http://science.sciencemag.org/content/sci/201/4359/899.2.full.pdf

Phenomenology of perception.

In this paper, we propose a simple but effective image prior-dark channel prior to remove haze from a single input image. The dark channel prior is a kind of statistics of outdoor haze-free images. It is based on a key observation-most local patches in outdoor haze-free images contain some pixels whose intensity is very low in at least one color channel. Using this prior with the haze imaging model, we can directly estimate the thickness of the haze and recover a high-quality haze-free image. Results on a variety of hazy images demonstrate the power of the proposed prior. Moreover, a high-quality depth map can also be obtained as a byproduct of haze removal.

Single Image Haze Removal Using Dark Channel Prior

List of figures and tables Preface Part I Introduction: 1 What is to be explained? some preliminaries Part II The Basic Model: 2 Model 1: conscious representations are internally consistent and globally distributed 3 The neural basis of conscious experience Part III The Fundamental Role of Context: 4 Model 2: unconscious contexts shape conscious experience 5 Model 3: conscious experience is informative - it always demands some degree of adaptation Part IV Goals and Voluntary Control: 6 Model 4: Goal contexts, spontaneous problem solving, and the stream of consciousness 7 Model 5: volition as ideomotor control of thought and action Part V Attention, self, and conscious self-monitoring: 8 Model 6: attention as control of access to consciousness 9 Model 7 Self as the dominant context of experience and action Part VI Consciousness is Functional: 10 The functions of consciousness Part VII Conclusion: 11 A summary and some future directions Glossary and guide to theoretical claims References Name index, Subject index

A cognitive theory of consciousness

Haze brings troubles to many computer vision/graphics applications. It reduces the visibility of the scenes and lowers the reliability of outdoor surveillance systems; it reduces the clarity of the satellite images; it also changes the colors and decreases the contrast of daily photos, which is an annoying problem to photographers. Therefore, removing haze from images is an important and widely demanded topic in computer vision and computer graphics areas. The main challenge lies in the ambiguity of the problem. Haze attenuates the light reflected from the scenes, and further blends it with some additive light in the atmosphere. The target of haze removal is to recover the reflected light (i.e., the scene colors) from the blended light. This problem is mathematically ambiguous: there are an infinite number of solutions given the blended light. How can we know which solution is true? We need to answer this question in haze removal. Ambiguity is a common challenge for many computer vision problems. In terms of mathematics, ambiguity is because the number of equations is smaller than the number of unknowns. The methods in computer vision to solve the ambiguity can roughly categorized into two strategies. The first one is to acquire more known variables, e.g., some haze removal algorithms capture multiple images of the same scene under different settings (like polarizers).But it is not easy to obtain extra images in practice. The second strategy is to impose extra constraints using some knowledge or assumptions .All the images in this thesis are best viewed in the electronic version. This way is more practical since it requires as few as only one image. To this end, we focus on single image haze removal in this thesis. The key is to find a suitable prior. Priors are important in many computer vision topics. A prior tells the algorithm "what can we know about the fact beforehand" when the fact is not directly available. In general, a prior can be some statistical/physical properties, rules, or heuristic assumptions. The performance of the algorithms is often determined by the extent to which the prior is valid. Some widely used priors in computer vision are the smoothness prior, sparsity prior, and symmetry prior. In this thesis, we develop an effective but very simple prior, called the dark channel prior, to remove haze from a single image. The dark channel prior is a statistical property of outdoor haze-free images: most patches in these images should contain pixels which are dark in at least one color channel. These dark pixels can be due to shadows, colorfulness, geometry, or other factors. This prior provides a constraint for each pixel, and thus solves the ambiguity of the problem. Combining this prior with a physical haze imaging model, we can easily recover high quality haze-free images.

A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

A review of the field of lightness perception from Helmholtz to the present shows the most adequate theories of lightness perception to be the intrinsic image models. Nevertheless, these models fail on 2 important counts: They contain no anchoring rule, and they fail to account for the pattern of errors in surface lightness. Recent work on both the anchoring problem and the problem of errors has produced a new 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 and recent, especially the pattern of errors. It provides a unified account of both illumination-dependent failures of constancy and background-dependent failures of constancy, resolving a number of long-standing puzzles.

An anchoring theory of lightness perception.

The perceived shade of gray depends primarily on the luminance relationship between surfaces percieved to lie in the same plane and not between surfaces that are merely adjacent in the retinal image This result implies that depth perception must precede lightness perception and that lateral inhibition cannot explain lightness constancy

https://science.sciencemag.org/content/sci/195/4274/185.full.pdf

Perceived lightness depends on perceived spatial arrangement

Experiments have recently been reported in which a decisive change in perceived lightness was produced by a change in perceived spatial position, with no important change in the retinal image. A number of previous studies had found little or no such effect. Experiments of the kind that produced these effects and of the kind that do not produce these effects are presented here. The main differences between these two kinds of experiments are discussed. One difference is whether the display allows the target to be part of one ratio in one spatial position but another in the other spatial position. Another difference concerns the range of luminances within the display. Also discussed are the implications of these findings for cognitive vs. S-R theories, the order of processing depth and lightness, laboratory data vs. experience, the role of lateral inhibi­ tion in lightness perception, and theories of lightness perception in general. The percentage of light a surface reflects is called the reflectance (or albedo) of the surface. The phe­ nomenal counterpart of reflectance is called light­ ness. The simplest and most obvious determinant of lightness would seem to be the absolute amount of light (luminance) reflected by a surface, as measured by the eye. This account immediately fails because the amount (as opposed to percentage) of light a surface reflects is determined as much by the amount of light striking the surface as by the reflectance of the surface. The remarkable fact is that we perceive surface reflectance with rough accuracy despite wide variations in illumination. These facts produce what has been called the constancy question, that is, how the apparent lightness of surfaces remains so con­ stant when variations in the amount of illumination create great variations in the absolute amount of light that these surfaces reflect. An additional question, made relevant by theoretical differences, concerns what role, if any, processes of depth perception play in lightness constancy. Consider how the major theo­ ries of lightness perception answer these questions. For many years, the conventional explanation of lightness constancy was that of Helmholtz (1867/1962), who maintained that the level of illumination is un­ consciously taken into account in evaluating the in­ tensity of the reflected light. Such a theory makes intuitive sense since we do seem to be aware of dif­ ferent levels of illumination, both over time and within

/pdf/when-does-perceived-lightness-depend-on-perceived-spatial-2qu8bsruru.pdf

When does perceived lightness depend on perceived spatial arrangement

1 Introduction 2 The Classic Period 3 The Katz Period 4 The Gestalt Period 5 The Contrast Period 6 The Computational Period 7 Computational Models 8 Illumination Perception 9 The Anchoring Problem 10 Errors in Lightness 11 An Anchoring Model of Errors 12 Theories of Lightness 13 Concluding Thoughts Glossary Bibliography

Alan Gilchrist

Papers

An anchoring theory of lightness perception.

Perceived lightness depends on perceived spatial arrangement

When does perceived lightness depend on perceived spatial arrangement

Seeing Black and White

The perception of surface blacks and whites.