AIMC Topic: Pattern Recognition, Automated

Clear Filters Showing 881 to 890 of 1689 articles

Nonlinear Semi-Supervised Metric Learning Via Multiple Kernels and Local Topology.

International journal of neural systems
Changing the metric on the data may change the data distribution, hence a good distance metric can promote the performance of learning algorithm. In this paper, we address the semi-supervised distance metric learning (ML) problem to obtain the best n...

Sensor, Signal, and Imaging Informatics.

Yearbook of medical informatics
To summarize significant contributions to sensor, signal, and imaging informatics published in 2016. We conducted an extensive search using PubMed® and Web of Science® to identify the scientific contributions published in 2016 that addressed sensor...

Sharable and Individual Multi-View Metric Learning.

IEEE transactions on pattern analysis and machine intelligence
This paper presents a sharable and individual multi-view metric learning (MvML) approach for visual recognition. Unlike conventional metric leaning methods which learn a distance metric on either a single type of feature representation or a concatena...

Fast Gaussian Naïve Bayes for searchlight classification analysis.

NeuroImage
The searchlight technique is a variant of multivariate pattern analysis (MVPA) that examines neural activity across large sets of small regions, exhaustively covering the whole brain. This usually involves application of classifier algorithms across ...

3-D Object Recognition of a Robotic Navigation Aid for the Visually Impaired.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
This paper presents a 3-D object recognition method and its implementation on a robotic navigation aid to allow real-time detection of indoor structural objects for the navigation of a blind person. The method segments a point cloud into numerous pla...

Seizure Classification From EEG Signals Using Transfer Learning, Semi-Supervised Learning and TSK Fuzzy System.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Recognition of epileptic seizures from offline EEG signals is very important in clinical diagnosis of epilepsy. Compared with manual labeling of EEG signals by doctors, machine learning approaches can be faster and more consistent. However, the class...

Identification and segmentation of myelinated nerve fibers in a cross-sectional optical microscopic image using a deep learning model.

Journal of neuroscience methods
BACKGROUND: The morphometric analysis of myelinated nerve fibers of peripheral nerves in cross-sectional optical microscopic images is valuable. Several automated methods for nerve fiber identification and segmentation have been reported. This paper ...

Learning-based automated segmentation of the carotid artery vessel wall in dual-sequence MRI using subdivision surface fitting.

Medical physics
PURPOSE: The quantification of vessel wall morphology and plaque burden requires vessel segmentation, which is generally performed by manual delineations. The purpose of our work is to develop and evaluate a new 3D model-based approach for carotid ar...

LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks.

IEEE transactions on visualization and computer graphics
Recurrent neural networks, and in particular long short-term memory (LSTM) networks, are a remarkably effective tool for sequence modeling that learn a dense black-box hidden representation of their sequential input. Researchers interested in better ...

A Machine Learning Approach to Automated Gait Analysis for the Noldus Catwalk System.

IEEE transactions on bio-medical engineering
OBJECTIVE: Gait analysis of animal disease models can provide valuable insights into in vivo compound effects and thus help in preclinical drug development. The purpose of this paper is to establish a computational gait analysis approach for the Nold...