Search

Segmentation and recognition of objects in a visual scene are two problems that are hard to solve separately from each other. When segmenting an ambiguous scene, it is helpful to already know the present objects and their shapes. However, for recognizing an object in clutter, one would like to consider its isolated segment alone to avoid confounds from features of other objects. Border-ownership cells (Zhou et al., 2000) appear to play an important role in segmentation, as they signal the side-of-figure of artificial stimuli. The present work explores the role of border-ownership cells in dorsal macaque visual areas V2 and V3 in the segmentation of natural object stimuli and locally ambiguous stimuli. We report two major results. First, compared with previous estimates, we found a smaller percentage of cells that were consistent across artificial stimuli used previously. Second, we found that the average response of those neurons that did respond consistently to the side-of-figure of artificial stimuli also consistently signaled, as a population, the side-of-figure for borders of single faces, occluding faces and, with higher latencies, even stimuli with illusory contours, such as Mooney faces and natural faces completely missing local edge information. In contrast, the local edge or the outlines of the face alone could not always evoke a significant border-ownership signal. Our results underscore that border ownership is coded by a population of cells, and indicate that these cells integrate a variety of cues, including low-level features and global object context, to compute the segmentation of the scene. [ABSTRACT FROM AUTHOR]

Determining whether a region belongs to the interior or exterior of a shape (figure-ground segregation) is a core competency of the primate brain, yet the underlying mechanisms are not well understood. Many models assume that figure-ground segregation occurs by assembling progressively more complex representations through feedforward connections, with feedback playing only a modulatory role. We present a dynamical model of figure-ground segregation in the primate ventral stream wherein feedback plays a crucial role in disambiguating a figure’s interior and exterior. We introduce a processing strategy whereby jitter in RF center locations and variation in RF sizes is exploited to enhance and suppress neural activity inside and outside of figures, respectively. Feedforward projections emanate from units that model cells in V4 known to respond to the curvature of boundary contours (curved contour cells), and feedback projections from units predicted to exist in IT that strategically group neurons with different RF sizes and RF center locations (teardrop cells). Neurons (convex cells) that preferentially respond when centered on a figure dynamically balance feedforward (bottom-up) information and feedback from higher visual areas. The activation is enhanced when an interior portion of a figure is in the RF via feedback from units that detect closure in the boundary contours of a figure. Our model produces maximal activity along the medial axis of well-known figures with and without concavities, and inside algorithmically generated shapes. Our results suggest that the dynamic balancing of feedforward signals with the specific feedback mechanisms proposed by the model is crucial for figure-ground segregation.

Camouflaged animals that have very similar textures to their surroundings are difficult to detect when stationary. However, when an animal moves, humans readily see a figure at a different depth than the background. How do humans perceive a figure breaking camouflage, even though the texture of the figure and its background may be statistically identical in luminance? We present a model that demonstrates how the primate visual system performs figure–ground segregation in extreme cases of breaking camouflage based on motion alone. Border-ownership signals develop as an emergent property in model V2 units whose receptive fields are nearby kinetically defined borders that separate the figure and background. Model simulations support border-ownership as a general mechanism by which the visual system performs figure–ground segregation, despite whether figure–ground boundaries are defined by luminance or motion contrast. The gradient of motion- and luminance-related border-ownership signals explains the perceived depth ordering of the foreground and background surfaces. Our model predicts that V2 neurons, which are sensitive to kinetic edges, are selective to border-ownership (magnocellular B cells). A distinct population of model V2 neurons is selective to border-ownership in figures defined by luminance contrast (parvocellular B cells). B cells in model V2 receive feedback from neurons in V4 and MT with larger receptive fields to bias border-ownership signals toward the figure. We predict that neurons in V4 and MT sensitive to kinetically defined figures play a crucial role in determining whether the foreground surface accretes, deletes, or produces a shearing motion with respect to the background. [ABSTRACT FROM AUTHOR]

