1. IP Introduction.pdf
- 1. OE5 CS8009 Image Processing Pritee Khanna [email protected] “One picture is worth more than ten thousand words” Anonymous January 2, 5, 2023
- 2. Overview Early days of computing, data was numerical Later, textual data became more common Today, many other forms of data: voice, music, speech, images, computer graphics, etc. Each of these types of data are signals Loosely defined, a signal is a function that conveys information
- 3. Signal Processing As long as people have tried to send or receive through electronic media : telegraphs, telephones, television, radar, etc. there has been the realization that these signals may be affected by the system used to acquire, transmit, or process them Sometimes, these systems are imperfect and introduce noise, distortion, or other artefacts Understanding the effects these systems have and finding ways to correct them is the fundamental of signal processing
- 4. Signal Processing and other fields Signals may be specific messages e.g., telegraph, telephone, television, digital networking, etc. We specifically introduce the information content into the signal and hope to extract it later Man-made signals may be encoding of natural phenomena Audio signal, acquired image, etc. Signals may be created from scratch Speech generation, computer-generated music, computer graphics, etc. Finally, we can sometimes merge these technologies together by acquiring a natural signal, processing it, and then transmitting it in some fashion
- 5. From acquisition to interpretation, the initial signal may be transformed, modified, and retransmitted numerous times Displayed to create another signal (visible light of the display) Recipient: Decoded Decompressed Transmitted codes of image Received by eyes Interpreted in some fashion by our brain Sender: Enhance the picture Compress for transmission Natural image Digital network Displayed on Computer See a Scenario
- 6. Some Related Fields Digital Communication Computer Graphics Image Processing ComputerVision Pattern Recognition Robotics Artificial Intelligence Speech Synthesis and Recognition
- 7. To summarize… Computer Vision, Image Processing, and Computer Graphics often work together to produce amazing results ……
- 8. What is a Digital Image? (1/4) A digital image is a representation of a two- dimensional image as a finite set of digital values, called picture elements or pixels
- 9. What is a Digital Image? (2/4) Pixel values typically represent gray levels, colors, heights, opacities, etc. Remember digitization implies that a digital image is an approximation of a real scene 1 pixel
- 10. What is a Digital Image? (3/4) Common image formats include: 1 sample per point (B andW or Grayscale) 3 samples per point (Red, Green, and Blue) 4 samples per point (Red, Green, Blue, and “Alpha”)
- 11. What is a Digital Image? (4/4) Digital image processing focuses on two major tasks Improvement of pictorial information for human interpretation Processing of image data for storage, transmission and representation for autonomous machine perception Where image processing ends and fields such as image analysis and computer vision start ???
- 13. Image Enhancement One of the most common uses of DIP techniques: improve quality, remove noise etc
- 14. Image Enhancement Another Example: The Hubble Telescope Launched in 1990, the Hubble telescope can take images of very distant objects However, an incorrect mirror made many of Hubble’s images useless Image processing techniques were used to fix this
- 15. Object Recognition a novel view recognized reference view 1 reference view 2 Another application: Inserting Artificial Objects into a Scene
- 16. Object Recognition Examples (1/2) Geographic Information Systems Digital image processing techniques are used extensively to manipulate satellite imagery Terrain classification Meteorology
- 17. Object Recognition Examples (2/2) Night-Time Lights of the World data set Global inventory of human settlement Not hard to imagine the kind of analysis that might be done using this data Interpretation of aerial photography is a problem domain in both image processing and computer vision
- 18. Character Recognition & Document Handling
- 19. Face Detection, Recognition, and Tracking
- 21. Security Applications Video Surveillance Biometrics
- 22. Fingerprint Verification / Identification
- 23. Medical Image Processing Feature extraction and classification with more than 95% accuracy Mammogram Preprocessed mammogram Segmented image patches Clustered image Preprocessing Segmentation Feature Extraction and classification with 98% accuracy
- 24. Other Interesting Applications Target Recognition: Need of Defense organizations AutonomousVehicles: Land, Underwater, Space Traffic Monitoring Smart Human-Computer User Interfaces
- 25. Industrial Applications (1/3) Human operators are expensive, slow and unreliable Make machines do the job instead Industrial vision systems are used in all kinds of industries Can we trust them?
