Does contour classification precede contour grouping in perception of partially visible figures? (original) (raw)

Extraction of Illusory Contours by Perceptual Grouping

Informatik aktuell, 1996

The detection of arbitrary objects in images is still a major challenge in image processing. Many approaches rely on the robust extraction of contours. Object contours, however, are frequently occluded by other objects. How can one deal with this problem? One important source for inspirations in such di cult situations is the human visual system. There are several systems that describe the early stages of visual contour processing up to the level of complex cell responses; only few models describe even end-stopped cells. However, the processes of grouping these local cell responses together are still hardly understood. This paper builds on a previously described model of early vision and investigates the logic of grouping. As a result, we propose a method to treat the induction of contours by corners and line-ends in a uni ed fashion. The properties of these grouping methods are demonstrated for illusory contour stimuli.

Using a model of the human visual system to identify and enhance object contours in natural images

Segmentation of natural images depends on the ability to identify continuous contours that define the boundaries between objects. However, in many natural images (especially those captured in environments where the illumination is largely ambient) continuous contours can be difficult to identify. In spite of this, the human visual system efficiently perceives the contours along the boundaries of occluding objects. In fact, optical illusions, such as the Kanizsa triangle, demonstrate that the human visual system can 'see' object boundaries even when spatial intensity contrasts are totally absent from an image. In searching for the mechanism that generates these 'subjective contours' neurological researchers have found that the 2D image on the retina is mapped onto Layer 4 of the primary visual cortex (V1) and that there are lateral connections within the 6 layers of V1 that might subserve contour completion. This paper builds on a previous model of the early visual system (including the retina, the LGN and the simple cells of V1) by adding lateral interconnections to demonstrate how these interconnections might provide contour completion. Images are presented to show how this model enhances the detection of continuous spatial contours, thus contributing to the segmentation of natural images.

Temporal constraints on the grouping of contour segments into spatially extended objects

Vision Research, 1999

The speed of contour integration was investigated in a task, which can be solved by grouping contour segments into elongated curves. Subjects had to detect a continuous curve, which could be intersected by one or two other curves. At locations where these curves came in close proximity, the assignment of contour segments to the different curves could be based on colinearity. Reaction times exhibited a strong dependence on (1) the presence of intersections among curves, and (2) the context provided by the stimulus set from which individual stimuli were selected. Reaction times were shortest when grouping ot contour segments depended on information at a single location in the visual field. In this condition, responses to stimuli containing an intersection were faster than responses to stimuli that did not. When responses were determined by information from spatially separate locations, responses were delayed, and every intersection slowed the reaction time considerably. This result contrasts with earlier investigations which have suggested that contour integration on the basis of colinearity is performed pre attentively (Field et al., 1993; Kovacs & ]ulesz, 1993), but is in accordance with studies on curve tracing (e.g. Jolicoeur et al., 1986). We propose that the assignment of contour segments to equally coherent curves, a process that may be called figure-figure segregation, is a function of object based attention. Moreover, the protracted reaction times lor some of the stimuli indicate that spread of attention within an object costs time. This implies that object recognition is not always as fast as is sometimes assumed.

Visual Representation of Contour and Shape

Oxford Handbooks Online, 2014

Contours provide an essential source of information about shape and, along contours, points with the greatest magnitude of curvature tend to be most informative. This concentration of information is closely tied to internal generative models of contours employed by the visual system. In going from open to closed contours, the sign of curvature becomes perceptually significant, with negative-curvature (concave) sections of a contour being more informative, and playing an important role in part segmentation. The visual system represents complex shapes by segmenting them into simpler parts ("simpler" because they have less negative curvature). Points of negative minima of curvature provide an important cue for part segmentation; however being entirely local and contour-based features, they cannot fully predict part segmentation. The visual system employs not only a contour-based representation of shape, but also a region-based one, making explicit properties such as axis curvature, local width of the shape, and locally-parallel and locally-symmetric structure. A region-based representation of shape based on Bayesian estimation of the shape skeleton provides a successful account of part segmentation. Moreover, psychophysical results from a variety of domains provide evidence for the representation of region-based geometry by human vision, based on the shape skeleton. Even at the level of so-called "illusory contours," nonlocal region-based geometry exerts a strong influence. We conclude that, as far as the visual representation of shape is concerned, contour geometry cannot ultimately be studied in isolation, but must be considered conjointly with region-based geometry.

