Three Membranes Cornea and Sclera outer cover, Cornea: tough, transparent tissue of the surface of eye Sclera: an Opaque membrane that encloses the optic globe The choroid: it lies below sclera with a network of blood vessels as a major source of nutrition to eye Ciliary body: The iris (pupil) – diameter is approximately 2 to 8mm The retina: Wall’s entire posterior portion Light from an object outside the eye is imaged on the retina Lens attached to ciliary body 60% to 70% water, 6% fat and more protein than any other tissue in the eye Colored by slightly yellow pigmentation that increases with age Excessive clouding of lens, caused by the affliction commonly referred to as cataracts (poor color discrimination and loss of clear vision) Absorbs 8% of visible light spectrum with relatively higher absorption at shorter wavelengths infrared and ultraviolet light are appreciably by proteins within lens structure (excess amount can damage eye)

Receptors Classes: Cones and rods Cones in each eye number between 6 to 7 million. Located in fovea and highly sensitive to color Cone vision is called photopic or bright-light vision The absence of receptors in this area results in the so-called blind spot (Fig. 2.1 Previous slide)

Image Formation in Eye Image height=15m, distance=100m Image height on retina: h= (15 X 17)/100=2.5mm

Brightness Adaptation and Discrimination -Digital images are displayed as discrete set of intensities, the eye’s ability to discriminate between different intensity levels -Subjective brightness is a logarithmic function of the light intensity on eye (Fig. 2.4) The long solid curve represents the range of intensities to which the visual system can adapt. In photopic vision alone, the range is about 106 The transition from scotopic to photopic vision is gradual over the approximate range from 0.001 to 0.1 miliambert (-3 to -1 mL in long scale) Brightness Adaption: large variation by changing its overall sensitivity Ba: Brightness on a short intersecting curve represent the subjective brightness (an eye can perceive adapted to this level) Bb: Range is restricted having this level Higher intensities would simply raise the adaptation level higher than Ba.

I: Intensity, ∆I: field addition to an increment of illumination Weber ratio: ∆Ic/I where, ∆Ic is the increment of illumination of discriminable 50%

A small Weber ratio indicates ”good” brightness where a small percentage change in illumination is discriminable. On the other hand, a large Weber ratio represents ”poor” brightness indicating that a large percentage change in intensity is needed. Rods at work Cones at work The curve shows that brightness discrimination is poor (large Weber ratio) at low level of illumination, and it improves significantly (Weber ratio decreases) as background Rods at work (Weber ratio decreases) as background illumination increases The two branches illustrate the fact that at low levels of illumination, vision is carried out by the rods, whereas at high levels (showing better discrimination), cones are at work.

Perceived brightness is NOT a simple function Example 1: Mach bands The reflected light intensity from each strip is uniform over its width and differs from its neighbors by a constant amount; nevertheless, the virtual appearance is that transitions at each bar appear brighter on the left side and darker on the right side. The Mach band* effect can be used to estimate the impulse response of the visual system * Mach Band is a perceptual phenomenon. When the human eye looks at two bands of colours, one light and one dark, side-by-side, a the eye perceives a narrow strip of gradient light to dark light, in the middle separating the two solid bands. However, this is not the actual image.

Example 2: Simultaneous Contrast Each small square is actually the same intensity, but because of different intensities of the surrounding, the small squares do not appear equally bright. Example 3: Metameric Pairs Any two objects which appear equally bright, even though, their intensities are different are called metameric pairs.

Definition: Light is an electromagnetic radiation which, by simulation, arouses a sensation on the visual receptors making sight possible. Sir Isaac Newton (1666) discovered that when a beam of sunlight is passed through a glass prism, the emerging beam of light is not white but consists instead of a continuous spectrum of colors ranging from violet to red. This is called the visible region of the spectrum, see next figure.

high energies high frequencies short wavelengths

Electromagnetic waves can be visualized as propagating sinusoidal waves of varying wavelengths (λ) or as a stream of massless particles, each traveling in a wavelike pattern and moving at the speed of light. Each massless particle contains a certain amount (or bundle) of energy. Each bundle of energy is called a photon. λ is measured in meters (or km for radio waves), microns (visible) or nanometers (for X-ray).

How to transform illumination energy into digital images? A single imaging sensor, eg. a photodiode voltage output is then digitized to produce a digital image A line sensor An array sensor

Generating a 2-D image using a single sensor This type of mechanical digitizers is called a microdensitometer and is used in high-precision scanning (but slow).

most flat bed scanner use linear strips circular sensor strips are used, e.g. in medical and industrial imaging to produce cross-sectional ”slice” images of 3-D objects.

Principles of Image Acquisition, Sampling and Quantization

Quantization: Digitizing the amplitude values Sampling: Digitizing the coordinate values

Digital Image Representation

Examples

The number of bits required to store an image is b=MxNxk and when M=N, b becomes N2k. Example: 8-bit images of size 1024 by 1024 and higher require a significant storage space! How do these parameters (N and k) affect the image?

The outline of a figure or body; the edge or line that defines or bounds a shape or object.

In this 32-level image, note the appearance of very fine ridge like structures in the areas of smooth gray levels, e.g. skull. 64 levels

16 8 4 2 Due to insufficient number of gray levels, this artifact is more visible below and it is called false contouring.

Isopreference [Huang 1965] curves are plotted in the Nk-plane, where each point represents an image having values of N and k equal to the coordinates of that point. Points lying on an isopreference curve correspond to images of equal subjective quality. A study conducted on this effect of gray level and contouring, and the results were shown in the graph in the form of curves, known as Isopreference curves . The phenomena of Isopreference curves shows, that the effect of contouring not only depends on the decreasing of gray level resolution but also on the image detail. According to this graph, we can see that the first image which was of face, was subject to contouring early then all of the other two images. The second image, that was of the cameraman was subject to contouring a bit after the first image when its gray levels are reduced. This is because it has more details then the first image. And the third image was subject to contouring a lot after the first two images i-e: after 4 bpp (bits per pixel). This is because, this image has more details.

m-adjacency: Two pixels p and q with values from V are m-adjacent if, – q is in N 4(P). – q is in N D(p) and the set [N4(p)∩N4(q)] is empty (has no pixels whose values are from V).