Emergence of border-ownership by large-scale consistency and long-range interactions: Neuro-computational model to reflect global configurations

The visual system performs remarkably well to perceive depth order of surfaces without stereo disparity, indicating the importance of figure-ground organization based on pictorial cues. To understand how figure-ground organization emerges, it is essential to investigate how the global configuration of an image is reflected. In the past, many neuro-computational models developed to reproduce figure-ground organization implemented algorithms to give a bias to convex areas. However, in certain conditions, a convex area can be perceived as a hole and a non-convex area as figural. This occurs when the surface properties of the convex area are consistent with the background and, hence, are grouped together in our perception. We argue that large-scale consistency of surface properties is reflected in the border-ownership computation. We developed a model, called DISC2, that first analyzes relationships between two border-ownership signals of all possible combinations in the image. It then enhances signals if they satisfy the following conditions: 1. the two signals fit to a convex configuration, and 2. the surface properties at the locations of the two signals are consistent. The strength of the enhancement decays with distance between the signals. The model gives extremely robust responses to various images with complexities both in shape and depth order. Furthermore, we developed an advanced version of the model ("augmented model") where the global computation above interacts with local computation of curvilinearity, which further enhanced the robust nature of the model. The results suggest the involvement of similar computational processes in the brain for figure-ground organization.

Ultra-High Performance and Low-Cost Architecture of Discrete Wavelet Transforms

This work targets the challenging issue to produce high throughput and low-cost configurable architecture of Discrete wavelet transforms (DWT). More specifically, it proposes a new hardware architecture of the first and second generation of DWT using a modified multi-resolution tree. This approach is based on serializations and interleaving of data between different stages. The designed architecture is massively parallelized and sharing hardware between low-pass and high-pass filters in the wavelet transformation algorithm. Consequently, to process data in high speed and decrease hardware usage. The different steps of the post/pre-synthesis configurable algorithm are detailed in this paper. A modulization in VHDL at RTL level and implementation of the designed architecture on FPGA technology in a NexysVideo board (Artix 7 FPGA) are done in this work, where the performance, the configurability and the generic of our architecture are highly enhanced. The implementation results indicate that our proposed architectures provide a very high-speed data processing with low needed resources. As an example, with the parameters depth order equal 2, filter order equal 2, order quantization equal 5 and a parallel degree P = 16, we reach a bit rate around 3160 Mega samples per second with low used of logic elements ( ≈ 400) and logic registers ( ≈ 700 ).

Examining the Role of Familiarity in the Perception of Depth

Bishop Berkeley suggested that the distance of an object can be estimated if the object’s size is familiar to the observer. It has been suggested that humans can perceive the distance of the object by using such “familiarity” information, but most or many of the prior experiments that found an effect of familiarity were not designed to minimize or eliminate potential influences of: higher cognitive factors on the observers’ responses, or the influences of low-level image features in the visual stimuli used. We looked for the familiarity effect in two experiments conducted both in Russia and Japan. The visual stimuli used were images of three coins used in Russia and Japan. The participants’ depth perception was measured with a multiple-choice task testing the perceived depth-order of the coins. Our expectation was that any effect of “familiarity” on depth perception would only be observed with the coins of the participant’s country. We expected a substantial familiarity effect based on our meta-analysis of the “familiarity” effects observed in prior experiments. But, our results in both experiments showed that the familiarity effect was virtually zero. These findings suggest that the importance of a familiarity effect in depth perception should be reconsidered.

Examining the role of familiarity in the perception of depth

Bishop Berkeley suggested that the distance of an object can be estimated if the object’s size is familiar to the observer. It has been suggested that humans can perceive the distance of the object by using such “familiarity” information, but most or many of the prior experiments that found an effect of familiarity were not designed to minimize, or eliminate potential influences of: higher cognitive factors on the observers' responses, or the influences of low-level image features in the visual stimuli used. We looked for the familiarity effect in two experiments conducted both in Russia and in Japan. The visual stimuli used were images of three coins used in Russia and in Japan. The participants' depth perception was measured with a multiple-choice task testing the perceived depth-order of the coins. Our expectation was that any effect of “familiarity” on depth perception would only be observed with the coins of the participant's country. We expected a substantial familiarity effect based on our meta-analysis of the "familiarity" effects observed in prior experiments. But, our results in both experiments showed that the familiarity effect was virtually zero. These findings suggest that the importance of a familiarity effect in depth perception should be reconsidered.

