COMLAB                                       Multimedia Arts & Technologies                                               ...
Outline•   Introduction•   Smart Environments•   Feature Extraction•   Object recognition•   Distributed Video coding for ...
SMART ENVIRONMENTSMART ENVIRONMENTinsieme di tecnologie basate su una forte integrazione tra• apparati sensoriali,• sistem...
SMART ENVIRONMENTSMART ENVIRONMENTinsieme di tecnologie basate su una forte integrazione tra• apparati sensoriali,• sistem...
Image Analysis•       Need for    –      an efficient and parsimonious representation of the various relevant           co...
Gauss-Laguerre WaveletsFilters   n(r,   )   n = 1, k = 0   n = 2, k = 0   n = 3, k = 0   n = 4, k = 0  Real part  Imaginar...
Surround Inhibition        Input image               Desired output           Canny edge detector                         ...
Multiscale Contour Detector        Output of the Canny edge detector for different scales                                 ...
Numerical resultsNoisy input    Proposedimage          approach(SNR = 13dB)  Canny        CARTOON
Results and ComparisonNoisy input image   Proposed approach      Canny (SNR = 13dB)                    Surround inhibition...
Results and ComparisonNoisy input image   Proposed approach      Canny (SNR = 13dB)                    Surround inhibition...
Object Recognition- Video Browsing              Image           Ranked Image              Storing          Collection     ...
Analisi MultivisteKey points extractionKey point matching (invariant with respect scale rotation perspective changes)   ...
KEYPOINTS SELECTION: SYSTEM OUTLINE                         Pre-processing Smoothing and color         conversion         ...
Image festures• 2D Patterns: based on Zernike polinomials expansion.                                                      ...
Position, orientation, and scale estimation• Extensive retrieval experiments making use of quadtree  decomposition combine...
Distribute Video Coding
Experimental results        ‘’Breakdancer’’ multiview sequence.        Source: Veronica Palma, PhD Thesis                 ...
Experimental ResultsObjective Video Quality Assessment
Plenoptic cameras• Misurazione e codifica  dell’intensità del  campo ricevuto da  una data direzione (ad  una data lunghez...
PLENOPTIC CAMERA              Single           exposure.            Different          processing
Plenoptic processing
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Estrazione e interpretazione di interazioni sociali
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Elettronica: Multimedia Information Processing in Smart Environments by Alessandro Neri

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Elettronica: Multimedia Information Processing in Smart Environments by Alessandro Neri

