The influence of stereopsis on visual saliency in a proto-object based model of selective attention.

Some animals including humans use stereoscopic vision which reconstructs spatial information about the environment from the disparity between images captured by eyes in two separate adjacent locations. Like other sensory information, such stereoscopic information is expected to influence attentional selection. We develop a biologically plausible model of binocular vision to study its effect on bottom-up visual attention, i.e., visual saliency. In our model, the scene is organized in terms of proto-objects on which attention acts, rather than on unbound sets of elementary features. We show that taking into account the stereoscopic information improves the performance of the model in the prediction of human eye movements with statistically significant differences.

Proto-Object Based Saliency Model With Texture Detection Channel.

The amount of visual information projected from the retina to the brain exceeds the information processing capacity of the latter. Attention, therefore, functions as a filter to highlight important information at multiple stages of the visual pathway that requires further and more detailed analysis. Among other functions, this determines where to fixate since only the fovea allows for high resolution imaging. Visual saliency modeling, i.e. understanding how the brain selects important information to analyze further and to determine where to fixate next, is an important research topic in computational neuroscience and computer vision. Most existing bottom-up saliency models use low-level features such as intensity and color, while some models employ high-level features, like faces. However, little consideration has been given to mid-level features, such as texture, for visual saliency models. In this paper, we extend a biologically plausible proto-object based saliency model by adding simple texture channels which employ nonlinear operations that mimic the processing performed by primate visual cortex. The extended model shows statistically significant improved performance in predicting human fixations compared to the previous model. Comparing the performance of our model with others on publicly available benchmarking datasets, we find that our biologically plausible model matches the performance of other models, even though those were designed entirely for maximal performance with little regard to biological realism.

Motion classification using epidural electrodes for low-invasive brain-machine interface.

Brain-machine interfaces (BMIs) are expected to be used to assist seriously disabled persons' communications and reintegrate their motor functions. One of the difficult problems to realize practical BMI is how to record neural activity clearly and safely. Conventional invasive methods require electrodes inside the dura mater, and noninvasive methods do not involve surgery but have poor signal quality. Thus a low-invasive method of recording is important for safe and practical BMI. In this study, the authors used epidural electrodes placed between the skull and dura mater to record a rat's neural activity for low-invasive BMI. The signals were analyzed using a short-time Fourier transform, and the power spectra were classified into rat motions by a support vector machine. Classification accuracies were up to 96% in two-class discrimination, including that when the rat stopped, walked, and rested. The feasibility of a low-invasive BMI based on an epidural neural recording was shown in this study.