Mach bands: how many models are possible? Recent experimental findings and modeling attempts

Improving AR-SSVEP Recognition Accuracy Under High Ambient Brightness Through Iterative Learning

Augmented reality-based brain-computer interface (AR-BCI) system is one of the important ways to promote BCI technology outside of the laboratory due to its portability and mobility, but its performance in real-world scenarios has not been fully studied. In the current study, we first investigated the effect of ambient brightness on AR-BCI performance. 5 different light intensities were set as experimental conditions to simulate typical brightness in real scenes, while the same steady-state visual evoked potentials (SSVEP) stimulus was displayed in the AR glass. The data analysis results showed that SSVEP can be evoked under all 5 light intensities, but the response intensity became weaker when the brightness increased. The recognition accuracies of AR-SSVEP were negatively correlated to light intensity, the highest accuracies were 89.35% with FBCCA and 83.33% with CCA under 0 lux light intensity, while they decreased to 62.53% and 49.24% under 1200 lux. To solve the accuracy loss problem in high ambient brightness, we further designed a SSVEP recognition algorithm with iterative learning capability, named ensemble online adaptive CCA (eOACCA). The main strategy is to provide initial filters for high-intensity data by iteratively learning low-light-intensity AR-SSVEP data. The experimental results showed that the eOACCA algorithm had significant advantages under higher light intensities ( 600 lux). Compared with FBCCA, the accuracy of eOACCA under 1200 lux was increased by 13.91%. In conclusion, the current study contributed to the in-depth understanding of the performance variations of AR-BCI under different lighting conditions, and was helpful in promoting the AR-BCI application in complex lighting environments.

Classical center-surround receptive fields facilitate novel object detection in retinal bipolar cells

Antagonistic interactions between center and surround receptive field (RF) components lie at the heart of the computations performed in the visual system. Circularly symmetric center-surround RFs are thought to enhance responses to spatial contrasts (i.e., edges), but how visual edges affect motion processing is unclear. Here, we addressed this question in retinal bipolar cells, the first visual neuron with classic center-surround interactions. We found that bipolar glutamate release emphasizes objects that emerge in the RF; their responses to continuous motion are smaller, slower, and cannot be predicted by signals elicited by stationary stimuli. In our hands, the alteration in signal dynamics induced by novel objects was more pronounced than edge enhancement and could be explained by priming of RF surround during continuous motion. These findings echo the salience of human visual perception and demonstrate an unappreciated capacity of the center-surround architecture to facilitate novel object detection and dynamic signal representation.

Center-surround receptive fields are typically considered to mediate edge detection. Here, by studying retinal bipolar cells responding to flashed and moving stimuli, the authors reveal an additional function: enhanced representation of newly appearing visual items.

Dynamic decorrelation as a unifying principle for explaining a broad range of brightness phenomena

The visual system is highly sensitive to spatial context for encoding luminance patterns. Context sensitivity inspired the proposal of many neural mechanisms for explaining the perception of luminance (brightness). Here we propose a novel computational model for estimating the brightness of many visual illusions. We hypothesize that many aspects of brightness can be explained by a dynamic filtering process that reduces the redundancy in edge representations on the one hand, while non-redundant activity is enhanced on the other. The dynamic filter is learned for each input image and implements context sensitivity. Dynamic filtering is applied to the responses of (model) complex cells in order to build a gain control map. The gain control map then acts on simple cell responses before they are used to create a brightness map via activity propagation. Our approach is successful in predicting many challenging visual illusions, including contrast effects, assimilation, and reverse contrast with the same set of model parameters.

Perceived Dark Rim Artifact in First-Pass Myocardial Perfusion Magnetic Resonance Imaging Due to Visual Illusion

OBJECTIVE

To demonstrate that human visual illusion can contribute to sub-endocardial dark rim artifact in contrast-enhanced myocardial perfusion magnetic resonance images.

MATERIALS AND METHODS

Numerical phantoms were generated to simulate the first-passage of contrast agent in the heart, and rendered in conventional gray scale as well as in color scale with reduced luminance variation. Cardiac perfusion images were acquired from two healthy volunteers, and were displayed by the same gray and color scales used in the numerical study. Before and after k-space windowing, the left ventricle (LV)-myocardium boarders were analyzed visually and quantitatively through intensity profiles perpendicular the boarders.

