes are considered, and the corresponding classifiers are tested on two well known databases available in the literature (LIVE and MICT), and on a new database (IVL) presented in this paper
Pixel based skin colour classification exploiting explicit skin cluster definition methods
No full textIn this paper we examine the performance, on a large and heterogeneous image database, of
various skin detectors based on explicit colour skin cluster definition, coupled with a cast remover to see whether, and to what degree, the effectiveness of classification is improved, regardless of the
strategy adopted. We also evaluate the hypothesis that a combination of some of the skin detection algorithms studied could ensure a more accurate classification than any of the algorithms provides individually. Different combination rules have been investigated. All the experiments have been performed on the Compaq skin database. The results are evaluated in terms of both recall (the ratio
between the number of skin pixels correctly classified and the total number of actual skin pixels), and precision (the ratio between the number of skin pixels correctly classified and the total number of
pixels labelled as skin pixels by the detection method employed)
Genetic programming approach to evaluate complexity of texture images
No full textWe adopt genetic programming (GP) to define a measure that can predict complexity perception of texture images. We perform psychophysical experiments on three different datasets to collect data on the perceived complexity. The subjective data are used for training, validation, and test of the proposed measure. These data are also used to evaluate several possible candidate measures of texture complexity related to both low level and high level image features. We select four of them (namely roughness, number of regions, chroma variance, and memorability) to be combined in a GP framework. This approach allows a nonlinear combination of the measures and could give hints on how the related image features interact in complexity perception. The proposed complexity measure MGP exhibits Pearson correlation coefficients of 0.890 on the training set, 0.728 on the validation set, and 0.724 on the test set. MGP outperforms each of all the single measures considered. From the statistical analysis of different GP candidate solutions, we found that the roughness measure evaluated on the gray level image is the most dominant one, followed by the memorability, the number of regions, and finally the chroma variance
Enhancing Underexposed Images Preserving the Original Mood
No full textIn the present article we focus on enhancing the contrast of images with low illumination that present large underexposed regions. Most of these images represent night images. When applying standard contrast enhancement techniques, usually the night mood is modified, and also a noise over-enhancement within the darker regions is introduced. In a previous work we have described our local contrast correction algorithm designed to enhance images where both underexposed and overexposed regions are simoultaneously present. Here we show how this algorithm is able to automatically enhance night images, preserving the original mood. To further improve the performance of our method we also propose here a denoising procedure where the strength of the smoothing is a function of an estimated level of noise and it is further weighted by a saliency map. The method has been applied to a proper database of outdoor and indoor underexposed images. Our results have been qualitatively compared with well know contrast correction methods. © 2011 Springer-Verlag Berlin Heidelberg
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