AIMC Topic: Pattern Recognition, Automated

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A Modified Sparse Representation Method for Facial Expression Recognition.

Computational intelligence and neuroscience
In this paper, we carry on research on a facial expression recognition method, which is based on modified sparse representation recognition (MSRR) method. On the first stage, we use Haar-like+LPP to extract feature and reduce dimension. On the second...

A Multiobjective Approach to Homography Estimation.

Computational intelligence and neuroscience
In several machine vision problems, a relevant issue is the estimation of homographies between two different perspectives that hold an extensive set of abnormal data. A method to find such estimation is the random sampling consensus (RANSAC); in this...

Particle Swarm Optimization with Double Learning Patterns.

Computational intelligence and neuroscience
Particle Swarm Optimization (PSO) is an effective tool in solving optimization problems. However, PSO usually suffers from the premature convergence due to the quick losing of the swarm diversity. In this paper, we first analyze the motion behavior o...

Classifier Subset Selection for the Stacked Generalization Method Applied to Emotion Recognition in Speech.

Sensors (Basel, Switzerland)
In this paper, a new supervised classification paradigm, called classifier subset selection for stacked generalization (CSS stacking), is presented to deal with speech emotion recognition. The new approach consists of an improvement of a bi-level mul...

Ultrasound Image Discrimination between Benign and Malignant Adnexal Masses Based on a Neural Network Approach.

Ultrasound in medicine & biology
The discrimination between benign and malignant adnexal masses in ultrasound images represents one of the most challenging problems in gynecologic practice. In the study described here, a new method for automatic discrimination of adnexal masses base...

Correlational Neural Networks.

Neural computation
Common representation learning (CRL), wherein different descriptions (or views) of the data are embedded in a common subspace, has been receiving a lot of attention recently. Two popular paradigms here are canonical correlation analysis (CCA)-based a...

Decoding intracranial EEG data with multiple kernel learning method.

Journal of neuroscience methods
BACKGROUND: Machine learning models have been successfully applied to neuroimaging data to make predictions about behavioral and cognitive states of interest. While these multivariate methods have greatly advanced the field of neuroimaging, their app...

Automatic Estimation of Osteoporotic Fracture Cases by Using Ensemble Learning Approaches.

Journal of medical systems
Ensemble learning methods are one of the most powerful tools for the pattern classification problems. In this paper, the effects of ensemble learning methods and some physical bone densitometry parameters on osteoporotic fracture detection were inves...

Quantifying co-cultured cell phenotypes in high-throughput using pixel-based classification.

Methods (San Diego, Calif.)
Biologists increasingly use co-culture systems in which two or more cell types are grown in cell culture together in order to better model cells' native microenvironments. Co-cultures are often required for cell survival or proliferation, or to maint...

Making Activity Recognition Robust against Deceptive Behavior.

PloS one
Healthcare services increasingly use the activity recognition technology to track the daily activities of individuals. In some cases, this is used to provide incentives. For example, some health insurance companies offer discount to customers who are...