RESULTS

k-space windowing yielded monotonically decreasing signal intensity near the LV-myocardium boarder in the phantom images, as confirmed by negative finite difference values near the board ranging -1.07 to -0.14. However, the dark band still appears, which is perceived by visual illusion. Dark rim is perceived in the in-vivo images after k-space windowing that removed the quantitative signal dip, suggesting that the perceived dark rim is a visual illusion. The perceived dark rim is stronger at peak LV enhancement than the peak myocardial enhancement, due to the larger intensity difference between LV and myocardium. In both numerical phantom and in-vivo images, the illusory dark band is not visible in the color map due to reduced luminance variation.

CONCLUSION

Visual illusion is another potential cause of dark rim artifact in contrast-enhanced myocardial perfusion MRI as demonstrated by illusory rim perceived in the absence of quantitative intensity undershoot.

Geobia Achievements and Spatial Opportunities in the Era of Big Earth Observation Data

The primary goal of collecting Earth observation (EO) imagery is to map, analyze, and contribute to an understanding of the status and dynamics of geographic phenomena. In geographic information science (GIScience), the term object-based image analysis (OBIA) was tentatively introduced in 2006. When it was re-formulated in 2008 as geographic object-based image analysis (GEOBIA), the primary focus was on integrating multiscale EO data with GIScience and computer vision (CV) solutions to cope with the increasing spatial and temporal resolution of EO imagery. Building on recent trends in the context of big EO data analytics as well as major achievements in CV, the objective of this article is to review the role of spatial concepts in the understanding of image objects as the primary analytical units in semantic EO image analysis, and to identify opportunities where GEOBIA may support multi-source remote sensing analysis in the era of big EO data analytics. We (re-)emphasize the spatial paradigm as a key requisite for an image understanding system capable to deal with and exploit the massive data streams we are currently facing; a system which encompasses a combined physical and statistical model-based inference engine, a well-structured CV system design based on a convergence of spatial and colour evidence, semantic content-based image retrieval capacities, and the full integration of spatio-temporal aspects of the studied geographical phenomena.

AutoCloud+, a “Universal” Physical and Statistical Model-Based 2D Spatial Topology-Preserving Software for Cloud/Cloud–Shadow Detection in Multi-Sensor Single-Date Earth Observation Multi-Spectral Imagery—Part 1: Systematic ESA EO Level 2 Product Generation at the Ground Segment as Broad Context

The European Space Agency (ESA) defines Earth observation (EO) Level 2 information product the stack of: (i) a single-date multi-spectral (MS) image, radiometrically corrected for atmospheric, adjacency and topographic effects, with (ii) its data-derived scene classification map (SCM), whose thematic map legend includes quality layers cloud and cloud–shadow. Never accomplished to date in an operating mode by any EO data provider at the ground segment, systematic ESA EO Level 2 product generation is an inherently ill-posed computer vision (CV) problem (chicken-and-egg dilemma) in the multi-disciplinary domain of cognitive science, encompassing CV as subset-of artificial general intelligence (AI). In such a broad context, the goal of our work is the research and technological development (RTD) of a “universal” AutoCloud+ software system in operating mode, capable of systematic cloud and cloud–shadow quality layers detection in multi-sensor, multi-temporal and multi-angular EO big data cubes characterized by the five Vs, namely, volume, variety, veracity, velocity and value. For the sake of readability, this paper is divided in two. Part 1 highlights why AutoCloud+ is important in a broad context of systematic ESA EO Level 2 product generation at the ground segment. The main conclusions of Part 1 are that ESA EO Level 2 information product is regarded as: (I) necessary-but-not-sufficient pre-condition for the yet-unaccomplished dependent problems of semantic content-based image retrieval (SCBIR) and semantics-enabled information/knowledge discovery (SEIKD) in multi-source EO big data cubes, where SCBIR and SEIKD are part-of the GEO-CEOS visionary goal of a yet-unaccomplished Global EO System of Systems (GEOSS). (II) State-of-the-art definition of EO Analysis Ready Data (ARD) format. (III) Horizontal policy, the goal of which is background developments, in a “seamless chain of innovation” needed for a new era of Space Economy 4.0. In the subsequent Part 2, the AutoCloud+ software system requirements specification, information/knowledge representation, system design, algorithm, implementation and preliminary experimental results are presented and discussed.

