Retinex: Unveiling the Secrets of Computational Vision with Retinex
By Fouad Sabry
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About this ebook
What is Retinex
Color constancy is an example of subjective constancy and a feature of the human color perception system which ensures that the perceived color of objects remains relatively constant under varying illumination conditions. A green apple for instance looks green to us at midday, when the main illumination is white sunlight, and also at sunset, when the main illumination is red. This helps us identify objects.
How you will benefit
(I) Insights, and validations about the following topics:
Chapter 1: Color Constancy
Chapter 2: Color
Chapter 3: Color Vision
Chapter 4: Visual System
Chapter 5: Chromatic Adaptation
Chapter 6: Afterimage
Chapter 7: Trichromacy
Chapter 8: Cone Cell
Chapter 9: Visual Acuity
Chapter 10: Opponent Process
(II) Answering the public top questions about retinex.
(III) Real world examples for the usage of retinex in many fields.
Who this book is for
Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Retinex.
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Titles in the series (100)
Computer Stereo Vision: Exploring Depth Perception in Computer Vision Rating: 0 out of 5 stars0 ratingsUnderwater Computer Vision: Exploring the Depths of Computer Vision Beneath the Waves Rating: 0 out of 5 stars0 ratingsComputer Vision: Exploring the Depths of Computer Vision Rating: 0 out of 5 stars0 ratingsHadamard Transform: Unveiling the Power of Hadamard Transform in Computer Vision Rating: 0 out of 5 stars0 ratingsRetinex: Unveiling the Secrets of Computational Vision with Retinex Rating: 0 out of 5 stars0 ratingsHistogram Equalization: Enhancing Image Contrast for Enhanced Visual Perception Rating: 0 out of 5 stars0 ratingsColor Management System: Optimizing Visual Perception in Digital Environments Rating: 0 out of 5 stars0 ratingsRadon Transform: Unveiling Hidden Patterns in Visual Data Rating: 0 out of 5 stars0 ratingsInpainting: Bridging Gaps in Computer Vision Rating: 0 out of 5 stars0 ratingsGamma Correction: Enhancing Visual Clarity in Computer Vision: The Gamma Correction Technique Rating: 0 out of 5 stars0 ratingsNoise Reduction: Enhancing Clarity, Advanced Techniques for Noise Reduction in Computer Vision Rating: 0 out of 5 stars0 ratingsImage Compression: Efficient Techniques for Visual Data Optimization Rating: 0 out of 5 stars0 ratingsImage Histogram: Unveiling Visual Insights, Exploring the Depths of Image Histograms in Computer Vision Rating: 0 out of 5 stars0 ratingsColor Matching Function: Understanding Spectral Sensitivity in Computer Vision Rating: 0 out of 5 stars0 ratingsHough Transform: Unveiling the Magic of Hough Transform in Computer Vision Rating: 0 out of 5 stars0 ratingsAnisotropic Diffusion: Enhancing Image Analysis Through Anisotropic Diffusion Rating: 0 out of 5 stars0 ratingsVisual Perception: Insights into Computational Visual Processing Rating: 0 out of 5 stars0 ratingsColor Appearance Model: Understanding Perception and Representation in Computer Vision Rating: 0 out of 5 stars0 ratingsHuman Visual System Model: Understanding Perception and Processing Rating: 0 out of 5 stars0 ratingsHomography: Homography: Transformations in Computer Vision Rating: 0 out of 5 stars0 ratingsTone Mapping: Tone Mapping: Illuminating Perspectives in Computer Vision Rating: 0 out of 5 stars0 ratingsColor Model: Understanding the Spectrum of Computer Vision: Exploring Color Models Rating: 0 out of 5 stars0 ratingsAffine Transformation: Unlocking Visual Perspectives: Exploring Affine Transformation in Computer Vision Rating: 0 out of 5 stars0 ratingsLeast Squares: Optimization Techniques for Computer Vision: Least Squares Methods Rating: 0 out of 5 stars0 ratingsJoint Photographic Experts Group: Unlocking the Power of Visual Data with the JPEG Standard Rating: 0 out of 5 stars0 ratingsActive Contour: Advancing Computer Vision with Active Contour Techniques Rating: 0 out of 5 stars0 ratingsColor Profile: Exploring Visual Perception and Analysis in Computer Vision Rating: 0 out of 5 stars0 ratingsScale Invariant Feature Transform: Unveiling the Power of Scale Invariant Feature Transform in Computer Vision Rating: 0 out of 5 stars0 ratingsContour Detection: Unveiling the Art of Visual Perception in Computer Vision Rating: 0 out of 5 stars0 ratingsCanny Edge Detector: Unveiling the Art of Visual Perception Rating: 0 out of 5 stars0 ratings
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Book preview
Retinex - Fouad Sabry
Chapter 1: Color constancy
Subjective constancy is demonstrated by the human color perception system, which guarantees that an object's color is viewed in a consistent manner regardless of the lighting conditions. For example, we perceive a green apple to be green both in the white light of day and in the red light of sunset. It's a great tool for determining what things are.
