2. Human and Computer Vision The best vision model we have! Knowledge of how images form in the eye can help us with processing digital images We can’t think of image processing without considering the human vision system. We observe and evaluate the images that we process with our visual system. Without taking this elementary fact into consideration, we may be much misled in the interpretation of images.

3. Structure of Human Eye Ciliary muscle Anterior chamber Cornea Iris Ciliary body Lens Ciliary fibers Vitreous humor Visual axis Fovea Blind spot Retina Sclera Choroid Nerve & sheath X-section of human eye

4. The Eye Retina Choroid Sclera Fovea Optic nerve Aqueous humor Cornea Iris Eye lens Vitreous humor

5. Structure of human eye  Receptors المستقبلات - Pattern vision is afforded by the distribution of discrete light receptors over the surface of the retina. Receptors are divided into 2 classes:  Cones مخاريط  Rods قضبان

6. Structure of human eye (contd….)  Cones: 6-7 million, located primarily in the central portion of the retina (the fovea, muscles controlling the eye rotate the eyeball until the image falls on the fovea). Highly sensitive to color. Each is connected to its own nerve end thus human can resolve fine details. Cone vision is called photopic or bright-light vision (day vision).  Rods- 75-150 million, distributed over the retina surface. Several rods are connected to a single nerve end reduce the amount of detail discernible. Serve to give a general, overall picture of the field of view. Sensitive to low levels of illumination. Rod vision is called scotopic or dim-light vision (night vision).

7. Rods Cones Rods Blind spot Number of rods or cones per mm Temporal on retinaNasal Perimetric angle (deg) Corn sweet 2

8. CAN and CANNOT CAN:  See black line 1 second of arc on white field.  Detect motion to 10 seconds of arc 2 minutes of arc per second of time.  Match brightness or color well (within 2%) (or a few millimicrons).  Process information in parallel. CAN’T:  Judge absolute level or brightness accurately.  Determine absolute wavelength of color well.  Detect motion faster than 200 per second.  See Beyond 0.4 to 0.7 microns.

9. Image Formation in the Eye Example:  Calculation of retinal image of an object

10. Test Images

11. Test images for distances and area estimation: a) Parallel lines with up to 5% difference in length. b) Circles with up to 10% difference in radius. c) The vertical line appears longer but actually has the same length as the horizontal line. d) Deception by perspective: the upper line appears longer than the lower one but actually have the same length.

12. Are the purple lines straight or bent? Do you see gray areas in between the squares? Now where did they come from?

13. Simultaneous Contrast All the small squares have exactly the same intensity, but they appear to the eye progressively darker as the background becomes brighter. Region’s perceived brightness does not depend simply on its intensity. Which small square is the darkest one ? An example of simultaneous contrast

14. Color Perception Color Representation for images and video  How the physical spectra of a scene is transformed into RGB components, and how these components are transformed to physical spectra at the display Cones vs. Rods 3 types of cones (for color) 1 type of rod (night vision, no color)

15. Light is a part of EM wave Perceived color depends on spectral content (wavelength composition) e.g., 700nm ~ red. “spectral color” A light with very narrow bandwidth A light with equal energy in all visible bands appears white.

16. Illuminating and Reflecting Light Illuminating sources: المنيره  emit light (e.g. the sun, light bulb, TV monitors)  perceived color depends on the emitted freq.  follows additive rule » R+G+B=White Reflecting sources: العاكسه  reflect an incoming light (e.g. the color dye, matte surface, cloth)  perceived color depends on reflected freq (=emitted freq -absorbed freq.)  follows subtractive rule » R+G+B=Black

17. Reflected Light The colours that we perceive are determined by the nature of the light reflected from an object For example, if white light is shone onto a green object most wavelengths are absorbed, while green light is reflected from the object White Light Colours Absorbed Green Light

18. Frequency Responses of Cones and the Luminous Efficiency Function Absorption spectra C i ( ) has peaks around 450nm (blue), 550nm (green), 620nm (yellow-green) [Jain’s Fig.3.11 (pp61)] Color sensation as described by spectral response α i ( ). blue green red luminance

19. Color Mixing Primary colors for illuminating sources: Red, Green, Blue (RGB) Color monitor works by exciting red, green, blue phosphors using separate electronic guns Primary colors for reflecting sources (also known as secondary colors): Cyan, Magenta, Yellow (CMY) Color printer works by using cyan, magenta, yellow and black (CMYK) dyes.

20. Color complements Complements on the color circles Color hue specification

22. Color Gamut of printing devices Color Gamut of RGB Monitors

23. Computer Imaging Can be defined a acquisition and processing of visual information by computer. Computer representation of an image requires the equivalent of many thousands of words of data, so the massive amount of data required for image is a primary reason for the development of many sub areas with field of computer imaging, such as image compression and segmentation.Another important aspect of computer imaging involves the ultimate “receiver” of visual information in some cases the human visual system and in others the computer itself. Computer imaging can be separate into two primary categories: 1. Computer Vision. 2. Image Processing.

24. Computer Vision Computer vision computer imaging where the application doses not involve a human being in visual loop. One of the major topics within this field of computer vision is image analysis. Image Processing Image processing is computer imaging where application involves a human being in the visual loop. In other words the image are to be examined and a acted upon by people. The major topics within the field of image processing include: 1. Image restoration.استعادة 2. Image enhancement.يعززاويجمل 3. Image compression.

25. Image Restoration Is the process of taking an image with some known, or estimated degradation, and restoring it to its original appearance. Image restoration is often used in the field of photography or publishing where an image was somehow degraded but needs to be improved before it can be printed

26. Involves taking an image and improving it visually, typically by taking advantages of human Visual Systems responses. One of the simplest enhancement techniques is to simply stretch the contrast of an image. Enhancement methods tend to be problem specific. For example, a method that is used to enhance satellite images may not suitable for enhancing medical images. Although enhancement and restoration are similar in aim, to make an image look better. They differ in how they approach the problem. Restoration method attempt to model the distortion to the image and reverse the degradation, where enhancement methods use knowledge of the human visual systems responses to improve an image visually. Image Enhancement

28. Involves reducing the typically massive amount of data needed to represent an image. This done by eliminating data that are visually unnecessary and by taking advantage of the redundancy that is inherent in most images. Image processing systems are used in many and various types of environments, such as: 1. Medical community 2. Computer – Aided Design 3. Virtual Reality 4. Image Processing. Image Compression