Bioplausible multiscale filtering in retino-cortical processing as a mechanism in perceptual grouping

Why does our visual system fail to reconstruct reality, when we look at certain patterns? Where do Geometrical illusions start to emerge in the visual pathway? How far should we take computational models of vision with the same visual ability to detect illusions as we do? This study addresses these questions, by focusing on a specific underlying neural mechanism involved in our visual experiences that affects our final perception. Among many types of visual illusion, 'Geometrical' and, in particular, 'Tilt Illusions' are rather important, being characterized by misperception of geometric patterns involving lines and tiles in combination with contrasting orientation, size or position. Over the last decade, many new neurophysiological experiments have led to new insights as to how, when and where retinal processing takes place, and the encoding nature of the retinal representation that is sent to the cortex for further processing. Based on these neurobiological discoveries, we provide computer simulation evidence from modelling retinal ganglion cells responses to some complex Tilt Illusions, suggesting that the emergence of tilt in these illusions is partially related to the interaction of multiscale visual processing performed in the retina. The output of our low-level filtering model is presented for several types of Tilt Illusion, predicting that the final tilt percept arises from multiple-scale processing of the Differences of Gaussians and the perceptual interaction of foreground and background elements. The model is a variation of classical receptive field implementation for simple cells in early stages of vision with the scales tuned to the object/texture sizes in the pattern. Our results suggest that this model has a high potential in revealing the underlying mechanism connecting low-level filtering approaches to mid- and high-level explanations such as 'Anchoring theory' and 'Perceptual grouping'.

A DOG filter model of the occurrence of Mach bands on spatial contrast discontinuities

The present work proposes a unified model to explain two previously reported properties of the Mach band illusion. The first is the frequently referenced fact that Mach bands are prominently visible at ramps, but practically vanish at intensity steps. The second property, less studied, on the other hand may also be related to the first. It concerns the fact that the width of the illusory Mach bands appears to be a function of the slope of the ramp itself. The model proposed here combines the difference of Gaussians (DOG) model of lateral inhibition in receptive fields with the models of feature detection, based on a holistic approach. The sharpness of discontinuity (SOD) concept for Mach band stimulus has been defined and is related to the slope of the ramp. It is suggested that calculation of SOD leads to an adaptive change in inhibitory surround, a notion that has the support of physiological experiments too.

Mach bands explained by response normalization

Mach bands are the illusory dark and bright bars seen at the foot and knee of a luminance trapezoid. First demonstrated by Ernst Mach in the latter part of the 19th century, Mach bands are a test bed not only for models of brightness illusions but of spatial vision in general. Up until 50 years ago the dominant explanation of Mach Bands was that they were caused by lateral inhibition among retinal neurons. More recently, the dominant idea has been that Mach bands are a consequence of a visual process that generates a sparse, binary description of the image in terms of "edges" and "bars". Another recent explanation is that Mach bands result from learned expectations about the pattern of light typically found on sharply curved surfaces. In keeping with recent multi-scale filtering accounts of brightness illusions as well as current physiology, I show however that Mach bands are most simply explained by response normalization, whereby the gains of early visual channels are adjusted on a local basis to make their responses more equal. I show that a simple one-dimensional model of response normalization explains the range of conditions under which Mach bands occur, and as importantly, the conditions under which they do not occur.

Illusions of brightness, length, and now size: comparable effects derived from the Craik-O'Brien-Cornsweet distributions

Analogous illusions of brightness, line length, and object size occur when stimulus distributions follow the Mach function. Similarly, analogous illusions of brightness and length are produced by stimulus distributions that follow the Craik-O'Brien-Cornsweet function. In Exp. 1, object size is studied using Craik-O'Brien-Cornsweet stimuli composed of 12 clip-art representations of candles, swords, forks, screwdrivers, or pens. Despite their equal dimensions, the three objects next to the taller gradient were judged larger than the three adjacent to the shorter gradient. In Exp. 2, variations of the sword and screwdriver figures having three separations of their constituent objects were used. The illusion produced in Exp. 1 was replicated and, for the figures composed of screwdrivers, increasing separation reduced the hypothesized mis-estimations. The relationship of these Craik-O'Brien-Cornsweet size illusions to their brightness and length counterparts mirrors the relationships previously reported for Mach stimuli; moreover, these findings converge to suggest that the visual system registers size of objects with a frequency code and that illusions of size appear when interactions among neurons disrupt this code.

