The Warped Geometry of Visual Space Near a Line Assessed Using a Hyperacuity Displacement Task

Badcock & Westheimer (Spatial Vision, 1(1), 3-11, 1985) showed that a thin vertical line induces nearby zones of attraction and repulsion; this study extends those results by more closely examining the horizontal and vertical extents of the repulsion zone and by using an illusory contour to induce repulsion. The experimental paradigm measures perceived hyperacute displacements of a thin vertical line 10' tall. Halfway through the stimulus, the bright target line was shifted and a lower contrast flanking line added. Conditions equivalent to Badcock & Westheimer replicate their results. Repulsion is observed horizontally from separations of 5' to at least 30' and becomes minimal at 50'. Repulsion also decreases with increasing vertical separation. Another experiment shows that symmetry is not required for repulsion when the flanking line is split into two vertically separated fragments; one fragment alone causes the same amount of repulsion as both fragments together. Finally, it is shown that a flanking contour formed by the grating illusion causes repulsion of the target line in the same manner as a target line defined by luminance.British Petroleum (89A-1204); Defense Advanced Research Projects Agency (90-0083); Air Force Office of Scientific Research (90-0175

Automated construction of a hierarchy of self-organized neural network classifiers

This paper documents an effort to design and implement a neural network-based, automatic classification system which dynamically constructs and trains a decision tree. The system is a combination of neural network and decision tree technology. The decision tree is constructed to partition a large classification problem into smaller problems. The neural network modules then solve these smaller problems. We used a variant of the Fuzzy ARTMAP neural network which can be trained much more quickly than traditional neural networks. The research extends the concept of self-organization from within the neural network to the overall structure of the dynamically constructed decision hierarchy. The primary advantage is avoidance of manual tedium and subjective bias in constructing decision hierarchies. Additionally, removing the need for manual construction of the hierarchy opens up a large class of potential classification applications. When tested on data from real-world images, the automatically generated hierarchies performed slightly better than an intuitive (handbuilt) hierarchy. Because the neural networks at the nodes of the decision hierarchy are solving smaller problems, generalization performance can really be improved if the number of features used to solve these problems is reduced. Algorithms for automatically selecting which features to use for each individual classification module were also implemented. We were able to achieve the same level of performance as in previous manual efforts, but in an efficient, automatic manner. The technology developed has great potential in a number of commercial areas, including data mining, pattern recognition, and intelligent interfaces for personal computer applications. Sample applications include: fraud detection, bankruptcy prediction, data mining agent, scalable object recognition system, email agent, resource librarian agent, and a decision aid agent

Estimation of the parameters of a boundary contour system using psychophysical hyperacuity experiments

Dissertation (Ph.D.)--Boston UniversityVisual hyperacuity enables observers to make accurate judgments of the relative positions of stimuli when the differences are smaller than the size of a single cone in the fovea. Because hyperacuity can serve as a gauge for precisely measuring characteristics of the visual system, it can provide stringent tests for models of the visual system. A variant of the Boundary Contour System (BCS) model is here used to clarify previously unexplained psychophysical hyperacuity results involving contrast polarity, stimulus separation, and sinusoidal masking gratings. Two-dot alignment thresholds were studied by Levi & Waugh (1996) by varying the gap between the dots, with same and opposite contrast polarity with respect to the background, and also with and without band-limited sinusoidal grating masks of different orientations. They found that when the gap between the dots is small (6 arcmin), different patterns of misalignment thresholds are obtained for the same and different contrast polarity conditions. However, when the gap is large (24 arcmin), the same pattern of thresholds was obtained irrespective of contrast polarity. The simulations presented here replicate these findings, producing the same pattern of results when varying the gap between the dots, with same and opposite contrast polarity with respect to the background, and also with and without sinusoidal grating masks of different orientations. The vision model used (BCS) is able to produce these patterns because of its inherent processing using contrast insensitivity, spatial and oriented competition, and long-range completion layers. A novel aspect of the model is the use of sampled field processing, which simplifies the model's equations. Modified Hebbian learning and a neural decision module are proposed as mechanisms that link the vision model's outputs to a decision criterion. All model parts have plausible neurobiological correlates. In addition, psychophysical hyperacuity experiments served to map the limits of inhibitory spatial interactions. The results show that inhibition occurs even when only half of the split flanking line of Badcock & Westheimer (1985b) is used, suggesting that subthreshold activity in units representing the line extends beyond the end of the line. Furthermore, strong inhibition was observed with a flanking illusory line grating

Computational Modeling of Depth-Ordering in Occlusion through Accretion or Deletion of Texture