- 26. Industrial Applications (2/3) Quality assurance Can be used to control all parts of the product are on place (a) all places in pill pack are filled (b) The level of liquid in bottles (c) the quality of plastic details (d) and even control the corn flakes! (e) (a) CD-ROM controller (b) Pack of pills (c) Level of liquid (d) Air-bladders in plastic (e) Corn flakes
- 27. Industrial Applications (3/3) Printed Circuit Board (PCB) inspection Machine inspection is used to determine that all components are present and that all solder joints are acceptable Both conventional imaging and x-ray imaging
- 28. Law Enforcement Image processing techniques are used extensively by law enforcers Number plate recognition for speed cameras/automated toll systems Fingerprint recognition Enhancement of CCTV images
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- 30. Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression
- 31. Key Stages in Digital Image Processing Image Acquisition – If output of the camera or sensor is not already in digital form, an analog-to-digital converter digitizes it – Frame grabber only needs circuits to digitize the electrical signal from the imaging sensor to store the image in the memory of computer Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression
- 32. Key Stages in Digital Image Processing Image Enhancement – To bring out obscured details, or simply to highlight certain features of interest in an image Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression
- 33. Key Stages in Digital Image Processing Image Restoration – Improving the appearance of an image – Tend to be based on mathematical or probabilistic models of image degradation Distorted Image Restored Image Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression
- 34. Key Stages in Digital Image Processing Morphological Processing – Tools for extracting image components that are useful in the representation and description of region shape, such as boundaries, skeletons, and the convex hull Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression
- 35. Image Enhancement Image Restoration To differentiate ….. Squares of various size Erosion Operation Dilation Operation Morphological Processing
- 36. Key Stages in Digital Image Processing Segmentation – To separate objects from the image background (one of the most difficult task in DIP) – Output is raw pixel data, constituting either the boundary or a region or all the points in the region itself Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression
- 37. Key Stages in Digital Image Processing Object Recognition – Recognition: the process that assigns a label to an object based on the information provided by its descriptors – Interpretation: assigning meaning to an ensemble objects Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression
- 38. – Make a decision whether the data should be represented as a boundary or as a complete region Boundary rep: Focus on external shape characteristics (corners, inflections) Region rep: Focus on internal properties, such as texture or skeleton shape Key Stages in Digital Image Processing Representation & Description Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression
- 39. Key Stages in Digital Image Processing Image Compression Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression – Reducing the storage required to save an image or the bandwidth required to transmit it • Ex: JPEG (Joint Photographic Experts Group) image compression standard
- 40. Key Stages in Digital Image Processing Colour Image Processing Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression
- 41. Text and Reference Books R. C. Gonzalez and R. E.Woods, Digital Image Processing,Third Edition, Pearson, 2012. M Sonka,V Hlavac, and R Boyle, Image Processing,Analysis, and MachineVision,Third Edition, Cengage learning, 2008. W. K. Pratt, Digital Image processing,Third Edition, John Wiley & Sons Inc, 2001.
- 42. Grading Scheme Assignments and Quizzes: 15 marks Programming Quiz: 10 Mid-Sem Exam: 20 marks End-Sem Exam: 40 marks Project: 15 marks Teaching Assistant Ms. Sudha Singh [email protected] Kindly see the details on the drive folder shared with you.
- 43. References: Thanks for images and text Digital Image Processing course by Dr. Brian Mac Namee http://www.comp.dit.ie/bmacnamee/ ComputerVision Home Page http://www.cs.cmu.edu/afs/cs/project/cil/ftp/html/vision.html UNR ComputerVision Laboratory http://www.cs.unr.edu/CVL Digital Image Processing course by Dr.Wanasanan Thongsongkrit [email protected] Google Image Search Engine R. C. Gonzalez and R. E.Woods, Digital Image Processing,Third Edition, Pearson, 2012.