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Video and Image Processing At Purdue

Video and Image Processing At Purdue. Edward J. Delp Video and Image Processing Laboratory ( VIPER) School of Electrical and Computer Engineering Purdue University West Lafayette, Indiana, USA email: ace@ecn.purdue.edu http://www.ece.purdue.edu/~ace. Acknowledgements. Students -

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Video and Image Processing At Purdue

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  1. Video and Image Processing At Purdue Edward J. Delp Video and Image Processing Laboratory (VIPER) School of Electrical and Computer Engineering Purdue University West Lafayette, Indiana, USA email: ace@ecn.purdue.edu http://www.ece.purdue.edu/~ace

  2. Acknowledgements • Students - • Eduardo Asbun • Dan Hintz • Paul Salama • Ke Shen • Martha Saenz • Eugene Lin • Ray Wolfgang • Greg Cook • Sheng Liu

  3. Image and Video Processing at Purdue Purdue has a rich history 60 year history in image and video processing.

  4. VIPER Research Projects • Scalable Video and Color Image Compression • still image compression (CEZW) • high and low bit rate video compression (SAMCoW) • wireless video • Error Concealment • Content Addressable Video Databases (ViBE) • Scene Change Detection and Identification • Pseudo-Semantic Scene Labeling • Multimedia Security: Digital Watermarking

  5. VIPER Research Projects • Multicast Video • Analysis of Mammograms • Embedded Image and Video Processing

  6. Other Purdue Projects • Electronic Imaging - Jan Allebach and Charles Bouman • half-tone printing • compound document compression • image databases • Remote Sensing - David Langrebe • Medical Imaging - Charles Bouman, Peter Doerschuk, Thomas Talavage, Edward Delp • computed imaging • functional MRI • x-ray crystallography • breast imaging

  7. Breast Cancer • Second major cause of cancer death among women in the United States (after lung cancer) • Leading cause of nonpreventable cancer death • 1 in 8 women will develop breast cancer in her lifetime • 1 in 30 women will die from breast cancer • Evidence seems to indicate that “curable” tumors must be less than 1 cm in diameter

  8. Mammography • Mammograms are X-ray images of the breast • Screening mammography is currently the best technique for reliable detection of early, non-palpable, potentially curable breast cancer • Studies show that mammogram can reduce the overall mortality from breast cancer by up to 30%

  9. Mammography • In the United States, it is recommended that women over 50 years old receive annual mammograms • this probably too late to start • Usually 4 views are taken (2 of each breast) • most mammograms are taken using X-Ray film (analog) • digital mammogram systems are now being deployed

  10. Screening Mammography

  11. A Digital Mammogram (normal)

  12. Density 1 Density 2 Density 3 Density 4 Analysis of Mammograms

  13. Digital Mammography • Resolution - 50  pixel size • 3000 x 4000 pixels (12,000,000 pixels) • 8-16 bits/pixels • 8 bits/pixel (12 MB) • 16 bits/pixel (24 MB) • Each study consists of 48-96 MB! • 200 patients per day can results to 20GB/day • Problems with storage and retrieval

  14. Three Types of Breast Abnormalities Micro-calcification Circumscribed Lesion Spiculated Lesion

  15. Problems in Screening Mammography • Radiologists vary in their interpretation of the same mammogram • False negative rate is 4 – 20% in current clinical mammography • Only 15 – 34% of women who are sent for a biopsy actually have cancer

  16. Current Research in Computer Aided Diagnosis (CAD) • The goal is to increase diagnostic accuracy as well as the reproducibility of mammographic interpretation • Most work aims at detecting one of the three abnormal structures • Some have explored classifying breast lesions as benign or malignant • The implementation of CAD systems in everyday clinical applications will change the practice of radiology

  17. Multiresolution Detection of Spiculated Lesions in Digital Mammograms • Spiculation or a stellate appearance in mammograms indicates with near certainty the presence of breast cancer • Detection of spiculated lesions is very important in the characterization of breast cancer

  18. Block Diagram of Multiresolution Detection of Spiculated Lesions

  19. Detection Results A 12.4mm lesion detected at the second coarsest resolution Automatic Detection Ground Truth

  20. Detection Results A 6.6mm lesion detected at the finest resolution Automatic Detection Ground Truth

  21. Research Team Charles Babbs - Department of Basic Medical Sciences Zygmunt Pizlo - Department of Psychological Sciences Sheng Lui - School of Electrical and Computer Engineering Valerie Jackson - IU Department of Radiology Funding - NSF, NIH, and Purdue Cancer Center http://www.ece.purdue.edu/~ace/mammo/mammo.html

