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 Signal detection theory (2)
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Articles 1  17 of 17
FullText Articles in Social and Behavioral Sciences
Image Segmentation Using FuzzySpatial Taxon Cut, Lauren Barghout
Image Segmentation Using FuzzySpatial Taxon Cut, Lauren Barghout
MODVIS Workshop
Images convey multiple meanings that depend on the context in which the viewer perceptually organizes the scene. This presents a problem for automated image segmentation, because it adds uncertainty to the process of selecting which objects to include or not include within a segment. I’ll discuss the implementation of a fuzzylogicnaturalvisionprocessing engine that solves this problem by assuming the scene architecture prior to processing. The scene architecture, a standardized naturalsceneperceptiontaxonomy comprised of a hierarchy of nested spatialtaxons. Spatialtaxons are regions (pixelsets) that are figurelike, in that they are perceived as having a contour, are either `thinglike', or a `group ...
‘Edge’ Integration Explains Contrast And Assimilation In A Gradient Lightness Illusion, Michael E. Rudd
‘Edge’ Integration Explains Contrast And Assimilation In A Gradient Lightness Illusion, Michael E. Rudd
MODVIS Workshop
In the ‘phantom’ illusion (Galmonte, Soranzo, Rudd, & Agostini, submitted), either an incremental or a decremental target, when surrounded by a luminance gradient, can to be made to appear as an increment or a decrement, depending on the gradient width. For wide gradients, incremental targets appear as increments and decremental targets appear as decrements. For narrow gradients, the reverse is true. Here, I model these phenomena with a twostage neural lightness theory (Rudd, 2013, 2014) in which local steps in log luminance are first encoded by oriented spatial filters operating on a logtransformed version of the image; then the filter outputs are appropriately integrated along image paths directed towards the target. A contrast gain control mechanism adjusts each filter’s gain on the basis of the outputs of other nearby filters. The weighted contribution of each filter to the target lightness decays exponentially with distance, as do the strengths of the betweenfilter gain modulations. I simulate the lightnesses of incremental and decremental targets as a function of gradient width and show that the model reproduces the key properties of the phantom illusion, even when the gain applied to decremental luminance ...
A Linearized Model For Flicker And Contrast Thresholds At Various Retinal Illuminances, Albert Ahumada, Andrew B. Watson
A Linearized Model For Flicker And Contrast Thresholds At Various Retinal Illuminances, Albert Ahumada, Andrew B. Watson
MODVIS Workshop
Watson and Ahumada (1992 SID) predicted flicker thresholds for bright displays using a temporal contrast sensitivity function (TCSF). Under the assumptions that the falling limb of the TCSF is linear at all retinal illuminations and that the FerryPorter law can be extended to suprathreshold levels, the thresholds for any of the three variables (frequency in Hz, log10 contrast, and retinal illuminance in log Trolands) can be predicted from the other two from a linear model with four parameters.
The Bounded LogOdds Model Of Frequency And Probability Distortion, Hang Zhang, Laurence T. Maloney
The Bounded LogOdds Model Of Frequency And Probability Distortion, Hang Zhang, Laurence T. Maloney
MODVIS Workshop
No abstract provided.
A Signal Detection Experiment With Limited Number Of Trials, Tadamasa Sawada
A Signal Detection Experiment With Limited Number Of Trials, Tadamasa Sawada
MODVIS Workshop
Signal detection theory has been well accepted in vision science to measure human sensitivity to stimuli in a Psychophysical experiment. The theory is formulated so that the measured sensitivity is independent from a response bias (criterion). The formulation is based on an assumption that number of trials in the experiment is infinite but this assumption cannot be satisfied in practice. The assumption came from two normal distributions used in the formulation. The distributions respectively represent a set of signal trial and that of noise trials in the experiment. In this study, I will show how the violation of the assumption ...
Testing The Bayesian Confidence Hypothesis, Wei Ji Ma, Ronald Van Den Berg
Testing The Bayesian Confidence Hypothesis, Wei Ji Ma, Ronald Van Den Berg
MODVIS Workshop
Asking subjects to rate their confidence is one of the oldest procedures in psychophysics. Remarkably, quantitative models of confidence ratings have been scarce. The Bayesian confidence hypothesis (BCH) states that an observer’s confidence rating is monotonically related to the posterior probability of their choice. I will report tests of this hypothesis in two visual categorization tasks: one requiring rapid categorization of a single oriented stimulus, the other a deliberative judgment typically made by scientists, namely interpreting scatterplots. We find evidence against the Bayesian confidence hypothesis in both tasks.
