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F Figure-Ground Segregation, Once figure-ground segmentation is achieved, Computational Neural the figure region often delineates a zone of addi- Models of tional attentive visual processing. Arash Yazdanbakhsh1 and Ennio Mingolla2 1 Computational Neuroscience and Vision Lab, Detailed Description Department of Psychological and Brain Sciences, Graduate Program for Neuroscience (GPN), The Importance of Figure-Ground Center for Systems Neuroscience (CSN), Center Segregation for Research in Sensory Communications and Visual function in animals has broadly increased Neural Technology (CReSCNT), Boston in complexity and competence across eons of University, Boston, MA, USA fi 2 evolution, with gure-ground perception being Department of Communication Sciences and among the later and more intriguing achieve- Disorders, Bouvé College of Health Sciences, ments of relatively sophisticated visual species. Northeastern University, Boston, MA, USA The importance of figure-ground perception can be seen by considering its advantages to animals that have the capability, as compared to the visual Definition limitations of animals whose visual systems do not support figure-ground perception (Land and Figure-ground segregation refers to the capacity Nilsson 2002). For example, the single-celled of a visual system to rapidly and reliably pick out euglena can use its eyespot and flagellum to orient for greater visual analysis, attention, or awareness, its locomotion in a light field, but it cannot take or preparation for motor action, a region of the account of the edges of individual objects in the visual field (figure) that is distinct from the com- sense of figure-ground for purposes of steering. bined areas of all the rest of the visual field Arrays of visual receptors that sample different (ground). The “figure” region is often, but not directions of the visual field, such as the com- necessarily, bounded by a single closed visual pound eyes of insects or mammalian retinas, contour, and the figure region is often said to are needed for computing figure-ground relations. “own” the boundary between it and any adjacent Successful figure-ground segregation can facili- regions. The figure region is generally experi- tate several subsequent visual tasks, such as object enced as “in front of” (along lines of sight) sur- recognition or guidance of locomotion toward faces of objects that are in the ground region. a goal or around an obstacle. © Springer Science+Business Media, LLC, part of Springer Nature 2019 D. Jaeger, R. Jung (eds.), Encyclopedia of Computational Neuroscience, https://doi.org/10.1007/978-1-4614-7320-6_100660-1 2 Figure-Ground Segregation, Computational Neural Models of Some species of animals can perform figure- regions of higher-than-average (relative to the rest ground perception only in the presence of visual of the scene) activation of certain featural qualities motion, while others can also do so in static envi- (e.g, of color, contrast, motion, or orientation) in ronments. It must be noted that even when the visually topographic “feature maps” make such environment outside an animal is perfectly still, regions likely targets for attention or of overt eye the animal itself may be making voluntary move- movements (Wolfe and Horowitz 2017). ments at a macroscale or involuntary movements An important class of figure-ground phenom- (eye tremor or microsaccades) that can help to ena occurs in cluttered visual scenes, where support figure-ground segregation. the achievement of figure-ground segregation Finally, as we will see in the case of human (or, can amount to the breaking of camouflage. Gibson more generally, primate) figure-ground percep- et al. (1969) demonstrated vivid figure-ground tion, the space-variant arrangement of receptors segregation in random dot kinematograms – dis- on the retina and the associated “cortical magnifi- plays involving some combination of static cation” that concentrates the brain’s processing and moving randomly distributed dots that are resource to the central region of the visual analogous in the time/motion domain to random field is are major factors both in achieving and dot stereograms. In random dot stereograms, two in exploiting figure-ground perception. Voluntary eyes receive related, but different, images, typi- eye movements to an approximate centroid of cally where a region of dots (the figure) is a relatively homogenous visual region can displaced in one eye’s view relative to the other enhance the visual system’s capacity to delineate eye’s view, causing a relative disparity of stimu- an associated figure-ground boundary and thus lated retinal locations that is different from the to perform figure-ground segregation at all. disparities of other corresponding dots in the two Conversely, as long as the eyes are foveating eyes (which form the ground). a “figure” region, a high proportion of cortical The importance of motion to figure-ground tissues is devoted to analysis of that region, cour- perception of ecological scenes has been evident tesy of cortical magnification, thus facilitating