  22. ViBE: A New Paradigm for Video Database Browsing and Search • ViBE has four components • scene change detection and identification • hierarchical shot representation • pseudo-semantic shot labeling • active browsing based on relevance feedback • ViBE provides an extensible framework that will scale as the video data grows in size and applications increase in complexity

  23. Video Analysis: Overview Closed-caption information Proc. Audio data Proc. Shot Transition Detection and Identification Compressed video sequence Image data (DC frames) Data Extraction Proc. MPEG-related data (MVs, AC coeffs, etc.) Shot attributes Transition locations and types Shot Labeling Shot trees Intrashot Clustering Proc. Captions

  24. Navigation via the Similarity Pyramid Zoom in Zoom out Zoom in Zoom out

  25. Browser Interface Control Panel Similarity Pyramid Relevance Set

  26. Video Over IP: Unicast

  27. Video Over IP: Multicast

  28. Video Over IP • Currently multicasting 3 streams • Multicast experiments with Europe • Multicast HDTV over Internet2 • Issues: • what is the backward information? • which video compression technique? • how is network control connected to the server/encoder?

  29. Why is Digital Watermarking Important? • Scenario • an owner places digital images on a network server and wants to detect the redistribution of altered versions • Goals • verify the owner of a digital image • detect forgeries of an original image • identify illegal copies of the image • prevent unauthorized distribution

  30. Why is Watermarking Important?

  31. Why is Watermarking Important?

  32. Why Watermarking is Important?

  33. Why is Watermarking Important?

  34. VW2D Watermarked Image

  35. Image Adaptive Watermarks (DCT)

  36. Scalable Image and Video Compression • Problem: desire to have a compression technique that allows decompression to be linked to the application • databases, wireless transmission, Internet imaging • will support both high and low data rate modes • Other desired properties: • error concealment • will support the protection of intellectual property rights (watermarking)

  37. Rate Scalable Image and Video Coding • Applications • Internet streaming • Image and video database search - browsing • Video servers • Teleconferencing and Telemedicine • Wireless Networks

  38. Scalability • Picture Coding Symposium(April 1999) - panel on “The Future of Video Compression,” importance of scalability: • rate scalability (Internet and wireless) • temporal scalability (Internet and wireless) • spatial scalability (databases - MPEG-7) • content scalability (MPEG-4) (Computational Scalability - implementation issues)

  39. Scalability “Author and Compress once - decompress on any platform feed by any data pipe”

  40. Scalability: Compression Standards • Scalability in JPEG • progressive mode • JPEG 2000 • Scalability in MPEG-2 • scalability is layered • Scalability in MPEG-4 • layered • “content” • fine grain scalability (fgs)

  41. Color Embedded Zero-Tree Wavelet (CEZW) • Developed new technique known as Color Embedded Zero-Tree Wavelet (CEZW) • Modified EZW with trees connecting all color components • can be extended to other color spaces

  42. Spatial Orientation Trees EZW SPIHT

  43. New Spatial Orientation Tree (CEZW)

  44. Color Image Compression Original CEZW JPEG SPIHT

  45. Coding Artifacts CEZW Original JPEG SPIHT

  46. Comparison JPEG 0.25 bits/pixel CEZW 0.25 bits/pixel

  47. Color Compression - Experiments • Objectives: • Evaluate scalable color image compression techniques • Color Transformations • Spatial Orientation Trees and Coding Schemes • Embedded Coding • Embedded Zerotree Wavelet: Shapiro (Dec’93) • Set Partitioning in Hierarchical Trees: Said & Pearlman (Jun’96) • Color Embedded Zerotree Wavelets: Shen & Delp (Oct ‘97) M. Saenz, P. Salama, K. Shen and E. J. Delp, "An Evaluation of Color Embedded Wavelet Image Compression Techniques," VCIP 1999

  48. SAMCoW • New scalable video compression technique - Scalable Adaptive Motion COompensated Wavelet compression • Features of SAMCoW: • use wavelets on entire frame and for prediction error frames • uses adaptive motion compensation to reduce error propagation • CEZW is used for the wavelet coder on both the intra-coded frames and prediction error frames

  49. Generalized Hybrid Codec

  50. Adaptive Motion Compensation

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