A Model for a Filling-in Process Triggered by Edges Predicts "Conflicting" Afterimage Effects

Temporal integration characteristics of an image defined by binocular disparity cues

We can correctly recognize the content of an image by presenting all of the elements within a limited time, such as in a slit view or a divided painting image. It is important to clarify how temporally divided information is integrated and perceived to understand the temporal properties of the information-processing mechanism of visual systems. Previous studies related to this topic have often used two-dimensional pictorial stimuli; however, few have considered the temporal integration of binocular disparity for the recognition of objects defined with disparity. In this study, we examined image recognition properties based on the temporal integration of binocular disparity, by comparing that based on the temporal integration of luminance. The effect of element onset asynchrony (the time lag among presented elements) was somewhat similar between disparity and luminance with respect to randomly divided elements. On the other hand, under slit-vision conditions, the tolerance range of spatiotemporal integration for luminance stimuli was much wider than that for disparity stimuli. These results indicate that the temporal integration mechanism in localized areas is common to disparity and luminance, but that for global motion shows differences between the two mechanisms. Thus, we conclude that global motion has little contribution to the temporal integration of binocular disparity information for image recognition.

Computational modeling of color perception with biologically plausible spiking neural networks

Biologically plausible computational modeling of visual perception has the potential to link high-level visual experiences to their underlying neurons' spiking dynamic. In this work, we propose a neuromorphic (brain-inspired) Spiking Neural Network (SNN)-driven model for the reconstruction of colorful images from retinal inputs. We compared our results to experimentally obtained V1 neuronal activity maps in a macaque monkey using voltage-sensitive dye imaging and used the model to demonstrate and critically explore color constancy, color assimilation, and ambiguous color perception. Our parametric implementation allows critical evaluation of visual phenomena in a single biologically plausible computational framework. It uses a parametrized combination of high and low pass image filtering and SNN-based filling-in Poisson processes to provide adequate color image perception while accounting for differences in individual perception.

A Compound Computational Model for Filling-In Processes Triggered by Edges: Watercolor Illusions

The goal of our research was to develop a compound computational model with the ability to predict different variations of the "watercolor effects" and additional filling-in effects that are triggered by edges. The model is based on a filling-in mechanism solved by a Poisson equation, which considers the different gradients as "heat sources" after the gradients modification. The biased (modified) contours (edges) are ranked and determined according to their dominancy across the different chromatic and achromatic channels. The color and intensity of the perceived surface are calculated through a diffusive filling-in process of color triggered by the enhanced and biased edges of stimulus formed as a result of oriented double-opponent receptive fields. The model can successfully predict both the assimilative and non-assimilative watercolor effects, as well as a number of "conflicting" visual effects. Furthermore, the model can also predict the classic Craik-O'Brien-Cornsweet (COC) effect. In summary, our proposed computational model is able to predict most of the "conflicting" filling-in effects that derive from edges that have been recently described in the literature, and thus supports the theory that a shared visual mechanism is responsible for the vast variety of the "conflicting" filling-in effects that derive from edges.