By noting that the color of an item affects the color of the light reflected from it, Ibn al-Haytham provided an early explanation of color constancy. The visual system, he said, separates light and color, so the quality of the light and the color of the object are combined. This is what he writes,:
In the same way that the color does not travel independently of the light from the item to the eye, neither does the shape of the color travel independently of the light from the object to the eye. The last sentient being can only see them as a jumbled mess because neither the light's form nor the color's being in the colored object can make it through unscathed. Nonetheless, the conscious observer realizes that the seen thing is bright and that the light perceived in the object is distinct from the color.
Humans, animals, and machines all use something called color vision
to distinguish between things based on the wavelengths of light they reflect, transmit, or emit. Cones and rods are the two types of photoreceptors used in the human eye to detect light and send signals to the visual brain, where the signals are interpreted to form an individual's experience of color. To the human eye, a familiar item always seems to have the same hue, regardless of the intensity or spectrum of light reflecting off of it. This phenomenon is known as color constancy.
When the light source is not clearly visible, a phenomenon known as color constancy develops.
It is believed that Land's retinex algorithm, which achieves color constancy, is based on specialized neurons in the primary visual cortex that compute local ratios of cone activity. Because of their ability to process both color and spatial opponency, these specialized cells are known as double-opponent cells.
Nigel Daw discovered double-opposing cells in the retina of goldfish. This, when damaged, causes the achromatopsia syndrome.
In order for color constancy to function, the incident illumination must include light at several wavelengths. The eye's cone cells detect light at a wide range of wavelengths reflected from all objects in the view. The visual system uses this data to make a best guess at the makeup of the light source. The true color
of the object, determined by the wavelengths of light it reflects after this illumination has been subtracted, is then determined. The color we see is based largely on this reflection.
Two methods have been proposed to explain color constancy. Unconscious inference is the first mechanism.
Retinal cones, which are sensitive to light, adapt to the surrounding environment.
The phenomenon of metamerism, in which a person's color perceptions differ between two scenes, can shed light on studies of color constancy. The brain would be able to detect the color and the void color as if they were seen in binocular manner if spatial comparisons occurred later in the visual system, such as in cortical area V4.
The ability to perceive full color (although muted) visuals by simply viewing a photograph with red and gray wavelengths is known as the Land effect.
In an effort to recreate James Clerk Maxwell's early experiments in color, Edwin H. Land noticed this phenomenon. Land discovered that the human visual system would interpret an image as green or blue if only the blue and green wavelengths were present, and ignore the red light. This effect was first described by Land in a 1959 article for Scientific American.
The following is an experimental demonstration of the effect in question. A Mondrian
(named after Piet Mondrian, whose paintings are similar) is a visual representation of many different colored squares or rectangles placed side by side. Three white lights, one each projected through a red filter, a green filter, and a blue filter, illuminate the display. The user's task is to control the brightness of the lights so that a specified area of the display reads as pure white. The reflectance of red, green, and blue light from this apparently white area is then measured. The participant is then asked to tell the experimenter what color another nearby patch is, such as a green one. The investigator then readjusts the lighting to replicate the white patch's measured red, blue, and green light intensities in the green patch's reflection. The individual exhibits color constancy when the green patch remains green, the white patch remains white, and all the other patches retain their original colors.
Many algorithms have been developed to maintain color accuracy in computer vision. Among these are a number of retinex algorithms.
{End Chapter 1}
Chapter 2: Color
The perception of color (or color in British English) is dependent on the electromagnetic spectrum. Color is perceived in relation to an object's light absorption, reflection, emission spectra, and interference; it is not a property of matter itself. There are three types of cone cells that allow humans to see color in the visible spectrum (trichromacy). Other animals, such as bees, who can discern ultraviolet light, may have a broader range of color perception because their eyes include a different number of cone cell types or are sensitive to other wavelengths. Cone cells, which are responsible for detecting light, have varying degrees of sensitivity to different wavelengths of light.
Hue, saturation, and brightness are all perceptual aspects of colors. Both additive color mixing (often used for actual light) and subtractive color mixing are possible (commonly used for materials). Due to metamerism, if the hues are combined properly, they can have the same appearance as a monochromatic source of light. For the sake of organization, colors can be represented by a mathematical color model that abstracts the color space and gives each region of color a number. Therefore, color spaces are an indispensable resource for accurate color rendering across various media, including print, photography, electronic displays, and television. Most people are familiar with the RGB, CMYK, YUV, HSL, and HSV color spaces.
Color perception is so fundamental to the human experience that various hues have been linked to specific feelings, occupations, and even countries. Color-coded territories might have different names and boundaries in various cultures. Color theory is a set of rules for combining colors in a way that is aesthetically pleasant and harmonious, and it is widely utilized in the visual arts. Traditional primary colors are red, yellow, and blue; secondary colors are orange, green, and purple; and tertiary colors are any combination of the two. Color science refers to the study of