Perceiving illusory contours: Figure detection and shape discrimination

Journal of Vision, 2008

We investigate the relationship between illusory figure detection and discrimination of its shape, asking whether these depend on a single, two separate, or two sequential processes. In a simultaneous detection-discrimination experiment, we presented subjects with brief, backward-masked Kanizsa-type patterns consisting of four "pacmen," arranged as if at the corners of a 60-degree parallelogram. Pacman openings were oriented in a quarter of the trials so as to induce an illusory parallelogram. In another quarter, three of the pacmen induced an equilateral triangle. In the remaining half, pacmen were rotated so as not to induce a complete figure. For each trial, subjects reported whether they perceived an illusory figure (detection) and which shape they saw (discrimination), "guessing" the shape even when it was not explicitly perceived. Average detection and discrimination psychometric curves were similar with significantly better-than-chance detection and discrimination beginning at È100 ms. Nevertheless, we found three patterns of performance, representing different detection-discrimination relationships, suggesting these may be separate processes. Detection was not always followed by correct discrimination, especially for poorer performers. Interestingly there were also cases where discrimination was accurate, even without detection, especially in mid-level performers. One detection-discrimination interaction was that only with explicit detection did shape discrimination use local features (such as the orientation of the fourth pacman in the case of an illusory triangle). We suggest that illusory figure detection and shape discrimination are separate tasks, with their relationship being determined individually.

The Effect of Contour Closure on the Rapid Discrimination of Two-Dimensional Shapes

1993

An outline drawing often serves as an excellent depiction of a visual scene. Somehow, our visual system can form two-and tbreedimensional percepts solely from onedimeosional contour information. In ma~mati~, contour &sure plays a key role ia bridging this ~~io~~ gap, however in perceptions the link between closure and shape is unclear. To better understand this relatiousbip, we devised a set of visual search experiments in which subjects discriminate outline figures by meaus of their two-dimensional shape. By modulating the degree of closure of the outlines, we show that two-~me~ional shape processiu g is rapid for closed stimuli but slow for open stbnuli. We further show that search can be characterized as a smooth, monotonic function of the degree of closure, supporting the notion of a perceptual closure continuum. Contour Shape Topology Perceptual organization Visual search CONTOUR CLOSURE AND DISCRIMINATION 983

On the perception of illusory contours

Vision Research, 1994

Illusory contours are invoked by the visual system to account for otherwise inexplicable gaps in the image. We report three sets of novel observations on illusory contours. First, when an illusory square is superimposed on a checkerboard pattern there is a considerable enhancement of the contours so long as they are exactly coincident with the borders of the checks. If the checks are misaligned, on the other hand, the illusory contours associated with the pacman edges disappear and a novel percept emerges: the contours of the checks nearest to the illusory square appear enhanced. This result implies that subjective contours are generated by intermediate-level contour interactions rather than the topdown processes of three-dimensional interpretation. Second, we find that steady fixation for as little as 4 set leads to a complete disappearance of the enhanced illusory contours caused, presumably, by adaptation or "fatigue" of cells that signal these contours. Such adaptation occurred even when the illusory contours were rendered invisible by displaying them on a misaligned checkerboard, suggesting that the adaptation occurs prior to the vetoing of the signal by the checks. Third, we found that illusory contours persist for a surprisingly long time (0.3 set) after the inducing elements have been switched off. These results suggest that the stimuli we have designed ("enhanced illusory contours") might provide a novel probe for dissecting different stages involved in the processing of illusory contours and for understanding how the visual system combines different types of contours to construct object boundaries.

Color contributes to object-contour perception in natural scenes

Journal of Vision, 2017

The magnitudes of chromatic and achromatic edge contrast are statistically independent and thus provide independent information, which can be used for objectcontour perception. However, it is unclear if and how much object-contour perception benefits from chromatic edge contrast. To address this question, we investigated how well human-marked object contours can be predicted from achromatic and chromatic edge contrast. We used four data sets of human-marked object contours with a total of 824 images. We converted the images to the Derrington-Krauskopf-Lennie color space to separate chromatic from achromatic information in a physiologically meaningful way. Edges were detected in the three dimensions of the color space (one achromatic and two chromatic) and compared to human-marked object contours using receiver operating-characteristic (ROC) analysis for a threshold-independent evaluation. Performance was quantified by the difference of the area under the ROC curves (DAUC). Results were consistent across different data sets and edge-detection methods. If chromatic edges were used in addition to achromatic edges, predictions were better for 83% of the images, with a prediction advantage of 3.5% DAUC, averaged across all data sets and edge detectors. For some images the prediction advantage was considerably higher, up to 52% DAUC. Interestingly, if achromatic edges were used in addition to chromatic edges, the average prediction advantage was smaller (2.4% DAUC). We interpret our results such that chromatic information is important for object-contour perception.