Border ownership-dependent tilt aftereffect for shape defined by binocular disparity and motion parallax

Discerning objects from their surrounds (i.e., figure-ground segmentation) in a way that guides adaptive behaviors is a fundamental task of the brain. Neurophysiological work has revealed a class of cells in the macaque visual cortex that may be ideally suited to support this neural computation: border ownership cells (Zhou H, Friedman HS, von der Heydt R. J Neurosci 20: 6594–6611, 2000). These orientation-tuned cells appear to respond conditionally to the borders of objects. A behavioral correlate supporting the existence of these cells in humans was demonstrated with two-dimensional luminance-defined objects (von der Heydt R, Macuda T, Qiu FT. J Opt Soc Am A Opt Image Sci Vis 22: 2222–2229, 2005). However, objects in our natural visual environments are often signaled by complex cues, such as motion and binocular disparity. Thus for border ownership systems to effectively support figure-ground segmentation and object depth ordering, they must have access to information from multiple depth cues with strict depth order selectivity. Here we measured in humans (of both sexes) border ownership-dependent tilt aftereffects after adaptation to figures defined by either motion parallax or binocular disparity. We find that both depth cues produce a tilt aftereffect that is selective for figure-ground depth order. Furthermore, we find that the effects of adaptation are transferable between cues, suggesting that these systems may combine depth cues to reduce uncertainty (Bülthoff HH, Mallot HA. J Opt Soc Am A 5: 1749–1758, 1988). These results suggest that border ownership mechanisms have strict depth order selectivity and access to multiple depth cues that are jointly encoded, providing compelling psychophysical support for their role in figure-ground segmentation in natural visual environments. NEW & NOTEWORTHY Figure-ground segmentation is a critical function that may be supported by “border ownership” neural systems that conditionally respond to object borders. We measured border ownership-dependent tilt aftereffects to figures defined by motion parallax or binocular disparity and found aftereffects for both cues. These effects were transferable between cues but selective for figure-ground depth order, suggesting that the neural systems supporting figure-ground segmentation have strict depth order selectivity and access to multiple depth cues that are jointly encoded.

Border-ownership-dependent tilt aftereffect for shape defined by binocular disparity and motion parallax

ABSTRACTDiscerning objects from their surrounds (i.e., figure-ground segmentation) in a way that guides adaptive behaviours is a fundamental task of the brain. Neurophysiological work has revealed a class of cells in the macaque visual cortex that may be ideally suited to support this neural computation: border-ownership cells (Zhou, Friedman, & von der Heydt, 2000). These orientation-tuned cells appear to respond conditionally to the borders of objects. A behavioural correlate supporting the existence of these cells in humans was demonstrated using two-dimensional luminance defined objects (von der Heydt, Macuda, & Qiu, 2005). However, objects in our natural visual environments are often signalled by complex cues, such as motion and depth order. Thus, for border-ownership systems to effectively support figure-ground segmentation and object depth ordering, they must have access to information from multiple depth cues with strict depth order selectivity. Here we measure in humans (of both sexes) border-ownership-dependent tilt aftereffects after adapting to figures defined by either motion parallax or binocular disparity. We find that both depth cues produce a tilt aftereffect that is selective for figure-ground depth order. Further, we find the effects of adaptation are transferable between cues, suggesting that these systems may combine depth cues to reduce uncertainty (Bülthoff & Mallot, 1988). These results suggest that border-ownership mechanisms have strict depth order selectivity and access to multiple depth cues that are jointly encoded, providing compelling psychophysical support for their role in figure-ground segmentation in natural visual environments.SIGNIFICANCE STATEMENTSegmenting a visual object from its surrounds is a critical function that may be supported by “border-ownership” neural systems that conditionally respond to object borders. Psychophysical work indicates these systems are sensitive to objects defined by luminance contrast. To effectively support figure-ground segmentation, however, neural systems supporting border-ownership must have access to information from multiple depth cues and depth order selectivity. We measured border-ownership-dependent tilt aftereffects to figures defined by either motion parallax or binocular disparity and found aftereffects for both depth cues. These effects were transferable between cues, but selective for figure-ground depth order. Our results suggest that the neural systems supporting figure-ground segmentation have strict depth order selectivity and access to multiple depth cues that are jointly encoded.