A Framework for Symmetric Part Detection in Cluttered Scenes

The role of symmetry in computer vision has waxed and waned in importance during the evolution of the field from its earliest days. At first figuring prominently in support of bottom-up indexing, it fell out of favor as shape gave way to appearance and recognition gave way to detection. With a strong prior in the form of a target object, the role of the weaker priors offered by perceptual grouping was greatly diminished. However, as the field returns to the problem of recognition from a large database, the bottom-up recovery of the parts that make up the objects in a cluttered scene is critical for their recognition. The medial axis community has long exploited the ubiquitous regularity of symmetry as a basis for the decomposition of a closed contour into medial parts. However, today's recognition systems are faced with cluttered scenes, and the assumption that a closed contour exists, i.e. that figure-ground segmentation has been solved, renders much of the medial axis community's work inapplicable. In this article, we review a computational framework, previously reported in Lee et al. (2013), Levinshtein et al. (2009, 2013), that bridges the representation power of the medial axis and the need to recover and group an object's parts in a cluttered scene. Our framework is rooted in the idea that a maximally inscribed disc, the building block of a medial axis, can be modeled as a compact superpixel in the image. We evaluate the method on images of cluttered scenes.Comment: 10 pages, 8 figure

Mind-craft: Exploring the relation between digital visual experience and orientation in visual contour perception

Visual perception depends fundamentally on statistical regularities in the environment to make sense of the world. One such regularity is the orientation anisotropy typical of natural scenes; most natural scenes contain slightly more horizontal and vertical information than oblique information. This property is likely a primary cause of the “oblique effect” in visual perception, in which subjects experience greater perceptual fluently with horizontally and vertically oriented content than oblique. However, recent changes in the visual environment, including the “carpentered” content in urban scenes and the framed, caricatured content in digital screen media presentations, may have altered the level of orientation anisotropy typical in natural scenes. Over a series of three experiments, the current work aims to evaluate whether “digital” visual experience, or visual experience with framed digital content, has the potential to alter the magnitude of the oblique effect in visual perception. Experiment 1 established a novel eye tracking method developed to index the visual oblique effect quickly and reliably using no overt responding other than eye movements. Results indicated that canonical (horizontal and vertical) contours embedded in visual noise were detected more accurately and quickly than oblique contours. For Experiment 2, the orientation anisotropy of natural, urban, and digital scenes was analyzed, in order to compare the magnitude of this anisotropic pattern across each image type. Results indicate that urban scenes contain exaggerated orientation anisotropy relative to natural scenes, and digital scenes possess this pattern to an even greater extent. Building off these two results, Experiment 3 adopts the eye tracking method of Experiment 1 as a pre- post-test measure of the oblique effect. Participants were eye tracked in the contour detection task several times before and after either a “training” session, in which they played Minecraft (Mojang, 2011) for four hours uninterrupted in a darkened room, or a “control” session, in which they simply did not interact with screens for four hours. It was predicted, based on the results of Experiment 2, that several hours of exposure to the caricatured orientation statistics of the digital stimulus would suffice to alter the magnitude of participants’ oblique effect, as indexed by the difference in the post-test relative to the pre-test. While no accuracy differences were observed in this primary manipulation, detection speed for canonical contours did alter significantly in the Minecraft subjects relative to controls. These results indicate that the oblique effect is quite robust at the level of visual contours and is measurable using eye tracking, that digital scenes contain caricatured orientation anisotropy relative to other types of scenes, and that exposure to naturalistic but caricatured scene statistics for only a few hours can alter certain aspects of visual perception

Disambiguating Multi–Modal Scene Representations Using Perceptual Grouping Constraints

In its early stages, the visual system suffers from a lot of ambiguity and noise that severely limits the performance of early vision algorithms. This article presents feedback mechanisms between early visual processes, such as perceptual grouping, stereopsis and depth reconstruction, that allow the system to reduce this ambiguity and improve early representation of visual information. In the first part, the article proposes a local perceptual grouping algorithm that — in addition to commonly used geometric information — makes use of a novel multi–modal measure between local edge/line features. The grouping information is then used to: 1) disambiguate stereopsis by enforcing that stereo matches preserve groups; and 2) correct the reconstruction error due to the image pixel sampling using a linear interpolation over the groups. The integration of mutual feedback between early vision processes is shown to reduce considerably ambiguity and noise without the need for global constraints