Search results for "Neural"
showing 10 items of 2783 documents
Full Reference Mesh Visual Quality Assessment Using Pre-Trained Deep Network and Quality Indices
2019
In this paper, we propose an objective quality metric to evaluate the perceived visual quality of 3D meshes. Our method relies on pre-trained convolutional neural network i.e VGG to extract features from the distorted mesh and its reference. Quality indices from well-known mesh visual quality metrics are concatenated with the extracted features resulting a global feature vector. this latter is used to learn the support vector regression (SVR) to predict the final quality score. Experimental results from two subjective databases (LIRIS masking database and LIRIS/EPFL general-purpose database) and comparisons with seven objective metrics cited in the state-of-the-art demonstrate the effective…
Analyzing the metrics of the perceptual space in a new multistage physiological colour vision model
2009
In this work, the metric of a new multistage colour vision model, ATTD05, is assessed and a new colour difference formula is suggested. Firstly, the uniformity of the ATTD05 colour space was compared with that of CIECAM02 for some Munsell samples, because if the model yields a uniform perceptual space, we will be able to implement a colour difference formula as a Euclidian distance between two points. Secondly, we developed a new space based on the perceptual descriptors of the model: brightness, hue, colourfulness, and saturation. After that, we calculated the free parameters of the space that better fit the measured and experimental data of two datasets (small-magnitude and large-magnitud…
Automatic Detection of Infantile Hemangioma using Convolutional Neural Network Approach
2020
Infantile hemangioma is the most common tumor of childhood. This study proposes an automatic detection as a preliminary step for a further accurate monitoring tool to evaluate the clinical status of hemangioma. For the detection of hemangioma pixels, a convolutional neural network (CNN) was trained on patches of two classes (hemangioma and nonhemangioma) from the train dataset, and then it was used to classify all the pixels of the region of interest from the test dataset. In order to evaluate the results of segmentation obtained with CNN, the region of interest of the test dataset was also segmented using two classical methods of segmentation: fuzzy c-means clustering (FCM) and segmentatio…
A Sentiment Enhanced Deep Collaborative Filtering Recommender System
2021
Recommender systems use advanced analytic and learning techniques to select relevant information from massive data and inform users’ smart decision-making on their daily needs. Numerous works exploiting user’s sentiments on products to enhance recommendations have been introduced. However, there has been relatively less work exploring higher-order user-item features interactions for sentiment enhanced recommender system. In this paper, a novel Sentiment Enhanced Deep Collaborative Filtering Recommender System (SE-DCF) is developed. The architecture is based on a Neural Attention network component aggregated with the output predictions of a Convolution Neural Network (CNN) recommender. Speci…
Reduced Reference Mesh Visual Quality Assessment Based on Convolutional Neural Network
2018
3D meshes are usually affected by various visual distortions during their transmission and geometric processing. In this paper we propose a reduced reference method for mesh visual quality assessment. The method compares features extracted from the distorted mesh and the original one using a convolutional neural network in order to estimate the visual quality score. The perceptual distance between two meshes is computed as the Kullback-Leibler divergence between the two sets of feature vectors. Experimental results from two subjective databases (LIRIS masking database and LIRIS/EPFL general purpose database) and comparisons with seven objective metrics cited in the state-of-the-art demonstr…
Computer Programming Aptitude Test as a Tool for Reducing Student Attrition
2015
Submitted to the VTR conference to be held in Rezekne, June 2015
Hybrid 3D-ResNet Deep Learning Model for Automatic Segmentation of Thoracic Organs at Risk in CT Images
2020
In image radiation therapy, accurate segmentation of organs at risk (OARs) is a very essential task and has clinical applications in cancer treatment. The segmentation of organs close to lung, breast, or esophageal cancer is a routine and time-consuming process. The automatic segmentation of organs at risk would be an essential part of treatment planning for patients suffering radiotherapy. The position and shape variation, morphology inherent and low soft tissue contrast between neighboring organs across each patient’s scans is the challenging task for automatic segmentation of OARs in Computed Tomography (CT) images. The objective of this paper is to use automatic segmentation of the orga…
Dynamics of Vertebral Column Observed by Stereovision and Recurrent Neural Network Model
2005
A new non-invasive method for investigation of movement of selected points on the vertebral column is presented. The registration of position of points marked on patient's body is performed by 4 infrared cameras. This experiment enables to reconstruct 3-dimensional trajectories of displacement of marked points. We introduce recurrent neural networks as formal nonlinear dynamical models of each point trajectory. These models are based only on experimental data and are set up of minimal number of parameters. Therefore they are suitable for pattern recognition problems.
Hype and Hopes of Stem Cell Research in Neurodegenerative Diseases
2017
Hope from the regeneration promoting effects of stem cells have provided new insights for understanding diseases that were previously thought to have a limited prognostic improvement upon medical intervention. This is especially indicated in neurodegenerative diseases, which until the discovery and research in stem cells were thought to have minimal regenerative capabilities. This review covers various treatment modalities involving different types of stem cells, such as human embryonic stem cells, induced pluripotent stem cells, mesenchymal stem cells and neural stem cells, which have been tested for various neurodegenerative disorders such as Multiple Sclerosis, Alzheimer’s disease, Parki…
Association between pain, central sensitization and anxiety in postherpetic neuralgia
2014
Background In postherpetic neuralgia (PHN), dorsal root ganglia neurons are damaged. According to the proposed models, PHN pain might be associated with nociceptive deafferentation, and peripheral (heat hyperalgesia) or central sensitization (allodynia). Methods In 36 PHN patients, afferent nerve fibre function was characterized using quantitative sensory testing and histamine-induced flare analysis. Psychological factors were evaluated with the Hospital Anxiety and Depression Scale (HADS), disease-related quality of life (QoL) with SF-36 and pain with the McGill questionnaire [pain rating index (PRI)]. The patients were also divided into subgroups according to the presence or absence of br…