398,855 research outputs found
Grid simulation services for the medical community
The first part of this paper presents a selection of medical simulation applications, including image reconstruction, near real-time registration for neuro-surgery, enhanced dose distribution calculation for radio-therapy, inhaled drug delivery prediction, plastic surgery planning and cardio-vascular system simulation. The latter two topics are discussed in some detail. In the second part, we show how such services can be made available to the clinical practitioner using Grid technology. We discuss the developments and experience made during the EU project GEMSS, which provides reliable, efficient, secure and lawful medical Grid services
Dictionary based Image Compression via Sparse Representation
Nowadays image compression has become a necessity due to a large volume of images. For efficient use of storage space and data transmission, it becomes essential to compress the image. In this paper, we propose a dictionary based image compression framework via sparse representation, with the construction of a trained over-complete dictionary. The over-complete dictionary is trained using the intra-prediction residuals obtained from different images and is applied for sparse representation. In this method, the current image block is first predicted from its spatially neighboring blocks, and then the prediction residuals are encoded via sparse representation. Sparse approximation algorithm and the trained over-complete dictionary are applied for sparse representation of prediction residuals. The detail coefficients obtained from sparse representation are used for encoding. Experimental result shows that the proposed method yields both improved coding efficiency and image quality as compared to some state-of-the-art image compression methods
Understanding and Comparing Deep Neural Networks for Age and Gender Classification
Recently, deep neural networks have demonstrated excellent performances in
recognizing the age and gender on human face images. However, these models were
applied in a black-box manner with no information provided about which facial
features are actually used for prediction and how these features depend on
image preprocessing, model initialization and architecture choice. We present a
study investigating these different effects.
In detail, our work compares four popular neural network architectures,
studies the effect of pretraining, evaluates the robustness of the considered
alignment preprocessings via cross-method test set swapping and intuitively
visualizes the model's prediction strategies in given preprocessing conditions
using the recent Layer-wise Relevance Propagation (LRP) algorithm. Our
evaluations on the challenging Adience benchmark show that suitable parameter
initialization leads to a holistic perception of the input, compensating
artefactual data representations. With a combination of simple preprocessing
steps, we reach state of the art performance in gender recognition.Comment: 8 pages, 5 figures, 5 tables. Presented at ICCV 2017 Workshop: 7th
IEEE International Workshop on Analysis and Modeling of Faces and Gesture
Prediction of NOx Emissions from a Biomass Fired Combustion Process Based on Flame Radical Imaging and Deep Learning Techniques
This article presents a methodology for predicting NOx emissions from a biomass combustion process through flame radical imaging and deep learning (DL). The dataset was established experimentally from flame radical images captured on a biomass-gas fired test rig. Morphological component analysis is undertaken to improve the quality of the dataset, and the region-of-interest extraction is introduced to extract the flame radical part and rescale the image size. The developed DL-based prediction model contains three successive stages for implementing the feature extraction, feature fusion, and emission prediction. The fine-tuning based on the prediction is introduced to adjust the process of the feature fusion. The effects of the feature fusion and fine-tuning are discussed in detail. A comparison between various image- and machine-learning-based prediction models show that the proposed DL prediction model outperforms other models in terms of root mean square error criteria. The predicted NOx emissions are in good agreement with the measurement results
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