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An active learning approach for stroke lesion segmentation on multimodal MRI data
We report encouraging results over a dataset combining functional, anatomical and diffusion data.
Source: Neurocomputing - November 21, 2014 Category: Neuroscience Source Type: research

Features and models for human activity recognition
In this study, an Information Correlation Coefficient (ICC) analysis was carried out followed by a wrapper Feature Selection (FS) method on the reduced input space. Additionally, a novel HAR method is proposed for this specific problem of stroke early diagnosing, comprising an adaptation of the well-known Genetic Fuzzy Finite State Machine (GFFSM) method. To the best of the author׳s knowledge, this is the very first analysis of the feature space concerning all the previously published feature transformations on raw acceleration data. The main contributions of this study are the optimization of the sample rate, selection o...
Source: Neurocomputing - July 10, 2015 Category: Neuroscience Source Type: research

Text detection in natural scene images based on color prior guided MSER
Publication date: 13 September 2018 Source:Neurocomputing, Volume 307 Author(s): Xiangnan Zhang, Xinbo Gao, Chunna Tian In this paper, we focus on text detection in natural scene images which is conducive to content-based wild image analysis and understanding. This task is still an open problem and usually includes two key issues: text candidate extraction and verification. For text candidate extraction, we introduce a color prior to guide the character candidate extraction by Maximally Stable Extremal Region (MSER). The principle of color prior acquirement is to obtain stroke-like textures with modified Stroke Width Tran...
Source: Neurocomputing - June 12, 2018 Category: Neuroscience Source Type: research

Neural Network Based Modeling and Control of Elbow Joint Motion Under Functional Electrical Stimulation
Publication date: Available online 6 March 2019Source: NeurocomputingAuthor(s): Yurong Li, Wenxin Chen, Jun Chen, Xin Chen, Jie Liang, Min DuAbstractIn patients with stroke and spinal cord injury, motor function is reduced or even lost because motor nerve signals cannot be transmitted due to nerve injury. Functional electrical stimulation (FES) is one of the most important rehabilitation techniques for the treatment of motor impairment in patients with stroke and spinal cord injury, which has been widely used in the recovery and reconstruction of limb motor function. In this paper, we propose a neural network based modelin...
Source: Neurocomputing - March 6, 2019 Category: Neuroscience Source Type: research

Integration of an Actor-Critic Model and Generative Adversarial Networks for a Chinese Calligraphy Robot
Publication date: Available online 16 January 2020Source: NeurocomputingAuthor(s): Ruiqi Wu, Changle Zhou, Fei Chao, Longzhi Yang, Chih-Min Lin, Changjing ShangAbstractAs a combination of robotic motion planning and Chinese calligraphy culture, robotic calligraphy plays a significant role in the inheritance and education of Chinese calligraphy culture. Most existing calligraphy robots focus on enabling the robots to learn writing through human participation, such as human-robot interactions and manually designed evaluation functions. However, because of the subjectivity of art aesthetics, these existing methods require a l...
Source: Neurocomputing - January 16, 2020 Category: Neuroscience Source Type: research

A machine learning approach to measure and monitor physical activity in children
Publication date: 8 March 2017 Source:Neurocomputing, Volume 228 Author(s): Paul Fergus, Abir J. Hussain, John Hearty, Stuart Fairclough, Lynne Boddy, Kelly Mackintosh, Gareth Stratton, Nicky Ridgers, Dhiya Al-Jumeily, Ahmed J. Aljaaf, Jenet Lunn The growing trend of obesity and overweight worldwide has reached epidemic proportions with one third of the global population now considered obese. This is having a significant medical impact on children and adults who are at risk of developing osteoarthritis, coronary heart disease and stroke, type 2 diabetes, cancers, respiratory problems, and non-alcoholic fatty liver disease...
Source: Neurocomputing - January 16, 2017 Category: Neuroscience Source Type: research

An analysis of Convolutional Long Short-Term Memory Recurrent Neural Networks for gesture recognition
Publication date: 13 December 2017 Source:Neurocomputing, Volume 268 Author(s): Eleni Tsironi, Pablo Barros, Cornelius Weber, Stefan Wermter In this research, we analyze a Convolutional Long Short-Term Memory Recurrent Neural Network (CNNLSTM) in the context of gesture recognition. CNNLSTMs are able to successfully learn gestures of varying duration and complexity. For this reason, we analyze the architecture by presenting a qualitative evaluation of the model, based on the visualization of the internal representations of the convolutional layers and on the examination of the temporal classification outputs at a frame lev...
Source: Neurocomputing - September 1, 2017 Category: Neuroscience Source Type: research

Automatic Segmentation of Left Ventricle from Cardiac MRI via Deep Learning and Region Constrained Dynamic Programming
Publication date: Available online 16 February 2019Source: NeurocomputingAuthor(s): Hu Huaifei, Ning Pan, Jiayu Wang, Tailang Yin, Renzhen YeAbstractSegmentation of the left ventricle from cardiac magnetic resonance images (MRI) is an essential step to quantitatively analyze global and regional cardiac function. The aim of this study is to develop a novel and robust algorithm which can improve the accuracy of automatic left ventricle segmentation on short-axis cardiac MRI. The database used in this study are 900 cardiac MRI cases from Hubei Cancer Hospital. Three key techniques are developed in this segmentation algorithm:...
Source: Neurocomputing - February 16, 2019 Category: Neuroscience Source Type: research

Direction-aware Neural Style Transfer with Texture Enhancement
Publication date: Available online 30 August 2019Source: NeurocomputingAuthor(s): Hao Wu, Zhengxing Sun, Yan Zhang, Qian LiAbstractNeural learning methods have been shown to be effective in style transfer. These methods, which are called NST, aim to synthesize a new image that retains the high-level structure of a content image while keeps the low-level features of a style image. However, these models using convolutional structures only extract local statistical features of style images and semantic features of content images. Since the absence of low-level features in the content image, these methods would synthesize imag...
Source: Neurocomputing - August 31, 2019 Category: Neuroscience Source Type: research