Digital Image Processing COSC 6380/ PDF Free Download

Digital Image Processing COSC 6380/4393 Lecture 21 Nov 1 st, 2018 Pranav Mantini Acknowledgment: Slides from Pourreza

Projects Project team and topic assigned Project proposal presentations : Nov 6 th (Tue) and 8 th (Thu)

Project proposal Schedule Proposals presentation 5 Pts Team No. Team Name Topic Proposal A1 Internationals United image restoration Nov 6th A2 MMFSN Geometric Transformation Nov 6th A3 Nearest Neighbors Intensity Transformation Nov 6th A4 KANE Spatial Filtering Nov 6th A5 Fans of Lenna! Frequency Filtering Nov 6th A6 Team A6 Restoration Nov 8th A7 Team DIP Morphology Nov 8th A8 PyCharmers Color Image Processing Nov 8th A9 Team A9 Morphology Nov 8th A10 IDK Spatial Filtering Nov 8th

Project proposal Schedule Proposals presentation 5 Pts Team No. Team Name Topic Proposal B1 First Row Restoration Nov 6th B2 404-Image not Found Geometric Transformation Nov 6th B3 Image-ine Dragons Intensity Transformation Nov 6th B4 TeamPixels Spatial Filtering Nov 6th B5 Team B5 Frequency Filtering Nov 6th B6 Crazy Thinkers Morphology Nov 8th B7 Team B7 Color Image Processing Nov 8th B8 Team B8 Restoration Nov 8th B9 Black Panther Spatial Filtering Nov 8th

Project Proposal Presentations Nov 6 th (Tue) and 8 th (Thu) Schedule will be on class website (Nov 1 st ) Time: Presentation: 5-7 Mins Q and A: 3 Mins Total: ~10 Mins

Project Proposal Presentations 1. Topic and objectives 1. What is your topic? 2. What sub topics do you plan to implement? 3. What are your objectives? 2. Hand Sketches of GUI 1. Sketches of how you want the GUI to look (Hand draws, Photoshop) 3. Member responsibility 1. Who is responsible for what? 4. Implementation and reuse 1. What functionality do you plan to implement? 2. What inbuilt functions do you plan to use? 3. What inbuilt functions you do not plan to use?

Today Review HIS Color space Color Image Processing Psuedo Color Image Processing Full Color Image Processing

What is color? Color is a psychological property of our visual experiences when we look at objects and lights, not a physical property of those objects or lights (S. Palmer, Vision Science: Photons to Phenomenology) Color is the result of interaction between physical light in the environment and our visual system Wassily Kandinsky (1866-1944), Murnau Street with Women, 1908

Principal Descriptor Visual Descriptor SHAPE COLOR TEXTURE MOTION 11/1/2018 9

Discerning Color ~1000 ~24

Color Fundamentals Cones are the sensors in the eye that are responsible for color vision 6 to 7 million cones in the human eye 11/1/2018 11

Primary colors Due to the absorption characteristics of human eye, Primary colors: Red Green Blue Color: described as a variable combination of the primary colors In 1931, CIE(International Commission on Illumination) defines specific wavelength values to the primary colors B = 435.8 nm, G = 546.1 nm, R = 700 nm However, we know that no single color may be called red, green, or blue

slide from T. Darrel

CIE RGB Tri-stimulus values: Color defined by three value (R,G,B) The amount of Red, Green and Blue needed to form any particular color

CIE XYZ New color matching functions were to be everywhere greater than or equal to zero. For the constant energy white point, it was required that x = y = z = 1/3.

CIE XYZ model RGB -> CIE XYZ model Normalized tristimulus values Z Y X X x Z Y X Y y Z Y X Z z B G R Z Y X 0.939 0.130 0.020 0.071 0.707 0.222 0.178 0.342 0.431 => x+y+z=1. Thus, x, y (chromaticity coordinate) is enough to describe all colors

CIE Chromaticity Diagram It shows color composition as a function of x (red) and y (green) Identify color, Color mixing 11/1/2018 17

Color models Color model, color space, color system Specify colors in a standard way A coordinate system that each color is represented by a single point RGB model CYM model CYMK model HSI model Suitable for hardware or applications - match the human description

RGB Color Model 11/1/2018 19

RGB Color Model Pixel depth The total number of colors in a 24-bit RGB image is (2 8 ) 3 = 16,777,216 11/1/2018 20

CMY model (+Black = CMYK) CMY: secondary colors of light, or primary colors of pigments Used to generate hardcopy output B G R Y M C 1 1 1

HSI color model Will you describe a color using its R, G, B components? Human describe a color by its hue, saturation, and brightness Hue: color attribute Saturation: purity of color (white->0, primary color->1) Brightness: achromatic notion of intensity