  1. 1. COMLAB Multimedia Arts & Technologies Patrizio CAMPISI Marco CARLI Emanuele MAIORANA Federica BATTISTIMULTIMEDIA INFORMATION PROCESSING Anna Maria VEGNI Veronica PALMA Marco LEO IN Mauro UGOLINI Marina SALATINO SMART ENVIRONMENTS Elena MAMMI Paolo SITA’ Luca COSTANTINI Daria LA ROCCA Alessandro Neri Engineering Department University of “Roma Tre”, Via della Vasca Navale 84, 00146 Roma, Italy neri@uniroma3.it
  2. 2. Outline• Introduction• Smart Environments• Feature Extraction• Object recognition• Distributed Video coding for multiple sources• New Imaging Techniques• Conclusions
  3. 3. SMART ENVIRONMENTSMART ENVIRONMENTinsieme di tecnologie basate su una forte integrazione tra• apparati sensoriali,• sistemi distribuiti di elaborazione• tecnologie delle comunicazioni,che dà luogo ad ambienti (casa, ufficio, ecc.) i cui servizi siadattano alle condizioni ambientali ed essendo in grado direagire opportunamente alla presenza di persone sono in gradodi produrre stimoli e interagire proattivamente con esse, ovveroanticipandone i desideri senza una mediazione cosciente, al finedi migliorare la qualità della vita.
  4. 4. SMART ENVIRONMENTSMART ENVIRONMENTinsieme di tecnologie basate su una forte integrazione tra• apparati sensoriali,• sistemi distribuiti di elaborazione• tecnologie delle comunicazioni,che dà luogo ad ambienti (casa, ufficio, ecc.) i cui servizi siadattano alle condizioni ambientali ed essendo in grado direagire opportunamente alla presenza di persone sono in gradodi produrre stimoli e interagire proattivamente con esse, ovveroanticipandone i desideri senza una mediazione cosciente, al finedi migliorare la qualità della vita. INFORMATION PROCESSING CHAIN Filtering & Parameter Feature Semantic Denoising estimation extraction Analysis
  5. 5. Image Analysis• Need for – an efficient and parsimonious representation of the various relevant components of a natural scene such as edges and textures (non achievable by means of a unique, non-redundant system).• Approach – Adaptation of the basis to the local image contents, by selecting the elements from an highly redundant set (wave-form dictionary)• Critical elements – dictionary setup – construction of the best local representation (Minimum Description Length).• Objective – local expansion – efficiently approximated by a few wave-forms based on specific patterns of visual relevance (edges, lines, crosses, etc.) whose scale, position and orientation can be varied in a parametric way
  6. 6. Gauss-Laguerre WaveletsFilters n(r, ) n = 1, k = 0 n = 2, k = 0 n = 3, k = 0 n = 4, k = 0 Real part Imaginary part 1.0 0.5 0.0 Test image Edges Lines Y-crosses X-crosses
  7. 7. Surround Inhibition Input image Desired output Canny edge detector output• Natural images may contain both texture and noise• Local luminance changes: strong on texture, weak on contours• Task: suppression of edges due to noise only• Human Visual System (HVS) easily discriminates between texture, noise and contours
  8. 8. Multiscale Contour Detector Output of the Canny edge detector for different scales Destroyed junction Restored • Morphological dilation • Superposition and logic ANDFine scale (small ) Coarse scale (large ) Texture residuals Texture residuals Well detailed contours Well detailed contours Preserved Junctions Preserved Junctions
  9. 9. Numerical resultsNoisy input Proposedimage approach(SNR = 13dB) Canny CARTOON
  10. 10. Results and ComparisonNoisy input image Proposed approach Canny (SNR = 13dB) Surround inhibition CARTOON
  11. 11. Results and ComparisonNoisy input image Proposed approach Canny (SNR = 13dB) Surround inhibition CARTOON
  12. 12. Object Recognition- Video Browsing Image Ranked Image Storing Collection Query Image Submission FeaturesExtraction Image DB Similarity Features Features DB Measurement Extraction
  13. 13. Analisi MultivisteKey points extractionKey point matching (invariant with respect scale rotation perspective changes) log2 σ y L. Sorgi, A. Neri. Keypoints Selection in the Gauss Laguerre Transformed Domain - BMVC06 x
  14. 14. KEYPOINTS SELECTION: SYSTEM OUTLINE Pre-processing Smoothing and color conversion Scalogram building ScalogramKeypoints scale-space inspection location Descriptors construction DescriptorsKeypoints descriptors normalization
  15. 15. Image festures• 2D Patterns: based on Zernike polinomials expansion. j f x i x0• Texture: Laguerre-Gauss local expansions hystograms• Edge: relative phase of Laguerre-Gauss expansions
  16. 16. Position, orientation, and scale estimation• Extensive retrieval experiments making use of quadtree decomposition combined with Gauss-Laguerre CHFs, as well as on Zernikes CHF have been performed on the Corel-1000-A Database.• The average percentage of recovered relevant images is greater than 0.96 while the other methods attain at the maximum 0.87 (global search)
  17. 17. Distribute Video Coding
  18. 18. Experimental results ‘’Breakdancer’’ multiview sequence. Source: Veronica Palma, PhD Thesis 50 48 MDVC_Zernike 46 H.264/AVC 44 Encoder driven fusion [1] 42 PSNR (dB) 40 38 36 34 32 30 80 200 300 800 Kbit/s[1] M. Ouaret, F. Dufaux and T. Ebrahimi, ‘’ MULTIVIEW DISTRIBUTED VIDEO CODING WITH ENCODER DRIVEN FUSION ‘’. In EUSIPCO Proceedings, 2007[2]M. Ouaret, F. Dufuax, and T. Ebrahimi. ‘’Recent advances in multi-view distributed video coding’’. In SPIE Mobile Multimedia/Image Processing forMilitary and Security Applications, April 2007.
  19. 19. Experimental ResultsObjective Video Quality Assessment
  20. 20. Plenoptic cameras• Misurazione e codifica dell’intensità del campo ricevuto da una data direzione (ad una data lunghezza d’onda)
  21. 21. PLENOPTIC CAMERA Single exposure. Different processing
  22. 22. Plenoptic processing
  23. 23. » Grazie per l’Attenzione
  24. 24. Estrazione e interpretazione di interazioni sociali

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