# Impacts of Viewing Geometry on Combination of Disparity and Texture Gradient Information

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Diagram. Foundation: Optimal sign combinationMethods: incline discriminationSingle-prompt resultsTwo-signal results: saw slantTwo-prompt results: JNDsConclusions. Layout. Foundation: Optimal prompt combinationMethods: incline discriminationSingle-signal resultsTwo-prompt results: saw slantTwo-prompt results: JNDsConclusions.

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﻿Impacts of Viewing Geometry on Combination of Disparity and Texture Gradient Information James M. Hillis Michael S. Landy Martin S. Banks

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Outline Background: Optimal sign mix Methods: incline segregation Single-prompt outcomes Two-prompt outcomes: saw incline Two-sign outcomes: JNDs Conclusions

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Outline Background: Optimal prompt mix Methods: incline separation Single-sign outcomes Two-sign outcomes: saw incline Two-signal outcomes: JNDs Conclusions

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Sources of Depth Information Motion Parallax Occlusion Stereo Disparity Shading Texture Linear Perspective Etc.

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Depth Cues Motion Parallax Occlusion Stereo Disparity Shading Texture Linear Perspective Etc.

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Optimal Cue Combination: Statistical Approach If the objective is to deliver a gauge with insignificant fluctuation, and the signs are uncorrelated, then the ideal gauge is a weighted normal where

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Optimal Cue Combination: Bayesian Inference Approach From the Bayesian viewpoint, the estimations D and T each outcome in a probability capacity These are joined with an earlier dispersion

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Optimal Cue Combination: Bayesian Inference Approach From Bayes govern, and expecting contingent freedom of the prompts, the back conveyance fulfills:

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Optimal Cue Combination: Bayesian Inference Approach Finally, accepting Gaussian probabilities and earlier, things being what they are the most extreme a posteriori (MAP) assess fulfills: where p remains for the earlier which goes about as though it were an extra signal, and the weights are again corresponding to converse change.

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Previous Qualitative Tests that Cue Weights Depend on Reliability Young, Landy & Maloney (1993) Johnston, Cumming & Landy (1994) Rogers and Bradshaw (1995) Frisby, Buckley & Horsman (1995) Backus and Banks (1999) and so forth and so forth.

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Previous Quantitative Tests that Cue Weights Depend on Reliability Landy & Kojima (2001) – surface signals to area Ernst & Banks (2002) – visual and haptic prompts to estimate Gepshtein & Banks (2003) – visual and haptic signs to measure Knill & Saunders (2003) – surface and dissimilarity prompts to incline

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The Current Study Texture and difference prompts to incline Vary unwavering quality by shifting base inclination (as in Knill & Saunders, 2003) and remove Measure single-signal dependability Compare two-prompt weights to expectations Compare two-signal dependability to forecasts

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Outline Background: Optimal signal blend Methods: incline segregation Single-prompt outcomes Two-prompt outcomes: saw incline Two-prompt outcomes: JNDs Conclusions

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Types of Stimuli Disparity-just: inadequate irregular specks Texture: Voronoi surfaces saw monocularly Two-signal jolts: Voronoi surface stereograms, both clash and no-contention

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Stimuli – Disparity-just

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Stimuli – Voronoi surfaces

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Cue Conflict Stimuli

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Methods Task: 2IFC inclination separation Single-sign and two-signal squares Opposite-sign inclinations blended crosswise over trials in a piece to evade incline adjustment One boost settled, other changed by staircase; a few interleaved staircases Analysis: fit psychometric capacity to assess PSE and JND

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Outline Background: Optimal signal mix Methods: incline segregation Single-prompt outcomes Two-prompt outcomes: saw incline Two-signal outcomes: JNDs Conclusions

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Single-signal JNDs: Texture

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Single-sign JNDs: Disparity

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Single-signal JNDs: Disparity

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Predicted Cue Weights

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Outline Background: Optimal sign mix Methods: incline segregation Single-signal outcomes Two-signal outcomes: saw incline Two-prompt outcomes: JNDs Conclusions

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Determination of PSEs

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Determination of Weights

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Full Two-Cue Dataset ACH JMH

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Effect of Viewing Distance

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Effect of Base Slant

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Outline Background: Optimal sign mix Methods: incline segregation Single-sign outcomes Two-sign outcomes: saw incline Two-sign outcomes: JNDs Conclusions

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Improvement in Reliability with Cue Combination If the ideal weights are utilized: then the subsequent fluctuation is lower than that accomplished by either prompt alone.

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Improvement in JND with 2 Cues

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Conclusion The information are reliable with ideal sign mix Texture weight is expanded with expanding separation and expanding base inclination, as anticipated Two prompt JNDs are for the most part lower than the constituent single-sign JNDs Thus, weights are resolved trial-by-trial, in view of the present jolt data and, specifically, the two single-prompt inclination gauges

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Are Cue Weights Chosen Locally?

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Are Cue Weights Chosen Locally?