Some insights into why the perception of Mach bands is strong for luminance ramps and weak or vanishing for luminance steps

In this paper we present some demonstrations concerning the width of Mach bands and henceforth hypothesize certain relations. We show that it is the variation in width of Mach bands in relation to luminance gradients which is responsible for Mach bands being strong for luminance ramps and weak or vanishing for luminance steps. We present the results of the experiments carried out by us using some of these demonstrations to provide support for our claims.

Classical illusions from parallel line figures: evidence for interactions among length-coding neurons

Variations of the Craik-O'Brien, Müller-Lyer, reversed Müller-Lyer, orthogonal Müller-Lyer, and Ponzo illusions were created using 10 to 12 parallel lines. 20 participants judged which of two physically equal lines in the patterns appeared longer. For 4 of the 5 patterns, the parallel lines created illusions which corresponded to those produced by the template figures. The results suggest that length is encoded by frequency of neural response and that the parallel line illusions, as well as their classical counterparts, emerge from interactions which distort this frequency code.

Multi-scale lines and edges in V1 and beyond: brightness, object categorization and recognition, and consciousness

In this paper we present an improved model for line and edge detection in cortical area V1. This model is based on responses of simple and complex cells, and it is multi-scale with no free parameters. We illustrate the use of the multi-scale line/edge representation in different processes: visual reconstruction or brightness perception, automatic scale selection and object segregation. A two-level object categorization scenario is tested in which pre-categorization is based on coarse scales only and final categorization on coarse plus fine scales. We also present a multi-scale object and face recognition model. Processing schemes are discussed in the framework of a complete cortical architecture. The fact that brightness perception and object recognition may be based on the same symbolic image representation is an indication that the entire (visual) cortex is involved in consciousness.

Gradient representation and perception in the early visual system--a novel account of Mach band formation

Recent evidence suggests that object surfaces and their properties are represented at early stages in the visual system of primates. Most likely invariant surface properties are extracted to endow primates with robust object recognition capabilities. In real-world scenes, luminance gradients are often superimposed on surfaces. We argue that gradients should also be represented in the visual system, since they encode highly variable information, such as shading, focal blur, and penumbral blur. We present a neuronal architecture which was designed and optimized for segregating and representing luminance gradients in real-world images. Our architecture in addition provides a novel theory for Mach bands, whereby corresponding psychophysical data are predicted consistently.

Smooth Gradient Representations as a Unifying Account of Chevreul's Illusion, Mach Bands, and a Variant of the Ehrenstein Disk

Recent evidence suggests that the primate visual system generates representations for object surfaces (where we consider representations for the surface attribute brightness). Object recognition can be expected to perform robustly if those representations are invariant despite environmental changes (e.g., in illumination). In real-world scenes, it happens, however, that surfaces are often overlaid by luminance gradients, which we define as smooth variations in intensity. Luminance gradients encode highly variable information, which may represent surface properties (curvature), nonsurface properties (e.g., specular highlights, cast shadows, illumination inhomogeneities), or information about depth relationships (cast shadows, blur). We argue, on grounds of the unpredictable nature of luminance gradients, that the visual system should establish corresponding representations, in addition to surface representations. We accordingly present a neuronal architecture, the so-called gradient system, which clarifies how spatially accurate gradient representations can be obtained by relying on only high-resolution retinal responses. Although the gradient system was designed and optimized for segregating, and generating, representations of luminance gradients with real-world luminance images, it is capable of quantitatively predicting psychophysical data on both Mach bands and Chevreul's illusion. It furthermore accounts qualitatively for a modified Ehrenstein disk.

Masking effect produced by Mach bands on the detection of narrow bars of random polarity

Difficulties arise in measuring masking by Mach bands because very-low-contrast signals distort the bands. [J. Opt. Soc. Am. A 17, 1147 (2000).] Adding narrow luminance increments (bright bars) in the dark Mach band widens the dark band; adding decrements (dark bars) narrows the dark band, and conversely in the bright bands. Randomizing signal polarity prevents observers from using the distortion of the Mach bands as a cue to the presence of the signal. We measured (two-alternative-forced-choice) Mach bands' masking of randomly selected bright (incremental) or dark (decremental) bars. Detection was worse in both dark and bright Mach bands than on the neighboring plateaus. Separate analysis of trials containing only one polarity signal revealed 9-cycle/deg oscillations in performance as a function of location. Oscillations in the two polarities were approximately 180 degrees out of phase.

