Neural representation of gestalt grouping and attention effect in human visual cortex.

Background: The brain aggregates meaningless local sensory elements to form meaningful global patterns in a process called perceptual grouping. Current brain imaging studies have found that neural activities in V1 are modulated during visual grouping. However, how grouping is represented in each of the early visual areas, and how attention alters these representations, is still unknown.

Groupwise structural sparsity for discriminative voxels identification.

This paper investigates the selection of voxels for functional Magnetic Resonance Imaging (fMRI) brain data. We aim to identify a comprehensive set of discriminative voxels associated with human learning when exposed to a neutral visual stimulus that predicts an aversive outcome. However, due to the nature of the unconditioned stimuli (typically a noxious stimulus), it is challenging to obtain sufficient sample sizes for psychological experiments, given the tolerability of the subjects and ethical considerations.

Multikernel Correntropy for Robust Learning.

As a novel similarity measure that is defined as the expectation of a kernel function between two random variables, correntropy has been successfully applied in robust machine learning and signal processing to combat large outliers. The kernel function in correntropy is usually a zero-mean Gaussian kernel. In a recent work, the concept of mixture correntropy (MC) was proposed to improve the learning performance, where the kernel function is a mixture Gaussian kernel, namely, a linear combination of several zero-mean Gaussian kernels with different widths.

Accurate Pedestrian Detection by Human Pose Regression.

Pedestrian detection with high detection and localization accuracy is increasingly important for many practical applications. Due to the flexible structure of the human body, it is hard to train a template-based pedestrian detector that achieves a high detection rate and a good localization accuracy simultaneously. In this paper, we utilize human pose estimation to improve the detection and localization accuracy of pedestrian detection.

Functional Source Separation for EEG-fMRI Fusion: Application to Steady-State Visual Evoked Potentials.

Neurorobotics is one of the most ambitious fields in robotics, driving integration of interdisciplinary data and knowledge. One of the most productive areas of interdisciplinary research in this area has been the implementation of biologically-inspired mechanisms in the development of autonomous systems. Specifically, enabling such systems to display adaptive behavior such as learning from good and bad outcomes, has been achieved by quantifying and understanding the neural mechanisms of the brain networks mediating adaptive behaviors in humans and animals.

Cross multivariate correlation coefficients as screening tool for analysis of concurrent EEG-fMRI recordings.

Over the past decade, the simultaneous recording of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) data has garnered growing interest because it may provide an avenue towards combining the strengths of both imaging modalities. Given their pronounced differences in temporal and spatial statistics, the combination of EEG and fMRI data is however methodologically challenging. Here, we propose a novel screening approach that relies on a Cross Multivariate Correlation Coefficient (xMCC) framework.

Multi-Timescale Collaborative Tracking.

We present the multi-timescale collaborative tracker for single object tracking. The tracker simultaneously utilizes different types of "forces", namely attraction, repulsion and support, to take advantage of their complementary strengths. We model the three forces via three components that are learned from the sample sets with different timescales.

Learning to detect a salient object.

In this paper, we study the salient object detection problem for images. We formulate this problem as a binary labeling task where we separate the salient object from the background. We propose a set of novel features, including multiscale contrast, center-surround histogram, and color spatial distribution, to describe a salient object locally, regionally, and globally.