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IIS for Image Processing Michael J. Watts mike.watts.nz

IIS for Image Processing Michael J. Watts http://mike.watts.net.nz. Lecture Outline. Image data Graphic formats Image analysis Image transformations Applications. Image Data. What is an image? Data in a visual medium Images on a computer are displayed as pixels

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IIS for Image Processing Michael J. Watts mike.watts.nz

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  1. IIS for Image Processing Michael J. Watts http://mike.watts.net.nz

  2. Lecture Outline Image data Graphic formats Image analysis Image transformations Applications

  3. Image Data What is an image? Data in a visual medium Images on a computer are displayed as pixels each pixel is one colour Resolution

  4. Graphics Formats Raster vs Vector Raster represents each pixel Vector represents shapes Meta files combine both

  5. Raster Images Image is divided into regularly sized cells pixels Each pixel is one colour Number of pixels / unit length = resolution Bits / pixel = number of colours / pixel 1, 2, 4, 8, 16, 24 bit colour How many colours each? File size is a function of resolution and bits / pixel

  6. Image Analysis What are the goals of image analysis? To enhance the image for increased understanding by: Human observers Machine observers To (possibly) obtain some insight into how human beings process visual information

  7. Image Analysis The study of images in some form to elicit information that's implicitly stored within the image Techniques used for image analysis Image filtering Edge enhancement Texture analysis Noise reduction

  8. Image Transformations Many image processing transformations are non-linear Why? Examples Convolution Sobel filters Median filters

  9. Image Transformations Convolution General technique Uses a small matrix Kernel Kernel is slid over the image Moves one pixel at a time Values in the kernel are used to transform values in image Basis of many image processing techniques

  10. Image Transformations Sobel filter Based on a convolution Edge detector Edges have high contrast Measures the gradient between adjacent groups of pixels Uses specific kernel values

  11. Image Transformations Median filters Family of filters Noise reduction Impulse noise Examine groups of pixels Remove spikes in intensity Different methods used

  12. Wavelet Transformations What is a wavelet? Two main properties Limited duration Average value of zero A wavelet fades in and fades out Wavelet analysis breaks a signal into its component wavelets

  13. Wavelet Transformations Wavelet coefficients can be recombined to form the original signal Wavelets have many applications Noise filtering Compression Wavelets are used when FFT don't work

  14. Wavelet Transformations Ability to perform local analysis and flexible decomposition of a signal Can characterise the colour-space and texture of an image Has been used for the indexing of images Can be used for image compression 25% size of equivalent JPEG

  15. Image Recognition The ability for a computer program to identify certain features of an image through: Statistical classifiers Neural networks Rule-based systems

  16. Applications of Image Recognition Face recognition Security Policing Pest identification Mapping visible damage -> insects Counting sheep Methane emissions

  17. Summary Images are a complex data source Many techniques exist for transforming images Image recognition has many applications

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