A Conceptual Framework Of Computations In MidLevel Vision, Jonas Kubilius, Johan Wagemans, Hans P. Op De Beeck
A Conceptual Framework Of Computations In MidLevel Vision, Jonas Kubilius, Johan Wagemans, Hans P. Op De Beeck
MODVIS Workshop
The goal of visual processing is to extract information necessary for a variety of tasks, such as grasping objects, navigating in scenes, and recognizing them. While ultimately these tasks might be carried out by separate processing pathways, they nonetheless share a common root in the early and intermediate visual areas. What representations should these areas develop in order to facilitate all of these higherlevel tasks? Several distinct ideas have received empirical support in the literature so far: (i) boundary feature detection, such as edge, corner, and curved segment extraction; (ii) secondorder feature detection, such as the difference in orientation or ...
Metacognition: Using Confidence Ratings For Type 2 And Type 1 Roc Curves, S A. Klein
Metacognition: Using Confidence Ratings For Type 2 And Type 1 Roc Curves, S A. Klein
MODVIS Workshop
In the past five years there has been a surge of renewed interest in metacognition ("thinking about thinking"). The typical experiment involves a binary judgment followed by a multilevel confidence rating. It is a confusing topic because the rating could be made either on one's confidence in the binary response (standard rating Type 1 ROC) or on one's confidence sorted by whether the response was correct (Type 2 ROC). Both are metacognition. After a few remarks on challenging aspects of the Type 2 approach, I will present some interesting results for Type 1 ROC for both memory and ...
Two Correspondence Problems Easier Than One, Aaron Michaux, Zygmunt Pizlo
Two Correspondence Problems Easier Than One, Aaron Michaux, Zygmunt Pizlo
MODVIS Workshop
Computer vision research rarely makes use of symmetry in stereo reconstruction despite its established importance in perceptual psychology. Such stereo reconstructions produce visually satisfying figures with precisely located points and lines, even when input images have low or moderate resolution. However, because few invariants exist, there are no known general approaches to solving symmetry correspondence on real images. The problem is significantly easier when combined with the binocular correspondence problem, because each correspondence problem provides strong nonoverlapping constraints on the solution space. We demonstrate a system that leverages these constraints to produce accurate stereo models from pairs of binocular images ...
Binocular 3d Motion Perception As Bayesian Inference, Martin Lages, Suzanne Heron
Binocular 3d Motion Perception As Bayesian Inference, Martin Lages, Suzanne Heron
MODVIS Workshop
The human visual system encodes monocular motion and binocular disparity input before it is integrated into a single 3D percept. Here we propose a geometricstatistical model of human 3D motion perception that solves the aperture problem in 3D by assuming that (i) velocity constraints arise from inverse projection of local 2D velocity constraints in a binocular viewing geometry, (ii) noise from monocular motion and binocular disparity processing is independent, and (iii) slower motions are more likely to occur than faster ones. In two experiments we found that instantiation of this Bayesian model can explain perceived 3D line motion direction under ...
Computational Modeling Of DepthOrdering In Occlusion Through Accretion Or Deletion Of Texture, Harald Ruda, Gennady Livitz, Guillaume Riesen, Ennio Mingolla
Computational Modeling Of DepthOrdering In Occlusion Through Accretion Or Deletion Of Texture, Harald Ruda, Gennady Livitz, Guillaume Riesen, Ennio Mingolla
MODVIS Workshop
Understanding the depthordering 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 ...
SpatiallyGlobal Integration Of Closed Contours By Means Of ShortestPath In A LogPolar Representation, Terry Kwon, Kunal Agrawal, Yunfeng Li, Zygmunt Pizlo
SpatiallyGlobal Integration Of Closed Contours By Means Of ShortestPath In A LogPolar Representation, Terry Kwon, Kunal Agrawal, Yunfeng Li, Zygmunt Pizlo
MODVIS Workshop
See the one page PDF with abstract and images.
