1. Image and Video Processing in MATLAB Partly based on slides by Dmitri Roduy

2. Topics Data Types Image Representation Image/Video I/O Matrix access Image Manipulation Application: Bright Cars Detection in Video

3. Image Data Types Relevant data types uint8 – [0 255] – native for natural images uint16 – [0 65,535] – common for medical images Logical – [0 1] – native for masks, morph. elements double – 64bit floating point (default range [0 1]) single – when you want to save memory (32bit floating point) Simple casting: double(), uint8(). Type Conversion (of images): im2double(),im2single(),im2uint8(), im2unit16() Works for colors, gray-scale, logical images

4. I = imread(‘pears.png'); I2 = I-1.4; diffI = I-I2; fprintf('Max difference between images: %d\n',max(diffI(:))); figure(1); subplot(1,2,1); imshow(I); title('I'); subplot(1,2,2); imshow(I2); title('I2'); I = imread(‘pears.png'); I2 = I-1.4; diffI = I-I2; fprintf('Max difference between images: %d\n',max(diffI(:))); figure(1); subplot(1,2,1); imshow(I); title('I'); subplot(1,2,2); imshow(I2); title('I2'); Common problem

5. I = double(imread('pears.png')); I2 = I-1.4; diffI = I-I2; fprintf('Max difference between images: %2.1f\n',max(diffI(:))); figure(1); subplot(1,2,1); imshow(I); title('I'); subplot(1,2,2); imshow(I2); title('I2'); I = double(imread('pears.png')); I2 = I-1.4; diffI = I-I2; fprintf('Max difference between images: %2.1f\n',max(diffI(:))); figure(1); subplot(1,2,1); imshow(I); title('I'); subplot(1,2,2); imshow(I2); title('I2'); Common problem

6. I = double(imread('pears.png')); I2 = I-1.4; diffI = I-I2; fprintf('Max difference between images: %2.1f\n',max(diffI(:))); figure(1); subplot(1,2,1); max_I = 255; imshow(I/max_I); title('I'); subplot(1,2,2); imshow(I2/max_I); title('I2'); I = double(imread('pears.png')); I2 = I-1.4; diffI = I-I2; fprintf('Max difference between images: %2.1f\n',max(diffI(:))); figure(1); subplot(1,2,1); max_I = 255; imshow(I/max_I); title('I'); subplot(1,2,2); imshow(I2/max_I); title('I2'); Possible Solution

7. Image Representation 2D Matrix Intensity: Each pixel value in the dynamic range [minP, maxP]. Can represent a grayscale image, results of a 2d function etc. Useful commands: imshow (), imagesc(), colormap(). Binary: a.k.a masks. Can represent areas of interest in image, morphological structuring elements and more… Useful commands: bwlabel(),bwmorph(),bwdist(),im2bw(),bwperim().

8. Image Representation 3D Matrix True Color: Three 2D matrices stacked. Each represents a color component. (e.g. RGB) Can represent an RGB color image, Ycbcr image, LAB image, etc. Useful commands: imshow(),rgb2gray(),rgb2ycbcr(), ycbcr2rgb(), rgb2lab().

9. Image/Video I/O Useful Commands imread() – read image imwrite() – write image im2frame() – convert image to movie frame movie2avi() – write avi file aviread() – read avi file mmreader()/VideoReader() – read video (better) VideoWriter() – create video file (2011b+) movie() – show movie implay() – show video interactively

10. Matrix access Useful Commands: sub2ind()– convert subscript (e.g. (r,c,clr)) to index (n). ind2sub() – convert index (n) to subscipt (e.g. (r,c,clr)). meshgrid() – generate X,Y grids.

11. Image Manipulation Useful Functions: imrotate()– Rotate image. imfilter() – Use kernal to convolve/correlation. nlfilter() – Sliding neighborhood operation. blkproc() – Perform function on (semi-)distinct blocks. fspecial() – Create common image filter kernels. imresize() – scale up/down image using defined interpolation. padarray() – Pad image. colfilt() – Column-stack filtering (faster) Many more – see IP toolbox help

12. Application: Bright Cars Detection in Video Objective: Tag with a red square bright colored cars in a color video with moving cars. Stages: 1)Convert each RGB frame to gray scale. 2)Search for regional maximas above a brightness threshold 3)Remove small regions (i.e morphology) 4)Compute centroid of each region 5)Place a red square in centroid location 6)Repeat 1-5 for all video frames

13. Morphological Open Erosion of the image with the Structuring Element followed by Dilation of the image with the Structuring Element.