Fuzziness-based active learning framework to enhance hyperspectral image classification performance for discriminative and generative classifiers.

Hyperspectral image classification with a limited number of training samples without loss of accuracy is desirable, as collecting such data is often expensive and time-consuming. However, classifiers trained with limited samples usually end up with a large generalization error. To overcome the said problem, we propose a fuzziness-based active learning framework (FALF), in which we implement the idea of selecting optimal training samples to enhance generalization performance for two different kinds of classifiers, discriminative and generative (e.

A Novel Maximum Entropy Markov Model for Human Facial Expression Recognition.

Research in video based FER systems has exploded in the past decade. However, most of the previous methods work well when they are trained and tested on the same dataset. Illumination settings, image resolution, camera angle, and physical characteristics of the people differ from one dataset to another.

Human facial expression recognition using stepwise linear discriminant analysis and hidden conditional random fields.

This paper introduces an accurate and robust facial expression recognition (FER) system. For feature extraction, the proposed FER system employs stepwise linear discriminant analysis (SWLDA). SWLDA focuses on selecting the localized features from the expression frames using the partial F-test values, thereby reducing the within class variance and increasing the low between variance among different expression classes.

Hierarchical recognition scheme for human facial expression recognition systems.

Over the last decade, human facial expressions recognition (FER) has emerged as an important research area. Several factors make FER a challenging research problem. These include varying light conditions in training and test images; need for automatic and accurate face detection before feature extraction; and high similarity among different expressions that makes it difficult to distinguish these expressions with a high accuracy.

Exploratory data analysis of acceleration signals to select light-weight and accurate features for real-time activity recognition on smartphones.

Smartphone-based activity recognition (SP-AR) recognizes users' activities using the embedded accelerometer sensor. Only a small number of previous works can be classified as online systems, i.e.

WEAMR-a weighted energy aware multipath reliable routing mechanism for hotline-based WSNs.

Reliable source to sink communication is the most important factor for an efficient routing protocol especially in domains of military, healthcare and disaster recovery applications. We present weighted energy aware multipath reliable routing (WEAMR), a novel energy aware multipath routing protocol which utilizes hotline-assisted routing to meet such requirements for mission critical applications. The protocol reduces the number of average hops from source to destination and provides unmatched reliability as compared to well known reactive ad hoc protocols i.

A single tri-axial accelerometer-based real-time personal life log system capable of activity classification and exercise information generation.

Recording a personal life log (PLL) of daily activities is an emerging technology for u-lifecare and e-health services. In this paper, we present an accelerometer-based personal life log system capable of human activity classification and exercise information generation. In our system, we use a tri-axial accelerometer and a real-time activity recognition scheme in which a set of augmented features of accelerometer signals, processed with Linear Discriminant Analysis (LDA), is classified by our hierarchical artificial neural network classifier: in the lower level of the classifier, a state of an activity is recognized based on the statistical and spectral features; in the upper level, an activity is recognized with a set of augmented features including autoregressive (AR) coefficients, signal magnitude area (SMA), and tilt angles (TA).

Accelerometer's position independent physical activity recognition system for long-term activity monitoring in the elderly.

Mobility is a good indicator of health status and thus objective mobility data could be used to assess the health status of elderly patients. Accelerometry has emerged as an effective means for long-term physical activity monitoring in the elderly. However, the output of an accelerometer varies at different positions on a subject's body, even for the same activity, resulting in high within-class variance.

A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer.

Physical-activity recognition via wearable sensors can provide valuable information regarding an individual's degree of functional ability and lifestyle. In this paper, we present an accelerometer sensor-based approach for human-activity recognition. Our proposed recognition method uses a hierarchical scheme.