Lecture 2 Digital Image Fundamentals Lin ZHANG, PhD School of - - PowerPoint PPT Presentation

Lin ZHANG, SSE, 2016

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 choroid blind spot visual axis

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
• utside 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

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!

• Blind‐spot experiment

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 Lens Iris Retina Related components?

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
• n 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
• bserver 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 distribution spectral power distribution reflectance spectrum Red

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 acquisiton using a linear sensor strip and a circular sensor strip

Image Sensing and Acquisition

• Imaging sensing using sensor strips

Lin ZHANG, SSE, 2016

An example of the digital image acquisition process

Image Sensing and Acquisition

• Imaging sensing using sensor arrays

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

Image Sampling and Quantization

• Spatial resolution—an example

Lin ZHANG, SSE, 2016

Image Sampling and Quantization

• Intensity resolution
• Intensity resolution refers to the smallest discernible change

in intensity level

• The more intensity levels used, the finer the level of detail

discernable in an image

• The number of bits used to quantize intensity is often referred

as the intensity resolution

Number of Bits Number of Intensity Levels Examples 1 2 0, 1 2 4 00, 01, 10, 11 4 16 0000, 0101, 1111 8 256 00110011, 01010101 16 65,536 1010101010101010

Lin ZHANG, SSE, 2016

Image Sampling and Quantization

• Intensity resolution

7 bpp 6 bpp 5 bpp 4 bpp 3 bpp 2 bpp 1 bpp 8 bits per pixel

Lin ZHANG, SSE, 2016

Image Sampling and Quantization

• Image interpolation
• It is a basic tool used in tasks such as zooming, shrinking,

rotating, and geometric corrections

• Traditional methods include nearest neighbor, bilinear, and

bicubic

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

Some Basic Relationships between Pixels

• Neighbors of a pixel

p(x, y) is a pixel p

N4(p): 4‐neighbours

• f p

p

ND(p): diagonal neighbours of p

p

N8(p): 8‐neighbours

• f p

Lin ZHANG, SSE, 2016

Summary

We have looked at:

• Elements of visual perception
• Light and the electromagnetic spectrum
• Image sensing and acquisition
• Image sampling and quantization
• Some basic relationships between pixels

Next time we start to look at techniques for image enhancement

Lin ZHANG, SSE, 2016

Thanks for your attention