HIS Color Model brightness: the achromatic notion of intensity. hue: dominant wavelength in a mixture of light waves, represents dominant color as perceived by an observer. saturation: relative purity or the amount of white light mixed with its hue. 11/1/2018 23

HIS Color Model 11/1/2018 24

HIS Color Model 11/1/2018 25

HIS Color Model 11/1/2018 26

Converting Colors from RGB to HSI Given an image in RGB color format, the H component of each RGB pixel is obtained using the equation H if B G 360 if B>G 1 cos 2 1/2 1 ( R G ) ( R B ) 2 R G ( R B)( G B) 11/1/2018 27

Converting Colors from RGB to HSI Given an image in RGB color format, the saturation component is given by S 3 1 min( R, G, B) ( R G B) 11/1/2018 28

Converting Colors from RGB to HSI Given an image in RGB color format, the intensity component is given by 1 I R G B 3 11/1/2018 29

Converting Colors from HSI to RGB RG sector (0 H 120 ) B I(1 S) Scos H R I 1 cos(60 H ) and G 3 I ( R B) 11/1/2018 30

Converting Colors from HSI to RGB RG sector (120 H 240 ) H H 120 R I(1 S) Scos H G I 1 cos(60 H ) and B 3 I ( R G) 11/1/2018 31

Converting Colors from HSI to RGB RG sector (240 H 360 ) H H 240 G I(1 S) Scos H B I 1 cos(60 H ) and R 3 I ( G B) 11/1/2018 32

11/1/2018 34

11/1/2018 35

Pseudocolor Image Processing Pseudocolor (also called false color) image processing consists of assigning colors to gray values based on a specified criterion. The principal use of pseudocolor is for human visualization and interpretation of gray-scale events in an image or sequence of images. 1. Intensity Slicing 2. Gray Level to Color Transformations

Intensity Slicing

Intensity Slicing (con t)

Gray Level to Color Transformations

Gray Level to Color Transformations Slide: nptelhrd

Gray Level to Color Transformations H.R. Pourreza

Gray Level to Color Transformations

Basic of Full-Color Image Processing Major categories of full-color Image processing: Per-color-component processing Vector-based processing

Basic of Full Color Image Processing

B G R c c c c B G R Let c represent an arbitrary vector in RGB color space ), ( ), ( ), ( ), ( ), ( ), ( ), ( y x B y x G y x R y x c y x c y x c y x c B G R For an image of size M*N, Basic of Full Color Image Processing

Basic of Full-Color Image Processing Color Transformation Processing the components of a color image within the context of a single color model. g( x, y) T f ( x, y) r, r r 2,,, i 1,2 n si Ti 1 n,..., Color components of g Color components of f Color mapping functions

Full-Color Image Processing Color Transformation CMYK RGB HSI

Full-Color Image Processing Color Transformation: Modify the Intensity g( x, y) kf ( x, y) s i kr i i 1,2,3 s i kr i ( 1 k) i 1,2,3 s s s 1 2 3 r 1 r 2 kr 3

Full-Color Image Processing Color Transformation: Color Complement

Full-Color Image Processing Color Transformation: Color Slicing Motive: Highlighting a specific range of colors in an image Basic Idea: Display the color of interest so that they stand out from background Use the region defined by the colors as a mask for further processing s i 0.5 ri if rj a j otherwise W 2 any1 jn, i 1,2,..., n

Full-Color Image Processing Color Transformation: Color Slicing 1. Colors of interest are enclosed by cube (or hypercube for n>3) s i 0.5 r i if r a otherwise j j W 2 any1 jn, i 1,2,..., n

Full-Color Image Processing Color Transformation: Color Slicing 1. Colors of interest are enclosed by cube (or hypercube for n>3) s i 0.5 r i if r a otherwise j j W 2 any1 jn, i 1,2,..., n 2. Colors of interest are enclosed by Sphere s i 0.5 r i if n j1 ( r otherwise j a j ) 2 R 2 0, i 1,2,..., n

Full-Color Image Processing Color Transformation: Color Slicing Cube Sphere

Full-Color Image Processing Color Image Smoothing Averaging: xy xy xy xy S y x S y x S y x S y x y x B K y x G K y x R K y x y x K y x ), ( ), ( ), ( ), ( ), ( 1 ), ( 1 ), ( 1 ), ( ), ( 1 ), (

Full-Color Image Processing Color Image Smoothing Red Green Blue

Full-Color Image Processing Color Image Smoothing Hue Saturation Intensity

Full-Color Image Processing Color Image Smoothing Averaging R,G and B Averaging Intensity Difference

Full-Color Image Processing Color Image Sharpening The Laplacian of Vector c : ), ( ), ( ), ( ), ( 2 2 2 2 y x B y x G y x R y x

Full-Color Image Processing Color Image Sharpening Sharpening R,G and B Sharpening Intensity Difference

Summary 1. Color Fundamentals 2. Color Spaces 3. Color Image Processing