since, at least, the seminal paper by Lettvin et al. object recognition or discrimination between (1959) “What the frog’s eye tells the frog’s brain,” two similar object categories. The ability to whose authors noted that frogs of the studied deploy foveal vision while performing figure- species would starve in the presence of freshly ground segregation can thus be viewed as killed flies spread on the ground before them. a “force multiplier” that enables detailed scrutiny Nonetheless, humans and certain other species at relatively low metabolic cost (eye movements), have the capacity to perform figure-ground segre- rather than requiring a reorientation of an entire gation for many complex static scenes, including head or actual approach of the entire body toward pictures – for which endogenous eye movements an environmental region. convey no additional information, as would be the case for views of actual three-dimensional Elementary Cues for Figure-Ground environments. Segregation In visual scenes with little structure, separation The Scope of the Present Article of figure from ground may appear as an easy or This article focuses on computational models of even trivial task. For example, a small dark spot human or primate figure-ground segregation. on an otherwise homogenous light background is We indicate whether models are intended to often said to “pop out” from its background in the address moving or static scenes, or both, and context of a visual search task (e.g., “Find the dark whether they rely on binocular vision. Reasons spot,” Treisman and Gelade 1980). Such isolated, for this focus include (1) that primate visual salient figural properties “call attention to them- systems have the greatest complexity and compe- selves” and are effective as lures that attract sac- tency among known biological systems and cades. A consensus of recent decades holds that (2) that many machine vision systems are Figure-Ground Segregation, Computational Neural Models of 3 designed with the intent of matching if not faces are seen against the uniform background, the exceeding human performance in important borders between the black and white areas belong visual tasks. While many computer vision algo- to the faces and the same for the vase. rithms are designed without reference to animal A good deal of attention in introductory text- examples, it makes sense to study natural visual books is devoted to bistable displays (Fig. 1b–c) systems for inspiration in those domains for when discussing figure-ground perception. which machine performance continues to lag, Although such displays help to make an important as is the case with figure-ground segregation point about the relationship between figure- in cluttered scenes. ground relations and border-ownership, they are a potentially misleading starting point relative What Is Border-Ownership and How Is It to figure-ground perception in natural scenes. Related to Figure-Ground Perception? In bistable displays, volitional manipulation A “figure” object and its borders occlude the by the viewer of semantic categories such as objects and their borders of the background. face or vase can “drive” a resulting percept. Figure 1a shows the famous painting by Leonardo The bistability of labeling border-ownership da Vinci: Mona Lisa occludes the trees, river, is not typical of most normal perceptual settings, bushes, and other objects in the background. which are closer to the depiction of Fig. 1a. Note The foreground borders in (Fig. 1a) obviously also that finding boundaries is trivial in binary- “belong” to the object (i.e., Mona Lisa) in front, valued displays with solid, connected regions, which is the figure. The shape of the borders is such as Fig. 1b and c, as opposed to conditions often informative about the identity of the object of, for example, animal camouflage (Fig. 2). (a human) that is depicted, which is said to “own” the border – thus figure-ground perception can be Psychophysical Antecedents of Recent an important step on the way to object recognition. Computational Neuroscience of Figure- Note however that Evans and Treisman (2005) Ground Perception have shown that people can perform animal detec- The second half of the twentieth century saw a tion in natural scenes with great success even great deal of human psychophysics devoted to the though they are unable to localize the animal, so study of texture segregation, visual segmentation figure-ground perception cannot be said to be a and grouping, and visual search. required step in object recognition. Rather, the Linking these studies – many of which localization of a figure’s borders and proper assign- sought to find fundamental visual “elements” of ment of the ownership of associated borders are perception – was the goal of characterizing how, logical requirements of figure-ground segregation. out of the “blooming, buzzing, confusion” of time-varying visual scenes, our visual systems Border-Ownership Is Independent from the are able to focus on relatively coherent objects Polarity of Contrast Across Borders of interest (figures).

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