Image Processing


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image processing basics are discussed.

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Image Processing

  1. 1. Image Processing by S.Steena Vaiz
  2. 2. Introduction <ul><li>Image processing is any form of signal processing for which the input is an image, such as photographs or frames of video; the output of image processing can be either an image or a set of characteristics or parameters related to the image. </li></ul><ul><li>Most image-processing techniques involve treating the image as a two-dimensional signal and applying standard signal-processing techniques to it. </li></ul>
  3. 3. Types <ul><li>Image processing usually refers to digital image processing , but optical and analog image processing are also possible. </li></ul>
  4. 4. Image Processing Operations <ul><li>Geometric transformations such as enlargement, reduction, and rotation </li></ul><ul><li>Color corrections such as brightness and contrast adjustments, quantization , or conversion to a different color space </li></ul><ul><li>Digital compositing or optical compositing (combination of two or more images). Used in filmmaking to make a &quot; matte &quot; </li></ul><ul><li>Interpolation , demosaicing , and recovery of a full image from a raw image format using a Bayer filter pattern </li></ul>
  5. 5. Image Processing Operations(Contd.) <ul><li>Image editing (e.g., to increase the quality of a digital image) </li></ul><ul><li>Image differencing </li></ul><ul><li>Image registration (alignment of two or more images) </li></ul><ul><li>Image stabilization </li></ul><ul><li>Extending dynamic range by combining differently exposed images </li></ul>
  6. 6. Image Segmentation <ul><li>Segmentation refers to the process of partitioning a digital image into multiple regions (sets of pixels ). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. </li></ul><ul><li>Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. </li></ul><ul><li>The result of image segmentation is a set of regions that collectively cover the entire image, or a set of contours extracted from the image. </li></ul><ul><li>Each of the pixels in a region are similar with respect to some characteristic or computed property, such as color, intensity, or texture. Adjacent regions are significantly different with respect to the same characteristic(s). </li></ul>
  7. 7. Image Processing Applications <ul><li>Computer vision </li></ul><ul><li>Face recognition </li></ul><ul><li>Feature detection </li></ul><ul><li>Non-photorealistic rendering </li></ul><ul><li>Medical image processing </li></ul><ul><li>Microscope image processing </li></ul><ul><li>Morphological image processing </li></ul><ul><li>Remote sensing </li></ul>
  8. 8. Face Recognition <ul><li>A facial recognition system is an image processing application for automatically identifying or verifying a person from a digital image or a video frame from a video source. One of the ways to do this is by comparing selected facial features from the image and a facial database. </li></ul>
  9. 9. <ul><li>Face Recognition Used in : </li></ul><ul><ul><li>Human and computer interface </li></ul></ul><ul><ul><li>Biometric identification </li></ul></ul><ul><li>Principal Component Analysis (PCA) : </li></ul><ul><ul><li>Widely adopted </li></ul></ul><ul><ul><li>Most promising face recognition algorithm </li></ul></ul><ul><li>Solution : Applying PCA on wavelet subband </li></ul><ul><li>Subbands obtained using wavelet decomposition . </li></ul><ul><li>PCA applied on the generated subband face </li></ul>
  10. 10. <ul><li>Objective of Face Recognition : </li></ul><ul><ul><li>To determine the identity of a person from a given face image. </li></ul></ul><ul><li>Complications occur due to variations in : </li></ul><ul><ul><li>Illumination </li></ul></ul><ul><ul><li>Pose </li></ul></ul><ul><ul><li>facial expression </li></ul></ul><ul><ul><li>Aging </li></ul></ul><ul><ul><li>occlusions such as spectacles, hair, etc. </li></ul></ul>
  11. 11. <ul><li>In the proposed method we proceed as follows : </li></ul><ul><ul><li>Decompose face image into subbands using Discrete Wavelet Transform (DWT) </li></ul></ul><ul><ul><li>Select mid-frequency subband (Diagonal) from third level. </li></ul></ul><ul><ul><li>Compute representational bases (apply PCA) for reference images </li></ul></ul><ul><ul><li>Store as training image representations </li></ul></ul><ul><ul><li>Translate probe image into probe image representation using representational bases </li></ul></ul><ul><ul><li>Use classifier to match with reference images to identify face image </li></ul></ul>
  12. 12. Discrete Wavelet Transform <ul><li>A face image of a person contains common (approximation) as well as discriminatory (detail) information. </li></ul><ul><li>Discriminatory information is due to structural variations of the face. </li></ul><ul><li>The similarity information and discriminatory information are segregated in different subbands at different levels of decomposition of the face image. </li></ul><ul><li>Wavelet decomposition splits the facial features into : </li></ul><ul><ul><li>Approximations , containing common (smooth) parts of the face </li></ul></ul><ul><ul><li>Details , containing the discriminatory (variations) information. </li></ul></ul>
  13. 13. DWT (Contd.) <ul><li>The original image is decomposed into four subbands - Approximation ( A ), Horizontal ( H ), Vertical ( V ) and Diagonal ( D ) details. </li></ul><ul><ul><ul><li>where D = {H, V,D} such that A1= A2+D2 = A3+D3+D2. </li></ul></ul></ul>
  14. 14. Subband Creation & Selection
  15. 15. Principal Component Analysis (PCA) <ul><li>To recognize a face we need to measure the difference between the new image and the original images </li></ul><ul><li>But the face contains an awful lot of data </li></ul><ul><li>PCA is used to find a low dimensional representation of data </li></ul><ul><li>By means of PCA, one can transform each original image of the training set into a corresponding eigenface </li></ul>
  16. 16. Eigenface <ul><li>Eigenface is the eigenvector obtained from PCA </li></ul><ul><li>Each eigenface represents only certain features of the face </li></ul><ul><li>In essence, eigenfaces are nothing but the characteristic features of a face </li></ul><ul><li>Similar faces (images) possess similar features (eigenfaces) </li></ul><ul><li>So, all images having similar eigenfaces are likely to be similar faces </li></ul>
  17. 17. Face & their eigenfaces
  18. 18. Classification <ul><li>An important part of image analysis is identifying groups of pixels having similar spectral characteristics and to determine the various features </li></ul><ul><li>This form of analysis is known as classification </li></ul><ul><li>Classification employs two phases of processing: </li></ul><ul><ul><li>Training – Create unique description based on characteristic properties of image (face) </li></ul></ul><ul><ul><li>Testing – Match the description and classify the image (face) </li></ul></ul>
  19. 19. Face Recognition Process
  20. 20. The training & recognition processes Training Process Recognition Process
  21. 21. Training Stage <ul><li>Steps involved : </li></ul><ul><ul><li>Apply 3-level Daubechies Wavelet Transform on reference images </li></ul></ul><ul><ul><li>Choose subband 4 from level 3 </li></ul></ul><ul><ul><li>Apply PCA on subband 4 & get eigenvectors and eigenvalues </li></ul></ul><ul><ul><li>By arranging eigenvalues in a descending order, eigenvectors with largest eigenvalues are used as representational bases </li></ul></ul>
  22. 22. Recognition Stage <ul><li>Steps Involved: </li></ul><ul><ul><li>Apply 3-level Daubechies Wavelet Transform on the test images </li></ul></ul><ul><ul><li>Apply PCA on subband 4 & get the eigenvectors and eigenvalues </li></ul></ul><ul><ul><li>Use k-NN classifier to classify the test images into appropriate classes based on the training set </li></ul></ul>
  23. 23. Conclusion <ul><li>Hence the nearest and similar neighbour is matched and the input face image is recognised using the Image Processing technique. </li></ul>
  24. 24. THANK YOU !!!
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