Week No. 02 Basic concepts of IMAGE (course: Computer Vision)

Transcription

1 Week No. 02 Basic concepts of IMAGE (course: Computer Vision) e- mail: Department of So9ware Engineering, Mehran UET Jamshoro, Sind, Pakistan

2 Outline Image Digital Image Gray Scale, Sampling, QuanSzaSon ResoluSon

3 Image? An Image is a projecson of 3D objects on 2D surface An Image is a 2D light intensity funcson of form f(x,y) Where x & y denotes the spasal co- ordinates and the value of (x,y) is brightness of the image at that point

4 Image? As Image is light Intensity funcson, so 0 < f(x,y) < Light Energy cannot be nega%ve Light Energy cannot be Infinite An image consists of two components, namely IlluminaSon and Reflectance f(x,y) = i(x,y) * r(x,y) 0 < i(x,y) < 0 < r(x,y) < 1

5 Image? IlluminaSon is the amount of light falling on the object, and, this is property of light source. Reflectance is the light reflected back from object and this remains between 0 & 1. Reflectance = 0 (Transparent objects) Reflectance = 1 (Opaque objects)

6 ReflecSon r(x,y) values Typical values of reflecson r(x, y) are: 0.01 for black velvet 0.65 for stainless steel 0.80 for flat-white wall paint 0.90 for silver-plated metal 0.93 for snow

7 Digital Image Source: hep://blogs.mathworks.com/steve/2011/08/26/digital- image- processing- using- matlab- digital- image- representason/ Wednesday, source: August Gonzales, 5, R C., & Woods, R. E. Digital image processing, 1993.

8 Digital Image A digital image is an image f(x,y) that has been discriszed both in spasal & in brightness. A 2D matrix whose rows & columns idensfy a unique point in the image. The corresponding matrix element value idensfies the brightness level at that point. The elements of such a digital array are called image elements, picture elements, pixels or pel.

9 Gray Scale The Intensity value of any Pixel is called as Gray Level Value, and it is denoted by L The value of L lies in a certain range, and this is called as Gray Scale [L min, L max ] is the Gray Scale, such that L min < L < L max For Binary Images, the Gray scale used is [0,1]. For color Images, the Gray scale is [0,255]

10 Gray Scale The interval between the L min and L max is usually taken from 0 to 1 (for Binary Images). We generally have the following convensons: [0, 7 ] 8- levels [0, 15 ] 16- levels [0, 31 ] 32- levels [0, 255] 256- levels The Low value represents BLACK The high value represent WHITE Intermediate Values gives different shades

11 DigiSzaSon A process of conversng Analog Images in to Digital. ConsStutes of Two steps. 1. Sampling 2. QuanSzaSon Sampling: DigiSzaSon of spasal coordinates Quan%za%on: DigiSzaSon of Amplitude Values

12 Sampling DigiSzaSon of spasal coordinates (x, y ) referred to as Image Sampling. How much samples are required to extract the enough informason from Analog Image? Decision is made by using famous Sampling Theorem DigiSzaSon process requires that a decision be made on the number of discrete grey levels allowed for each pixel

13 QuanSzaSon Amplitude DigiSzaSon is called Gray- level QuanSzaSon In Digital Image Processing let these quansses be integer powers of two; that is N = 2 n and G = 2 m Where G denotes number of Gray level and Discrete levels are equally spaced between 0- L

14 Digital Image ApproximaSon Suppose that a consnuous Image f(x,y) is approximated by equally spaced samples to form a N*N array, such that: f(x,y)= f(0,0) f(0,1) f(0,2) f(0,n- 1) f(1,0) f(1,1) f(1,2) f(1,n- 1) f(2,0) f(2,1) f(2,2) f(2,n- 1) f(n- 1,0) f(n- 1,1) f(n- 1,2) f(n- 1,N- 1)

15 DigiSzaSon Analog Image Digital Image Quan%za%on Sampling

16 Image representason A Binary Image stored in computer can be shown as: Memory RepresentaSon Image Displayed for a Digital Image

17 ResoluSon It may be defined as the degree of discrete details of an image which is strongly dependent on both n and m. The more these parameters are increased, the closer the digiszed array will approximate the original image. By reducing the number of samples an image is distorted (less informason is available). By decreasing the number of gray levels we get impercepsble image and is called False Contouring.

18 Storage requirements The storage capacity for a digital Image depends upon: The details available in Image The Gray Scale being used The detail available is represented in terms of the ResoluSon (rows * Cols) The gray scale is represented in terms of encoded bits

19 Storage requirements The formula used for calculasng bits required, is given by: B=M*N*k Where M = Number of Rows N = Number of Columns K = Bits required to encode the used Gray scale For encoding a gray scale of [0,7],that is 8 different gray values, we need 3 bits In case of Square Images (M=N), it becomes: B=N 2 *k

20 Example: Storage Requirements Find bits required to store a 4*4 digital Image,when we are using 64 different gray levels? SoluSon: o ResoluSon=4*4 o Gray scale=[0,63] o Encoded bits =6 (since 2 6 =64) So bits required are: B = M*N*k B = 4*4*6 = 96 bits

21 Storage requirements N/k Table showing Bits required for some typical values of N (N 2 k)

22