Conference on human vision, visual processing, and digital display part of SPIE/SPSE symposium on electronic imaging science and technology san jose convention center, san jose, california february 24-March 1, 1991

Previous studies in nonhuman primates and cats have shown that the pulvinar receives input from various cortical and subcortical areas involved in vision. Although the contribution of the pulvinar to human vision remains to be established, anatomical tracer and electrophysiological animal studies on cortico-pulvinar circuits suggest an important role of this structure in visual spatial attention, visual integration, and higher-order visual processing. Because methodological constraints limit investigations of the human pulvinar's function, its role could, up to now, only be inferred from animal studies. In the present study, we used an innovative imaging technique, Diffusion Tensor Imaging (DTI) tractography, to determine cortical and subcortical connections of the human pulvinar. We were able to reconstruct pulvinar fiber tracts and compare variability across subjects in vivo. Here we demonstrate that the human pulvinar is interconnected with subcortical structures (superior colliculus, thalamus, and caudate nucleus) as well as with cortical regions (primary visual areas (area 17), secondary visual areas (area 18, 19), visual inferotemporal areas (area 20), posterior parietal association areas (area 7), frontal eye fields and prefrontal areas). These results are consistent with the connectivity reported in animal anatomical studies.

Download Full-text

TOWARD A VISUAL COGNITIVE SYSTEM USING ACTIVE TOP-DOWN SACCADIC CONTROL

International Journal of Humanoid Robotics ◽

10.1142/s0219843608001443 ◽

2008 ◽

Vol 05 (02) ◽

pp. 225-246 ◽

Cited By ~ 3

Author(s):

JOYCA LACROIX ◽

ERIC POSTMA ◽

JAAP VAN DEN HERIK ◽

JAAP MURRE

Keyword(s):

Visual Processing ◽

Humanoid Robot ◽

Human Memory ◽

Classification Performance ◽

Visual Input ◽

Cognitive System ◽

Human Vision ◽

Top Down ◽

Interactive Nature ◽

Visual Cognitive

The saccadic selection of relevant visual input for preferential processing allows the efficient use of computational resources. Based on saccadic active human vision, we aim to develop a plausible saccade-based visual cognitive system for a humanoid robot. This paper presents two initial steps toward our objective by extending the saccade-based model of human memory called NIM1 to a plausible model of natural visual classification. NIM builds feature-vector representations from selected local image samples and uses these to make memory-based decisions. As a first step, we adapt NIM to a straightforward saccade-based model for the classification of natural visual input called NIM-CLASS and evaluate the model in a face-classification experiment. As a second step, we aim to approach the interactive nature of human vision by extending NIM-CLASS to NIM-CLASSTD by adding active top-down saccadic control. We then assess to what extent top-down control enhances classification performance. The results show that the incorporation of top-down saccadic control benefits classification performance compared to the purely bottom-up control, reducing the amount of visual input required for correct classification. We conclude that NIM-CLASSTD may provide a fruitful basis for an active visual cognitive system in a humanoid robot that enables efficient visual processing.

Download Full-text