Mach bands change asymmetrically during solar eclipses

Observations made during two partial eclipses of the Sun show that the Mach bands on shadows cast by the Sun disappear and reappear asymmetrically as an eclipse progresses. These changes can be explained as due to changes in the shape of the penumbras of shadows as the visible portion of the Sun forms crescents of different orientation.

Effect of scenario and experience on interpretation of mach bands

The creation of the radiographic illusion known as a mach band at the intersection of two images of differing radiopacities can be misinterpreted as pathosis in certain situations. After reviewing instances where misinterpretation may occur, this study asked 33 fourth-year dental students and 40 dentists to interpret the same radiograph involving a maxillary central incisor under two different hypothetical scenarios: first, in the case of a patient requesting vital bleaching, and second, where a patient has received recent trauma to the mouth. Results showed that dental students are more susceptible than dentists to misinterpreting as a horizontal root fracture (a mach band illusion) what is known to be the junction of alveolar crestal bone and root. Furthermore when presented with a scenario of trauma, both students and dentists are more likely to mistake what is being seen as being a fracture line.

A two-dimensional model of brightness perception based on spatial filtering consistent with retinal processing

We have applied a multiple scale, 2-D model of brightness perception to a broad range of brightness phenomena. The filters encapsulate only processing that is well established to occur in retinal ganglion cells. Their outputs are then combined in the simplest way compatible with the earliest levels of cortical processing. Not only essential features of a number of the phenomena but also more subtle shading effects are reproduced. Because of the retinal nature of this model, these results would appear to support previous speculation that much of the ground work for brightness perception is performed at the retinal level.

The combination of filters in early spatial vision: a retrospective analysis of the MIRAGE model

Since the discovery of spatial-frequency-tuned channels in the visual system, most theories attempting to account for pattern encoding have assumed that the filters can be independently accessed and flexibly combined. We review here an alternative model, 'MIRAGE', in which the filters are inflexibly combined before pattern analysis. In the MIRAGE model the half-wave rectified outputs of all spatial-frequency channels are combined before locating spatial zero-bounded regions in the neural image, which serve as the spatial primitives for pattern analysis. We describe the evidence that led to this model, and review recent evidence on the rules of filter combination.

A theory of illusory lightness and transparency in monocular and binocular images: the role of contour junctions

A theory of illusory transparency and lightness is described for monocular and binocular images containing X-, T- and I-contour junctions. This theory asserts that the geometric and luminance relationships of contour junctions induce illusory transparency and lightness percepts by causing a phenomenal scission of a homogenous luminance into multiple contributions. Specifically, it is argued that a discontinuous change in contrast along aligned contours that preserve contrast polarity induces a scission of the lower contrast region into a near-transparent surface or an illumination change, and a more distant surface that continues behind behind this near layer. This scission is assumed to cause changes in perceived lightness and/or surface opacity. Discontinuous changes in contrast along contours also are assumed to induce end-cut illusory contours that run roughly perpendicular to the inducing orientation of the contour, both monocularly and binocularly. Binocular illusory contours are shown to be caused by the presence of unmatchable contour terminators. It is argued that the presented theory can provide a unified account of a variety of monocular and binocular illusions that induce uniform transformations in perceived lightness, including neon-color spreading, the Munker-White illusion, Benary's illusion, and illusory monocular and binocular transparency.

Mach-band attenuation by adjacent stimuli: experiment and filling-in simulations

Mach bands are illusory bright and dark bands seen where a luminance plateau meets a ramp, as in half shadows or penumbras. It has previously been shown that Mach bands are attenuated by placing stimuli, such as bars, nearby. It was shown in an experiment in which Mach-band attenuation for bar and Craik-O'Brien stimuli was compared that they are equally effective in attenuating Mach bands. The results suggest that the abrupt luminance transition of a stimulus adjacent to a ramp is responsible for the attenuation. The findings are interpreted in terms of a recent filling-in model of brightness perception and the results of computer simulations of stimuli are shown.