1. COMLAB Multimedia Arts & Technologies Patrizio CAMPISI Marco CARLI Emanuele MAIORANAFederica BATTISTIMULTIMEDIA INFORMATION PROCESSINGAnna Maria VEGNI Veronica PALMA Marco LEOINMauro UGOLINIMarina SALATINOSMART ENVIRONMENTS Elena MAMMI Paolo SITALuca COSTANTINIDaria LA ROCCA Alessandro NeriEngineering DepartmentUniversity of Roma Tre, Via della Vasca Navale 84, 00146 Roma, Italy neri@uniroma3.it

2. Outline Introduction Smart Environments Feature Extraction Object recognition Distributed Video coding for multiple sources New Imaging Techniques Conclusions 3. SMART ENVIRONMENTSMART ENVIRONMENTinsieme di tecnologie basate su una forte integrazione tra apparati sensoriali, sistemi distribuiti di elaborazione tecnologie delle comunicazioni,che d 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 qualit della vita. 4. SMART ENVIRONMENTSMART ENVIRONMENTinsieme di tecnologie basate su una forte integrazione tra apparati sensoriali, sistemi distribuiti di elaborazione tecnologie delle comunicazioni,che d 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 qualit della vita.INFORMATION PROCESSING CHAINFiltering & ParameterFeatureSemanticDenoising estimationextractionAnalysis 5. Image Analysis Need foran 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). ApproachAdaptation of the basis to the local image contents, by selecting the elements from an highly redundant set (wave-form dictionary) Critical elementsdictionary setupconstruction of the best local representation (Minimum Description Length). Objectivelocal expansionefficiently 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. Gauss-Laguerre WaveletsFilters n(r, ) n = 1, k = 0 n = 2, k = 0 n = 3, k = 0 n = 4, k = 0Real partImaginarypart 1.0 0.5 0.0 Test imageEdgesLinesY-crossesX-crosses 7. Surround InhibitionInput 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 andcontours 8. Multiscale Contour DetectorOutput of the Canny edge detector for different scalesDestroyed junctionRestored Morphological dilation Superposition and logic ANDFine scale (small )Coarse scale (large ) Texture residuals Texture residuals Well detailed contoursWell detailed contours Preserved Junctions Preserved Junctions 9. Numerical resultsNoisy inputProposedimageapproach(SNR = 13dB)CannyCARTOON 10. Results and ComparisonNoisy input image Proposed approachCanny (SNR = 13dB)Surround inhibition CARTOON 11. Results and ComparisonNoisy input image Proposed approachCanny (SNR = 13dB)Surround inhibition CARTOON 12. Object Recognition- Video BrowsingImage Ranked ImageStoringCollection Query Image Submission FeaturesExtraction Image DBSimilarity FeaturesFeatures DBMeasurementExtraction 13. Analisi MultivisteKey points extractionKey point matching (invariant with respect scale rotation perspective changes)log2 yL. Sorgi, A. Neri. Keypoints Selection in the GaussLaguerre Transformed Domain - BMVC06 x 14. KEYPOINTS SELECTION: SYSTEM OUTLINE Pre-processing Smoothing and color conversionScalogram buildingScalogramKeypoints scale-space inspectionlocationDescriptorsconstructionDescriptorsKeypoints descriptorsnormalization 15. Image festures 2D Patterns: based on Zernike polinomials expansion. jf xi x0 Texture: Laguerre-Gauss local expansions hystograms Edge: relative phase of Laguerre-Gauss expansions 16. Position, orientation, and scale estimation Extensive retrieval experiments making use of quadtreedecomposition combined with Gauss-Laguerre CHFs, as well as onZernikes CHF have been performed on the Corel-1000-A Database. The average percentage of recovered relevant images is greaterthan 0.96 while the other methods attain at the maximum 0.87 (globalsearch) 17. Distribute Video Coding 18. Experimental resultsBreakdancer multiview sequence.Source: Veronica Palma, PhD Thesis5048 MDVC_Zernike46 H.264/AVC44 Encoder driven fusion [1]42PSNR (dB)40383634323080200300800 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. Experimental ResultsObjective Video Quality Assessment 20. Plenoptic cameras Misurazione e codificadellintensit delcampo ricevuto dauna data direzione (aduna data lunghezzadonda) 21. PLENOPTIC CAMERASingle exposure.Differentprocessing 22. Plenoptic processing 23. Grazie per lAttenzione 24. Estrazione e interpretazione di interazioni sociali