Lecture 2 Digital Image Fundamentals Lin ZHANG, PhD School of Software Engineering Tongji University Fall 2016 Lin ZHANG, SSE, 2016
Contents • Elements of visual perception • Light and the electromagnetic spectrum • Image sensing and acquisition • Image sampling and quantization • Some basic relationships between pixels Lin ZHANG, SSE, 2016
Elements of Visual Perception • Structure of the human eye sclera visual axis choroid blind spot Lin ZHANG, SSE, 2016
Elements of Visual Perception • Structure of the human eye • Three membranes enclose the eye: the cornea and sclera, choroid, and retina • At its anterior extreme, the choroid is divided into the ciliary body and the iris; the later contracts or expands to control the amount of light that enters the eye Lin ZHANG, SSE, 2016
Elements of Visual Perception • Structure of the human eye • When the eye is properly focused, light from an object outside the eye is imaged on the retina • Two kinds of light receptors distribute on the retina, cones and rods • Cones are primarily located in the central portion of the retina, called fovea and are sensitive to color; they function best in relatively bright light; so, cone vision is called bright‐light vision • Rods are distributed over the retinal surface; rods serve to give a general overall picture of the field of view; they are not involved in color vision and are sensitive to low levels of illumination; rod vision is called dim‐light vision • Around the region of the emergence of the optic nerve, there is no receptors and results in the so‐called blind spot Lin ZHANG, SSE, 2016
Elements of Visual Perception • Structure of the human eye (more on cone cell) • Humans usually have three kinds of cones with different photopsins , which have different spectral response curves; thus, we have trichromatic vision. • Interestingly, some people have four or more types of cones, giving them tetrachromatic vision Lin ZHANG, SSE, 2016
Elements of Visual Perception • Structure of the human eye (more on cone cell) Three types of color‐sensitive cones in the retina of the human eye, corresponding roughly to red, green, and blue sensitive detectors. Lin ZHANG, SSE, 2016
Elements of Visual Perception • Structure of the human eye (more on cone cell) Lin ZHANG, SSE, 2016
Elements of Visual Perception • Structure of the human eye (more on rod cell) Rod cell Lin ZHANG, SSE, 2016
Elements of Visual Perception • Structure of the human eye (more on rod cell) Wavelength responsiveness of rods compared to that of three types of cones. The dashed gray curve is for rods. Lin ZHANG, SSE, 2016
Elements of Visual Perception • Structure of the human eye Distribution of rods and cones in the retina Lin ZHANG, SSE, 2016
Elements of Visual Perception • Blind‐spot experiment Draw an image similar to that below on a piece of paper (the dot and cross are about 6 inches apart) Close your right eye and focus on the cross with your left eye Hold the image about 20 inches away from your face and move it slowly towards you The dot should disappear! Lin ZHANG, SSE, 2016
Elements of Visual Perception • Image formation in the eye • Muscles within the eye can be used to change the shape of the lens allowing us focus on objects that are near or far away • An image is focused onto the retina causing rods and cones to become excited which ultimately send signals to the brain C is the optical center of the lens Lin ZHANG, SSE, 2016
Elements of Visual Perception • Human eye VS camera VS Related Lens components? Iris Retina Lin ZHANG, SSE, 2016
Elements of Visual Perception • Perceived brightness is not a simple function of intensity • Visual system tends to undershoot or overshoot around the boundary of regions of different intensities, called as “Mach” bands • A region’s perceived brightness does not only depend simply on its intensity, but on its surrounding regions; such a phenomenon is called “simultaneous contrast” Lin ZHANG, SSE, 2016
Elements of Visual Perception • Perceived brightness is not a simple function of intensity An example of Mach bands Lin ZHANG, SSE, 2016
Elements of Visual Perception • Perceived brightness is not a simple function of intensity Lin ZHANG, SSE, 2016
Elements of Visual Perception • Perceived brightness is not a simple function of intensity An example of simultaneous contrast Lin ZHANG, SSE, 2016
Elements of Visual Perception • Perceived brightness is not a simple function of intensity Lin ZHANG, SSE, 2016
