Structural, Syntactic, and Statistical Pattern Recognition: by Narendra Ahuja, Sinisa Todorovic (auth.), Edwin R. Hancock,

By Narendra Ahuja, Sinisa Todorovic (auth.), Edwin R. Hancock, Richard C. Wilson, Terry Windeatt, Ilkay Ulusoy, Francisco Escolano (eds.)

This quantity within the Springer Lecture Notes in machine technological know-how (LNCS) sequence includes the papers awarded on the S+SSPR 2010 Workshops, which was once the 7th party that SPR and SSPR workshops were held together. S+SSPR 2010 was once prepared by means of TC1 and TC2, Technical Committees of the foreign organization for development Recognition(IAPR), andheld inCesme, Izmir, whichis a seashore inn at the Aegean coast of Turkey. The convention happened in the course of August 18–20, 2010, just a couple of days ahead of the twentieth overseas convention on development attractiveness (ICPR) which used to be held in Istanbul. the purpose of the sequence of workshops is to create a world discussion board for the presentation of the most recent effects and trade of rules among researchers within the ?elds of statistical and structural development popularity. SPR 2010 and SSPR 2010 acquired a complete of ninety nine paper submissions from many di?erent international locations all over the world, giving it a very foreign point of view, as has been the case for past S+SSPR workshops. This quantity comprises 70 authorized papers, 39 for oral and 31 for poster presentation. as well as par- lel oral periods for SPR and SSPR, there have been joint oral classes of curiosity to either SPR and SSPR groups. additionally, to augment the workshop event, there have been joint panel periods on “Structural studying” and “Clustering,” within which brief writer displays have been by way of dialogue. one other innovation this yr used to be the ?lming of the lawsuits via Videol- tures.

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Extra info for Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshop, SSPR&SPR 2010, Cesme, Izmir, Turkey, August 18-20, 2010. Proceedings

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PAMI 28(6), 954–967 (2006) 62. : Discovering shape classes using tree editdistance and pairwise clustering. IJCV 72(3), 259–285 (2007) 63. : Shape matching and recognition - using generative models and informative features. ) ECCV 2004. LNCS, vol. 3023, pp. 195–209. Springer, Heidelberg (2004) 64. : Recognizing articulated objects using a region-based invariant transform. IEEE Trans. Pattern Analysis Machine Intelligence 27(10), 1660–1665 (2005) From Region Based Image Representation to Object Discovery and Recognition 19 65.

Keeping the best matching locations and scales, the search for initial detections is repeated using the covariance matrices of NR partially occluded subregions at the second phase. The distance of the object model O and a candidate region R is computed as NR |O − R| = minj O R O d(XR i , Xi ) − d(Xj , Xj ) (15) i=0 where the worst match is dismissed to provide robustness towards possible occlusions and changes. The region with the smallest distance is selected as the matching region. Sample matching results are presented in Figure 3 where the manifold search using the Riemannian metrics is compared to the histogram features using the Bhattacharyya distance.

IEEE TPAMI 22(8), 888–905 (2000) 50. : Indexing hierarchical structures using graph spectra. IEEE TPAMI 27(7), 1125–1140 (2005) 51. : Shock graphs and shape matching. IJCV 35(1), 13–32 (1999) 52. : Learning hierarchical models of scenes, objects, and parts. In: ICCV, vol. 2, pp. 1331–1338 (2005) 53. : Multiscale image segmentation by integrated edge and region detection. IEEE Trans. Image Processing 6(5), 642–655 (1997) 54. : Extracting subimages of an unknown category from a set of images. In: CVPR, vol.

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