Segmentation of a visual scene in “figure” and “ground” is essential for perception of the three-dimensional layout of a scene. In cases of bi-stable perception, two distinct figure–ground interpretations alternate over time. We were interested in the temporal dynamics of these alternations, in particular when the same image is presented repeatedly, with short blank periods in-between. Surprisingly, we found that the intermittent presentation of Rubin’s classical “face-or-vase” figure, which is frequently taken as a standard case of bi-stable figure–ground perception, often evoked perceptual switches during the short presentations and stabilization was not prominent. Interestingly, bi-stable perception of Kanizsa’s anomalous transparency figure did strongly stabilize across blanks. We also found stabilization for the Necker cube, which we used for comparison. The degree of stabilization (and the lack of it) varied across stimuli and across individuals. Our results indicate, against common expectation, that the stabilization phenomenon cannot be generally evoked by intermittent presentation. We argue that top-down feedback factors such as familiarity, semantics, expectation, and perceptual bias contribute to the complex processes underlying the temporal dynamics of bi-stable figure–ground perception. [ABSTRACT FROM AUTHOR]

We argued that borderline completion does not explain the completion, that the computation of border-ownership (BOWN) causes illusory signals, and that neurons activated at illusory contours represent BOWN. Although most commentaries show support to our view, they further emphasized the importance of feedback and also pointed out some examples challenging our view. The signal processing in the hierarchy and the classification of neurons are also discussed. In this reply, we explain our position on a dynamic feedback system reflecting the global configuration, and clarify our view on completion, by examining the example figures and neurophysiological data indicated in the commentaries. [ABSTRACT FROM AUTHOR]

The perception of figure-ground organization is a highly context-sensitive phenomenon. Accumulating evidence suggests that the so-called completion phenomenon is tightly linked to this figure-ground organization. While many computational models have applied borderline completion algorithms based on the detection of boundary alignments, we point out the problems of this approach. We hypothesize that completion is a result of computing the figure-ground organization. Specifically, the global interactions in the neural network activate the “border-ownership” sensitive neurons at the location where no luminance contrast is given and this activation corresponds to the perception of illusory contours. The implications of this result to the general property of emerging Gestalt percepts are discussed. [ABSTRACT FROM AUTHOR]

Abstract: We have proposed the computational model for border-ownership (BO) determination based on the contrast configurations within a certain range that extends beyond the classical receptive field (CRF). In this study, we adopt two crucial functions of the surrounding modulation reported by the recent physiological studies; (1) changes in functional connection depending on the amplitude of contrast, and (2) a variety of surrounding suppression/facilitation depending on the orientation and retinotopic position of surrounding stimuli relative to the CRF. Simulation results show that the model reproduces the major characteristics of BO selective neurons. [Copyright &y& Elsevier]

We investigate the neural mechanisms for border-ownership (BO) determination, specifically whether the determination of BO is plausible from the contrast configuration within a certain range that extends beyond the classical receptive fields. The relevance of the contrast is suggested since the majority of BO-selective neurons in V2 and V4 show co-selectivity to the contrast. We hypothesize that the spatial structure of surrounding inhibition/excitation recently revealed in V1, or the similar structure in V2, is a key to integrate surrounding contrast to determine BO. The model reproduces a range of the neuronal activities responding to complex figures including occlusion. [Copyright &y& Elsevier]

We investigated psychophysically whether feature-based attention modulates the perception of figure-ground (F-G) segregation and, based on the results, we investigated computationally the neural mechanisms underlying attention modulation. In the psychophysical experiments, the attention of participants was drawn to a specific motion direction and they were then asked to judge the side of figure in an ambiguous figure with surfaces consisting of distinct motion directions. The results of these experiments showed that the surface consisting of the attended direction of motion was more frequently observed as figure, with a degree comparable to that of spatial attention (Wagatsuma et al., 2008). These experiments also showed that perception was dependent on the distribution of feature contrast, specifically the motion direction differences. These results led us to hypothesize that feature-based attention functions in a framework similar to that of spatial attention. We proposed a V1-V2 model in which feature-based attention modulates the contrast of low-level feature in V1, and this modulation of contrast changes directly the surround modulation of border-ownership-selective cells in V2; thus, perception of F-G is biased. The model exhibited good agreement with human perception in the magnitude of attention modulation and its invariance among stimuli. These results indicate that early-level features that are modified by feature-based attention alter subsequent processing along afferent pathway, and that such modification could even change the perception of object.