Elements of Visual Perception • Perceived brightness is not a simple function of intensity Lin ZHANG, SSE, 2016
Elements of Visual Perception • Optical illusions: our visual systems play lots of interesting tricks on us • It is stilly not fully understood yet Lin ZHANG, SSE, 2016
Elements of Visual Perception Lin ZHANG, SSE, 2016
Contents • Elements of visual perception • Light and the electromagnetic spectrum • Image sensing and acquisition • Image sampling and quantization • Some basic relationships between pixels Lin ZHANG, SSE, 2016
Light and the Electromagnetic Spectrum • Light is just a particular part of the electromagnetic spectrum that can be sensed by the human eye • The electromagnetic spectrum is split up according to the wavelengths of different forms of energy Lin ZHANG, SSE, 2016
Light and the Electromagnetic Spectrum • The colours that we perceive are determined by the nature of the light reflected from an object • In addition to frequency, three basic quantities are used to describe the quality of a chromatic light source • Radiance. It is the total amount of energy that flows from the light source, measured in Watts • Luminance. Gives a measure of the amount of energy an observer perceives , measured in lumens • Brightness. It is a subjective descriptor of light perception that is practically impossible to measure Lin ZHANG, SSE, 2016
Light and the Electromagnetic Spectrum electromagnetic wave spectral power reflectance distribution spectrum spectral power Red distribution Lin ZHANG, SSE, 2016
Contents • Elements of visual perception • Light and the electromagnetic spectrum • Image sensing and acquisition • Image sampling and quantization • Some basic relationships between pixels Lin ZHANG, SSE, 2016
Image Sensing and Acquisition • Image creation based on two factors • Illumination source • Reflection or absorption of energy from that source by the elements of the “scene” being imaged Any examples for these two kinds? Lin ZHANG, SSE, 2016
Image Sensing and Acquisition • Imaging sensors • Single imaging sensor • Line sensor • Array sensor Single imaging sensor Lin ZHANG, SSE, 2016
Image Sensing and Acquisition • Imaging sensors • Single imaging sensor • Line sensor • Array sensor Line sensor Application scenario? Lin ZHANG, SSE, 2016
Image Sensing and Acquisition • Imaging sensors • Single imaging sensor • Line sensor • Array sensor Array sensor, used in ordinary digital camera Lin ZHANG, SSE, 2016
Image Sensing and Acquisition • Imaging sensing using sensor strips Image acquisiton using a linear sensor strip and a circular sensor strip Lin ZHANG, SSE, 2016
Image Sensing and Acquisition • Imaging sensing using sensor arrays An example of the digital image acquisition process Lin ZHANG, SSE, 2016
Contents • Elements of visual perception • Light and the electromagnetic spectrum • Image sensing and acquisition • Image sampling and quantization • Some basic relationships between pixels Lin ZHANG, SSE, 2016
Image Sampling and Quantization • Sampling and quantization will convert a continuous image signal f to a discrete digital form • Digitizing the coordinate values is called sampling • Digitizing the amplitude is called quantization Lin ZHANG, SSE, 2016
Image Sampling and Quantization • Sampling and quantization will convert a continuous image signal f to a discrete digital form Lin ZHANG, SSE, 2016
Image Sampling and Quantization • Sampling and quantization will convert a continuous image signal f to a discrete digital form Result of image sampling and quantization Lin ZHANG, SSE, 2016
Image Sampling and Quantization • Representing images Lin ZHANG, SSE, 2016
Image Sampling and Quantization • Spatial resolution • The spatial resolution of an image is determined by how sampling was carried out • DPI (dots per inch) is used to measure the spatial resolution Note: to say that an image has a resolution 1024*1024 is not a meaningful statement without stating the spatial dimensions encompassed by the image Lin ZHANG, SSE, 2016
Image Sampling and Quantization • Spatial resolution—an example Lin ZHANG, SSE, 2016
Image Sampling and Quantization • Spatial resolution—an example Lin ZHANG, SSE, 2016
Image Sampling and Quantization • Spatial resolution—an example Lin ZHANG, SSE, 2016
Image Sampling and Quantization • Spatial resolution—an example Lin ZHANG, SSE, 2016
Image Sampling and Quantization • Spatial resolution—an example Lin ZHANG, SSE, 2016
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