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Berkeley COMPSCI 294 - Course Outline

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1. Introduction: Theoretical Developments; From Unconscious Inference to Intrinsic Image: 1.1. Basic ambiguity: luminance vs. lightness; lightness constancy 1.2. Inferring the illuminance: Helmholtz 1.3. Hering’s paradox 1.4. Gestalt and the relational approach 1.5. Contrast theories versus relational theories 1.6. Intrinsic image theories 1.6.1. Edge integration. 1.6.2. Edge classification. 1.6.3. Parsing into layers 1.8. Two weaknesses of the intrinsic image models. 1.8.1. The problem of errors. 1.8.2. The missing anchor. 2. The Anchoring Problem 2.1. A concrete example 2.2. Mapping luminance onto lightness 2.3. Wallach on anchoring: Highest Luminance Rule 2.4. Helson on anchoring: Average Luminance Rule 2.5. Anchoring in intrinsic image models 2.5.1. Anchoring applied to reflectance layer. 2.6. Absolute lightness versus absolute luminance. 2.7. Anchoring versus scaling 3. The Rules of Anchoring in Simple Displays 3.1. What are minimal conditions? 3.2. Highest Luminance versus Average Luminance 3.3. Luminosity Problem: Direct Contradiction to HLR 3.3.1. Wallach on increments: HLR does not apply 3.3.2. Common factor for decrements and increments: Geometric, not photometric 4. Area effects4.1. Surround rule fails 4.2. Highest luminance plus area 4.3. The Area Rule 4.3.1. Other empirical findings 4.4. Luminosity and the Area Rule 4.4.1. Area Rule implies luminosity: 4.4.2. Luminosity induction versus grayness induction. 4.4.3. Phenomena related to the Area Rule 4.4.4. Luminosity threshold and area: Bonato and Gilchrist 4.4.5. Maximum luminance is not the same as the anchor 4.5. Anchoring rules for simple images: A summary 5. Anchoring in Complex Images: A New Theory 5.1. What are the relevant components of a complex image? 5.2. Framework 5.3. Local and global frameworks 5.4. Weighting 5.5. Belongingness and grouping factors 5.5.1. Importance of T-junctions 5.5.1. Role of luminance gradients 5.6. Theoretical value of belongingness. 5.7. The scale normalization effect. 6. Testing the model: The staircase Gelb effect 6.1. Applying the anchoring model 6.2. Compromise 6.3. Weighting factors in the staircase Gelb effect 6.3.1. Configuration. 6.3.2. Articulation. 6.3.3. Field Size. 6.3.4. Perceived size is crucial, not retinal size6.3.5. Insulation 7. Anchoring and the Pattern of Lightness Errors 7.1. Gilchrist (1988): Failures due to classification problem 7.1.2. Staircase Gelb data inconsistent 7.1.3. Anchoring model consistent with Gilchrist (1988) data. 7.2. Source of Errors 8. The Model Applied to Illumination-Dependent Errors 8.1. Errors associated with level of illumination 8.2. Errors associated with reflectance of target. 8.3. Errors associated with reflectance of backgrounds 8.3.1. The key: Increments versus decrements 8.4. Errors associated with articulation level 8.4.1. Burzlaff experiments. 8.4.2. Arend & Goldstein experiments. 8.4.3. Schirillo experiments. 8.4.4. Application of model to depth and lightness. 8.5. Errors associated with field size 9. The Model Applied to Background-Dependent Errors (Simultaneous Lightness Contrast) 9.1. Anchoring component 9.2. The scale normalization component 9.3. Predictions of the model 9.3.1. The locus of the error. 9.3.2. The size of the error. 9.3.3. No contrast effect with double increments 9.3.4. Segregating effect of luminance ramps 9.3.5. Belongingness creates the illusion 9.4. The Benary effect 9.5. White’s illusion. 9.6. Checkerboard contrast.9.7. Agostini and Proffitt: Common fate. 9.8. Laurinen and Olzak. 9.9. Wolff illusion 9.10. Depth manipulations: 9.11. Adelson’s corrugated Mondrian 9.11.1. Wishart experiments. 10. Brightness Induction: Contrast or Anchoring? 10.1. Induction 10.2. Area effects 10.3. Separation 10.4. Depth separation: Gogel and Mershon 10.5. Gelb effect: Contrast versus anchoring 10.6. Summary: application of model to reduction conditions. 10.6.1. Lightness versus brightness 11. Tests of scaling normalization effect 11.1. Crispening effect 12. Problems for the Intrinsic Image Models 12.1. Problem of the staircase Gelb effect data. 12.2. Problem of area effects. 12.3. Problem of articulation effects. 12.4. Problem of errors. 12.5. A response paradox. 13. New Model vs. Intrinsic Image Models 13.1. Classified edge integration 13.2. A Critical Test 13.2.1. Method and results: New model wins. 13.2.2. Low articulation replication. 13.2.3. Review of 1983 data. 14. Evaluating The Model14.1. Range of application. 14.2. Prediction of errors. 14.3. Unification of constancy failures. 14.4. Rigor and concreteness. 14.5. The role of perceived illumination. 14.6. Choice of scaling rule for the global framework. 15. Debt to the Early Literature 15.1. Koffka 15.2. Compromise and intelligence 15.3. The aperture problem for lightness An Anchoring Theory of Lightness Perception1Gilchrist, A., 2Kossyfidis, C., 3Bonato, F., 4Agostini, T., 3Cataliotti, J., 1Li, X., 5Spehar, B., 1Szura, J.1Rutgers University2Deceased3St. Peters College4University of Trieste5University of New South WalesThis paper is dedicated to the memory of Christos Kossyfidis.Running Head: AN ANCHORING THEORY OF LIGHTNESS PERCEPTIONAbstractA review of the field of lightness perception from Helmholtz to the present shows the most adequatetheories of lightness perception to be the intrinsic image models. Nevertheless these models fail on twoimportant counts: they contain no anchoring rule and they fail to account for the pattern of errors insurface lightness. Recent work on both the anchoring problem and the problem of errors has produced anew model of lightness perception, one that is qualitatively different from the intrinsic image models.The new model, which is based on a combination of local and global anchoring of lightness values,appears to provide an unprecedented account of a wide range of empirical results, both classical andrecent, especially the pattern of errors. It provides a unified account of both illumination-dependentfailures of constancy and background-dependent failures of constancy, resolving a number oflong-standing puzzles.1. Introduction: Theoretical Developments; From Unconscious Inference to IntrinsicImage: 1.1. Basic ambiguity: luminance vs. lightness; lightness constancyWe present here a new theory of how the visual system assigns lightness values, or perceived black,white, or gray values to various regions of the retinal image. The theory grew out of two problemsfacing what are probably the most advanced models of lightness


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Berkeley COMPSCI 294 - Course Outline

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