ground organization
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2021 ◽  
Author(s):  
Naoki Kogo ◽  
Vicky Froyen

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.


Author(s):  
Tandra Ghose ◽  
Mary A. Peterson

AbstractIn figure–ground organization, the figure is defined as a region that is both “shaped” and “nearer.” Here we test whether changes in task set and instructions can alter the outcome of the cross-border competition between figural priors that underlies figure assignment. Extremal edge (EE), a relative distance prior, has been established as a strong figural prior when the task is to report “which side is nearer?” In three experiments using bipartite stimuli, EEs competed and cooperated with familiar configuration, a shape prior for figure assignment in a “which side is shaped?” task.” Experiment 1 showed small but significant effects of familiar configuration for displays sketching upright familiar objects, although “shaped-side” responses were predominantly determined by EEs. In Experiment 2, instructions regarding the possibility of perceiving familiar shapes were added. Now, although EE remained the dominant prior, the figure was perceived on the familiar-configuration side of the border on a significantly larger percentage of trials across all display types. In Experiment 3, both task set (nearer/shaped) and the presence versus absence of instructions emphasizing that familiar objects might be present were manipulated within subjects. With familiarity thus “primed,” effects of task set emerged when EE and familiar configuration favored opposite sides as figure. Thus, changing instructions can modulate the weighing of figural priors for shape versus distance in figure assignment in a manner that interacts with task set. Moreover, we show that the influence of familiar parts emerges in participants without medial temporal lobe/ perirhinal cortex brain damage when instructions emphasize that familiar objects might be present.


2021 ◽  
Vol 6 (3) ◽  
pp. 1388
Author(s):  
Rudi Suherman

The study aims to find out the attention system, especially, the focus of attention patterns, on news articles related to the Harvey Weinstein sexual harassment allegations case. Qualitative descriptive methods were used in this study. The data was taken from the BBC and the stylist's broadcasting article about the case. The results show that the figure-ground organization as an element in the focus of attention patterns is mapped and clearly constructed. Weinstein, who fits the role of the perpetrator, reasoned on the victim and the attention of the news writer's article that formed the ground, while the victim and other entities set the ground in the sentence. This reasoned and background phenomenon shows that the cognition of victims and news writers places more attention and perception on perpetrators than other entities.


Poetics Today ◽  
2020 ◽  
Vol 41 (4) ◽  
pp. 641-668
Author(s):  
Pavlo Shopin

In this article, the author analyzes spatial metaphors for voice in the work of Herta Müller. His analysis shows how she evokes spatial experience to convey her vision of voice. Drawing on conceptual metaphor theory and Viktor Shklovsky’s concept of defamiliarization, the author argues that Müller defamiliarizes conventional spatial language used to make sense of voice. She encourages her readers to recognize the figurative meaning of such language and invites them to build new and original associations between space and voice. The analysis focuses on verticality, figure-ground organization, motion, and container image schema as source domains for voice. The author’s research demonstrates that voice is associated with different sensory experiences and does not exist as a purely acoustic image. He claims that metaphorical conceptualization helps understand voice because the latter has different meanings depending on the context and is a complex physical, linguistic, and cultural phenomenon. The article concludes that Müller both relies on and defamiliarizes the tentative yet motivated association between space and voice.


AI ◽  
2020 ◽  
Vol 1 (4) ◽  
pp. 436-464
Author(s):  
Sudarshan Ramenahalli

Figure Ground Organization (FGO)-inferring spatial depth ordering of objects in a visual scene-involves determining which side of an occlusion boundary is figure (closer to the observer) and which is ground (further away from the observer). A combination of global cues, like convexity, and local cues, like T-junctions are involved in this process. A biologically motivated, feed forward computational model of FGO incorporating convexity, surroundedness, parallelism as global cues and spectral anisotropy (SA), T-junctions as local cues is presented. While SA is computed in a biologically plausible manner, the inclusion of T-Junctions is biologically motivated. The model consists of three independent feature channels, Color, Intensity and Orientation, but SA and T-Junctions are introduced only in the Orientation channel as these properties are specific to that feature of objects. The effect of adding each local cue independently and both of them simultaneously to the model with no local cues is studied. Model performance is evaluated based on figure-ground classification accuracy (FGCA) at every border location using the BSDS 300 figure-ground dataset. Each local cue, when added alone, gives statistically significant improvement in the FGCA of the model suggesting its usefulness as an independent FGO cue. The model with both local cues achieves higher FGCA than the models with individual cues, indicating SA and T-Junctions are not mutually contradictory. Compared to the model with no local cues, the feed-forward model with both local cues achieves ≥8.78% improvement in terms of FGCA.


2020 ◽  
Author(s):  
Sandersan Onie ◽  
Mary Peterson ◽  
Mike Le Pelley ◽  
Steven Most

Many factors affect figure-ground segregation, but the contributions of attention and reward history to this process is uncertain. We conducted two experiments to investigate whether reward learning influences figure assignment, and whether this relationship was mediated by attention. Participants learned to associate certain shapes with a reward contingency: during a learning phase, they chose between two shapes on each trial, with subsets of shapes associated with high-probability win, low-probability win, high-probability loss, and low-probability loss. In a test phase, participants were given a figure ground task, in which they indicated which of two regions that shared a contour they perceived as the figure (high probability-win and low-probability win shapes were pitted against each other, as were high probability-loss and low probability-loss shapes). The results revealed that participants had learned the reward contingencies and that, following learning, attention was reliably drawn to the optimal stimulus. Despite this, neither reward history nor the resulting attentional allocation influenced figure-ground organization.


2019 ◽  
Vol 19 (10) ◽  
pp. 35
Author(s):  
Tandra Ghose ◽  
Ananya Mukherjee
Keyword(s):  

Author(s):  
Anders Christian Buch

Purpose The purpose of this paper is to critique the metaphor of “shadow organizing” in relation to researchers’ allegedly ontological commitment to processual metaphysics. Design/methodology/approach The paper focuses on the association of “shadow organizing” with post-epistemologies that are grounded in process ontology. The investigation examines aspects of relational thinking and is guided by John Dewey and Arthur Bentley’s genealogical reconstruction of modes of inquiry. Findings Inquiry is construed in either substantialist or relational ways by researchers. By using the metaphor of “shadow organizing,” the relational aspects of organizational phenomena are prioritized for explorative purposes. Other research objectives are aided by substantialist modes of inquiry. It is the argument of the paper, however, that relational research approaches need not make commitment to process ontology, and that the relational ambitions imbued in the metaphor of shadow organizing are in fact better honored for their methodological virtues. Originality/value The paper’s original contribution consists in critiquing post-epistemological attempts to ground organization studies in ontological first principles of process metaphysics. The paper argues that the metaphor of “shadow organizing” is a promising concept that is better appreciated as a methodological move than an ontological commitment.


eNeuro ◽  
2019 ◽  
Vol 6 (3) ◽  
pp. ENEURO.0479-18.2019 ◽  
Author(s):  
Brian Hu ◽  
Rüdiger von der Heydt ◽  
Ernst Niebur

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