Understanding the depth-ordering of surfaces in the natural world is one of the most fundamental operations of the primate visual system. Surfaces that undergo accretion or deletion (AD) of texture are always perceived to behind an adjacent surface. An updated ForMotionOcclusion (FMO) model (Barnes & Mingolla, 2013) includes two streams for computing motion signals and boundary signals. The two streams generate depth percepts such that AD signals together with boundary signals generate a farther depth on the occluded side of the boundary. The model fits the classical data (Kaplan, 1969) as well as the observation that moving surfaces tend to appear closer in depth (Royden et al., 1988), for both binary and grayscale stimuli. The recent ‘Moonwalk illusion’ described by Kromrey et al. (2011) upends the classical view that the surface undergoing AD always becomes the background. Here surface that undergoes AD appears to be in front of the surrounding surface; a result of the random flickering noise in the surround. As an additional challenge, we developed an AD display with dynamic depth ordering. A new texture version of the Michotte rabbit hole phenomenon (Michotte, Thinès, & Crabbé, 1964/1991) generates depth that changes in part of the display area. We will show simulations that explain the workings of the new version of the model. The model now uses a simplified push-pull mechanism to generate depth-order signals. Because the FMO model separates the computation of boundaries from the computation of AD signals, it is able to explain the counter-intuitive Moonwalk stimulus. We will show detailed simulations explaining the Moonwalk illusion as well as the textured Michotte rabbit hole phenomena

Influence of socioeconomic factors on pregnancy outcome in women with structural heart disease

OBJECTIVE: Cardiac disease is the leading cause of indirect maternal mortality. The aim of this study was to analyse to what extent socioeconomic factors influence the outcome of pregnancy in women with heart disease.  METHODS: The Registry of Pregnancy and Cardiac disease is a global prospective registry. For this analysis, countries that enrolled ≥10 patients were included. A combined cardiac endpoint included maternal cardiac death, arrhythmia requiring treatment, heart failure, thromboembolic event, aortic dissection, endocarditis, acute coronary syndrome, hospitalisation for cardiac reason or intervention. Associations between patient characteristics, country characteristics (income inequality expressed as Gini coefficient, health expenditure, schooling, gross domestic product, birth rate and hospital beds) and cardiac endpoints were checked in a three-level model (patient-centre-country).  RESULTS: A total of 30 countries enrolled 2924 patients from 89 centres. At least one endpoint occurred in 645 women (22.1%). Maternal age, New York Heart Association classification and modified WHO risk classification were associated with the combined endpoint and explained 37% of variance in outcome. Gini coefficient and country-specific birth rate explained an additional 4%. There were large differences between the individual countries, but the need for multilevel modelling to account for these differences disappeared after adjustment for patient characteristics, Gini and country-specific birth rate.  CONCLUSION: While there are definite interregional differences in pregnancy outcome in women with cardiac disease, these differences seem to be mainly driven by individual patient characteristics. Adjustment for country characteristics refined the results to a limited extent, but maternal condition seems to be the main determinant of outcome

Adaptive Preprocessing for On-Line Learning with Adaptive Resonance Theory (ART) Networks

Neural networks based on Adaptive Resonance Theory (ART) are capable of on-line learning. However, a limiting factor in on-line processing has been the need to preprocess input patterns so that features fall in the range [0.0, 1.0] 1 , typically done with scale-factors that depend on the input range of each feature. This paper demonstrates a method by which the scaling of features becomes adaptive, eliminating the need to batch-process patterns before presenting them to the ART network. The resulting network implementation for on-line learning does not call for any knowledge of the feature signals, ranges or otherwise. A variety of implications of this scheme are analyzed. INTRODUCTION A classifier is capable of on-line learning if it can learn to classify patterns as they are presented without storing the patterns for reference. It has been suggested that ART networks are capable of such on-line learning [1,2]. Even though this has not been proven theoretically, one finds that learn..

Image Understanding Software for Hybrid Hardware

In this Phase I effort, we designed a hybrid image understanding system consisting of neural network software running on parallel hardware and symbolic processing software running on conventional hardware. Such a hybrid system exploits the inherent parallelism in neural systems without sacrificing the efficiency of symbolic processing on conventional hardware. We used automatic target recognition for laser-radar (LADAR) imagery as a specific image understanding problem to demonstrate algorithm feasibility. We demonstrated that segmentation can be done without neural methods, but we also determined that the Boundary Contour System neural model of low-level vision offers great potential for improved segmentation, and we performed an efficiency analysis on a massively parallel computer. Our research into the feature extraction process demonstrated that both neuromorphic (local receptive field) and standard statistical features are necessary for high recognition rates. Since these features..