Elements of Visual Perception

Elements of Visual Perception Outline Structure of the human eye Image formation in the human eye Brightness adaptation and discrimination

Elements of Visual Perception Structure of the human eye

Elements of Visual Perception Structure of the human eye The cornea and sclera outer cover The choroid Ciliary body Iris diaphragm Lens The retina (two kinds of receptors) Cones vision (photopic/bright-light vision) : centered at fovea, highly sensitive to color Rods (scotopic/dim-light vision) : general view Blind spot Cornea: 角膜， Sclera: 巩膜，choroid: 脉络膜，ciliary: 睫状体，retina: 视网膜，cones: 圆锥细胞，rod: 杆状细胞，fovea: 中央凹， blind spot: 盲点

Elements of Visual Perception Image formation in the human eye Flexible lens: the principle difference from an ordinary optical lens. Controlled by the tension in the fibers of the ciliary body To focus on distant objects – flattened To focus on objects near eye – thicker Near-sighted and far-sighted

Image formation in the eye Brain Light receptor radiant energy electrical impulses

Elements of Visual Perception Brightness adaptation Dynamic range of human visual system: 10-6~104 mL (millilambert) Can not accomplish this range simultaneously The current sensitivity level of the visual system is called brightness adaptation level

Elements of Visual Perception Brightness adaptation

Elements of Visual Perception Brightness discrimination Weber ratio (the experiment) : I: the background illumination : the increment of illumination Small Weber ratio indicates good discrimination Larger Weber ratio indicates poor discrimination

Elements of Visual Perception Brightness discrimination

Elements of Visual Perception Psycho-visual effects The perceived brightness is not a simple function of intensity Mach band pattern Simultaneous contrast Optical illusion

The perceived brightness is not a simple function of intensity Psychovisual effects The perceived brightness is not a simple function of intensity Mach band pattern Simultaneous contrast And more… (see link)

Elements of Visual Perception Mach band pattern The visual system tends to undershoot or overshoot the boundary of regions of different intensities. In this image, the intensity of the stripes is constant., but we actually perceive a brightness pattern that is strongly scalloped.

Elements of Visual Perception Simultaneous contrast This phenomenon is related to the fact that a region’s perceived brightness does not depend simply on its intensity. In the three images, all the center squares have exactly the same intensity, however, they appear to the eye to become darker as the background gets lighter.

Elements of Visual Perception Optical illusion The eye fills in nonexisting information or wrongly perceives geometrical properties of objects.

Elements of Visual Perception Optical illusion

Elements of Visual Perception Optical illusion

Sampling, Quantization and Other Simple Operations Dr. Jiajun Wang School of Electronics & Information Engineering Soochow University

Sampling, Quantization, and Operations Outline Image formation model Uniform sampling Uniform quantization Digital image representation Relationships between pixels Arithmetic operations Logical operations

Sampling, Quantization and Other Simple Operations Image Formation Model Monochrome image may be characterized by two components : Illumination: Reflectance: Typical values of the illumination and reflectance: Illumination: sun on earth: 90,000 lm/m2 on a sunny day; 10,000 lm/m2 on a cloud day; moon on clear evening: 0.1 lm/m2; in a commercial office is about 1000 lm/m2 Reflectance: 0.01 for black velvet, 0.65 for stainless steel, 0.80 for flat-white wall paint, 0.90 for silver-plated metal, and 0.93 for snow

Sampling, Quantization, and Operations Uniform sampling and quantization Sampling digitalized in spatial domain Quantization digitalized in amplitude Sampling: digitizing in the coordinate values is called sampling. The sampling process may be viewed as partitioning the xy plane into a grid, with the center of each grid being a pixel. Quantization: digitizing the amplitude values into discrete gray values. Due to processing, storage, and sampling hardware considerations, the number of gray levels typically is an integer power of 2.

Sampling, Quantization, and Operations Uniform sampling and quantization Sampling: digitizing in the coordinate values is called sampling. Quantization: digitizing the amplitude values is called quantization.

Sampling, Quantization, and Operations Digital image representation The result of sampling and quantization is a matrix of real numbers. Figure 2.18 shows the coordinate conversion used throughout this book.

Sampling, Quantization, and Operations Image resolution Spatial resolution : the more pixels in a fixed range, the higher the resolution Gray-level resolution : the more bits, the higher the resolution Sampling is the principal factor determining the spatial resolution. Basically, the spatial resolution is the smallest discernible detail in an image. Gray level resolution similarly refers to the smallest discernible change in gray level, but measuring discernible changes in gray level is a highly subjective process. When an actual measure of physical resolution is not necessary, we often refer to an L-level digital image of size MXN as having a spatial resolution of M x N pixels and a gray-level resolution of L levels.

Sampling, Quantization, and Operations Image zooming and shrinking Both applied to digital image Zooming Creation of new pixel locations Assignment of gray levels to those new locations Pixel replication, when increasing the size of an image an integer times Nearest neighbor interpolation Bilinear interpolation Bicubic interpolation Shrinking

Sampling, Quantization, and Operations Bilinear Interpolation

Sampling, Quantization, and Operations Relationships between pixels Neighbors of a pixel 4-neighbors diagonal-neighbors 8-neighbors Adjacency 4-adjacency 8-adjacency m-adjacency (i-1,j-1) (i-1,j) (i-1,j+1) (i,j-1) (i,j) (i,j+1) (i+1,j-1) (i+1,j) (i+1,j+1)

Sampling, Quantization, and Operations Relationships between pixels m-adjacency

Sampling, Quantization, and Operations Relationships between pixels Path: 4, 8, and m-paths A sequence of distinct pixels from pixel p to q. Connectivity Connect set: only has one connected component. Region Region is a connected set. Boundary The set of pixels in the region which has one or more neighbors that are not in the region. Let S represent a subset of pixels in an image. Two pixels are said to be connected if in S there exists a path between them consisting entirely of pixels in S. Connected component: for any pixel p in S, the set of pixels that are connected to p in S. Connected set: S has only one connected component.

Sampling, Quantization, and Operations Arithmetic operations Addition Subtraction Multiplication Division

Sampling, Quantization, and Operations Logical operations AND OR Complement (NOT) XOR