1. Chapter 2: Digital Image Fundamentals Spring 2006, 劉震昌

2. Outline Elements of Visual Perception Image sensing and acquisition Image sampling and quantization Images and MATLAB

3. Understanding visual perception Most image processing operations are based on mathematics and probability Why do we need to understand visual perception? Human intuition plays an important role in the choice of processing technique

4. Research fields Low-level processing Mid-level processing High-level processing Image processing Computer vision Early vision Brain processing

5. Image formation in the Eye

6. Structure of the Human eye 角膜 虹膜 網膜 水晶體 Diameter:20mm

7. 2 classes of receptors: cones and rods Distribution of cones and rods 1 cone -> 1 nerve Many rods -> 1 nerve 錐狀體 柱狀體

8. How human eyes sense light? 6~7M cones are the sensors in the eye 3 principal sensing categories in eyes Red light 65%, green light 33%, and blue light 2%

9. Image Sensing and Acquisition

10. Images? Illumination source scene reflection

11. Image sensors Incoming energy is transformed into a voltage by the combination of input electrical power and sensor material (continuous)

12. Single sensor with motion

13. Sensor strips Flat-bed scanner aircraft

14. Sensor arrays CCD arrays in digital camera

15. Image sampling and quantization

16. continuous data digital data Sampling: digitize the coordinate values Quantization: digitize the amplitude values Why? Limited representation power in digital computers discretize

18. Image sampling and quantization (cont.) Sometimes, the sampling and quantization are done mechanically Limitation on the sensing equipment sensor array

19. Sampling rule How to determine the sampling rate? Nyquist sampling theorem If input is a band-limited signal with maximum frequency Ω N The input can be uniquely determined if sampling rate Ω S > 2Ω N Nyquist frequency : Ω N Nyquist rate : Ω S

20. Sampling rule (cont.)

21. Representing digital images

22. Representing digital images (cont.) Matrix form f(0,0) f(0,1) … f(0,N-1) f(1,0) f(0,1) … f(1,N-1) … f(M-1,0) f(M-1,1) … f(M-1,N-1) MxN bits to store the image = M x N x k gray level = 2 k

23. Representing digital images (cont.) L = 2 k gray levels, gray scales [0, …,L-1] The dynamic range of an image [min(image) max(image)] If the dynamic range of an image spans a significant portion of the gray scale -> high contrast Otherwise, low dynamic range results in a dull, washed out gray look