Bayesian Modeling Of 3d Shape Inference From Line Drawings, Seha Kim, Jacob Feldman, Manish Singh
Bayesian Modeling Of 3d Shape Inference From Line Drawings, Seha Kim, Jacob Feldman, Manish Singh
MODVIS Workshop
Human depth comparisons in line drawings reflect the underlying uncertainty of perceived 3D shape. We propose a Bayesian model that estimates the 3D shape from line drawings based on the local and nonlocal contour cues. This model estimates the posterior distribution over depth differences at two points on a line drawing. The likelihood is numerically computed by assuming a generative model, which generates random 3D surfaces and, via projection, random line drawings. The 3D surfaces are inflated from random skeletons and projected into line drawings. Given a novel line drawing, the model samples probable local surfaces based on the relations ...
Formal Aspects Of NonRigidShapeFromMotion Perception, Vicky Froyen, Qasim Zaidi
Formal Aspects Of NonRigidShapeFromMotion Perception, Vicky Froyen, Qasim Zaidi
MODVIS Workshop
Our world is full of objects that deform over time, for example animals, trees and clouds. Yet, the human visual system seems to readily disentangle object motions from nonrigid deformations, in order to categorize objects, recognize the nature of actions such as running or jumping, and even to infer intentions. A large body of experimental work has been devoted to extracting rigid structure from motion, but there is little experimental work on the perception of nonrigid 3D shapes from motion (e.g. Jain, 2011). Similarly, until recently, almost all formal work had concentrated on the rigid case. In the last ...
Appearance Controls Interpretation Of Orientation Flows For 3d Shape Estimation, Steven A. Cholewiak, Romain Vergne, Benjamin Kunsberg, Steven W. Zucker, Roland W. Fleming
Appearance Controls Interpretation Of Orientation Flows For 3d Shape Estimation, Steven A. Cholewiak, Romain Vergne, Benjamin Kunsberg, Steven W. Zucker, Roland W. Fleming
MODVIS Workshop
The visual system can infer 3D shape from orientation flows arising from both texture and shading patterns. However, these two types of flows provide fundamentally different information about surface structure. Texture flows, when derived from distinct elements, mainly signal firstorder features (surface slant), whereas shading flow orientations primarily relate to secondorder surface properties (the change in surface slant).
The source of an image's structure is inherently ambiguous, it is therefore crucial for the brain to identify whether flow patterns originate from texture or shading to correctly infer shape from a 2D image. One possible approach would be to use ...
Can Computational Models Of Shape Explain Object Perception?, Sp Arun, Rt Pramod
Can Computational Models Of Shape Explain Object Perception?, Sp Arun, Rt Pramod
MODVIS Workshop
Despite advances in computation and machine learning, computers are still far behind humans in vision. This is most likely because humans use a sophisticated object representation which is very different from that used in computers today. Another challenge is that object representations in computer vision and human vision have not been systematically compared on the same objects. To address this issue, we measured perceptual dissimilarity between objects in humans in a visual search (taking search difficulty as an index of targetdistracter similarity). We then compared these observed dissimilarities against the dissimilarity predicted by a large number of stateoftheart computational models ...
Object Recognition And Visual Search With A Physiologically Grounded Model Of Visual Attention, Frederik Beuth, Fred H. Hamker
Object Recognition And Visual Search With A Physiologically Grounded Model Of Visual Attention, Frederik Beuth, Fred H. Hamker
MODVIS Workshop
Visual attention models can explain a rich set of physiological data (Reynolds & Heeger, 2009, Neuron), but can rarely link these findings to realworld tasks. Here, we would like to narrow this gap with a novel, physiologically grounded model of visual attention by demonstrating its objects recognition abilities in noisy scenes.
To base the model on physiological data, we used a recently developed microcircuit model of visual attention (Beuth & Hamker, in revision, Vision Res) which explains a large set of attention experiments, e.g. biased competition, modulation of contrast response functions, tuning curves, and surround suppression. Objects are represented by objectview specific neurons, learned via a trace learning approach (Antonelli et al., 2014, IEEE TAMD). A visual cortex model combines the microcircuit with neuroanatomical properties like topdown attentional processing, hierarchicalincreasing receptive field sizes, and synaptic transmission delays. The visual cortex model is complemented by a model of the frontal eye field (Zirnsak et al., 2011, Eur J Neurosci).
We evaluated the model on a realistic object recognition task in which a given target has to be localized in a scene (guided visual search task), using 100 different target objects, 1000 scenes, and two backgrounds. The model achieves an accuracy of 92% at black, and of 71% at whitenoise backgrounds. We found that two of the underlying, neuronal attention mechanisms are prominently relevant for guided visual search: amplification of neurons preferring the target; and suppression of neurons encoding distractors or background noise.