Real Time Image Processing


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Seminar presented by Soniya Kumari of SOE,CUSAT.

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Real Time Image Processing

  1. 1. REAL TIME IMAGE PROCESSING<br /> From research to reality<br />GUIDE:SHEENA S <br /> By:<br /> SONIYA KUMARI<br />
  3. 3. INTRODUCTION<br /><ul><li>Image Processing
  4. 4. Real Time
  5. 5. Real-time in the perceptual sense
  6. 6. Real-time in the software engineering </li></ul> sense. <br /><ul><li>Real-time in the signal processing sense.</li></li></ul><li>Real Time Image Processing<br /><ul><li>What is Real-time Image Processing?
  7. 7. How it differs from ordinary Image Processing?
  8. 8. What is the need for RTIP?</li></li></ul><li>camera<br />ADC<br />driver<br />RTIP<br />display<br />bus<br />software<br />Real-Time Image Processing<br />System Design<br />Haredware Selection and Software Performance both are crucial.<br />
  9. 9. Real-Time Image Processing<br /><ul><li>Platform Requirements:</li></ul> • high resolution, high frame rate video <br /> input <br /> • low latency video input<br /> • low latency operating system scheduling<br /> • high processing performance<br />
  10. 10. Sampling Resolution<br /><ul><li>What is the need for Sampling Resolution?
  11. 11. Spatial resolution and temporal resolution are both crucial</li></ul>camera<br />ADC<br />driver<br />RTIP<br />display<br />bus<br />
  12. 12. Low Latency Video Input<br />Latency targets<br />perceived synchronicity <br />Unavoidable latency<br />1 to 2 frames(40 - 80ms for PAL)<br />Additional latency must be minimized<br />
  13. 13. Low Latency Operating System Scheduling<br />Processing of video signals depend on <br /> -video capture hardware in use.<br /> -driver component.<br />Software components has crucial impact on system latency.<br />To avoid loss of input data, buffering is introduced to cover lag.<br />Mac OS X has excellent low latency performance.<br />
  14. 14. High Processing Performance<br />Both latency and throughput are important<br />PAL video frame: 884Kb<br />Sustained data rate: 22Mb/s<br />Memory bandwidth is crucial.<br />AltiVec is very useful .<br />
  15. 15. MAC OS X<br />Mac OS X is the world’s most advanced operating system.<br /><ul><li>Features:</li></ul>Power of unix simplicity of MAC.<br />Perfect integration of hardware and software.<br />Elegant interface and stunning graphics.<br />Highly secure by design.<br />Innovation for everyone.<br />Reliable to the core.<br />
  16. 16. Software Operations Involved In RTIP<br /><ul><li>Levels of Image Processing:</li></ul>HIGH -LEVEL<br />INTERMEDIATE -LEVEL<br />LOW-LEVEL<br />
  17. 17. <ul><li>Low-level operations
  18. 18. Intermediate -level operations
  19. 19. High-level operations</li></li></ul><li>Low level Operations<br /><ul><li>Low-level operators take an image as their input and produce an image as their output.
  20. 20. It transform image data to image data i.e it</li></ul> deal directly with image matrix data at the pixel level.<br /><ul><li>Examples:-color transformations, gamma correction, linear or nonlinear filtering, noise reductionetc.
  21. 21. Goal of Low Level Operation.</li></li></ul><li>Intermediate Level Operations<br /><ul><li> It transform image data to a slightly more abstract form of information by extracting certain attributes of image.
  22. 22. Ultimate goal is to reduce the amount of data to form a set of features suitable for further high-level processing.
  23. 23. Examples:-segmentation of image into regions/objects of interest, extracting edges etc.</li></li></ul><li>High Level operations<br /><ul><li>It interpret the abstract data from the intermediate-level, performing high level knowledge-based scene analysis on a reduced amount of data.
  24. 24. They are less data intensive and more inherently sequential rather than parallel.</li></li></ul><li>RTIP Applied On Traffic-Queue Detection Algorithm<br /><ul><li>Why RTIP applied to traffic?</li></ul> -For reducing congestion problem<br /><ul><li>Need for processing of traffic data</li></ul> -Traffic control<br /> -Traffic management<br /> -Road safety <br /> -Development of transport policy.<br /><ul><li>Traffic measurable parameters</li></ul> -Traffic volumes & Speed <br /> -Inter-vehicle gaps & Vehicle classification<br />
  25. 25. Image analysis system structure: -<br />backing<br />store<br /> data bus<br />RAM<br />64kbytes<br />CCTV<br />camera<br />ADC<br />16-Bit mini-computers<br />DAC<br />Printer<br />Monitor<br />
  26. 26. Stages of image analysis:-<br /><ul><li>Image sensors used
  27. 27. ADC Conversion
  28. 28. Pre-processing</li></ul>To cope with this, two methods are proposed:<br /> 1. Analyze data in real time – uneconomical<br /> 2. Stores all data and analyses off-line at low speed <br />
  29. 29. Two jobs to be done:<br />Green light on: - determine no. of vehicles moving along particular lanes and their classification by shape and size.<br />Red light on: - determine the backup length along with the possibility to track its dynamics and classify vehicles in backup.<br />
  30. 30. QUEUE DETECTION ALGORITHM<br /><ul><li>Spatial-domain technique is used to detect queue – implemented in real-time using low-cost system.
  31. 31. For this purpose two different algorithms have been used:-
  32. 32. Motion detection operation
  33. 33. Vehicle detection operation</li></li></ul><li>QUEUEDETECTION<br />EDGE DETECTION<br />
  34. 34. APPLICATIONS<br /><ul><li>video conferencing
  35. 35. augmented reality
  36. 36. context aware computing
  37. 37. video-based interfaces for human-computer interaction</li></li></ul><li>VIDEO CONFERENCING<br />It is digital compression of audio and video streams in real time.<br />Video input : video camera or webcam. <br />Video output: computer monitor television or projector<br />
  38. 38. AUGMENTED REALITY<br />A combination of a real scene viewed by a user and a virtual scene generated by a computer that augments the scene with additional information.<br />
  39. 39. CONTEXT AWARE COMPUTING<br />A system is context-aware if it uses context to provide relevant information and/or services to the user, where <br /> relevancy depends on<br /> the user’s task.<br />
  40. 40. CONCLUSION<br />RTIP involves many aspects of hardware and software in order to achieve high resolution input,low latency capture,high performance processing and efficient display.The measure- mentalgorithm has been applied to traffic scenes with different lighting conditions. And RTIP be at the heart of many applications.<br />
  41. 41. THANK YOU<br /> ?<br />