By Klaus Broelemann, Xiaoyi Jiang (auth.), Young-Bin Kwon, Jean-Marc Ogier (eds.)
This publication constitutes the completely refereed post-conference complaints of the ninth overseas Workshop on pics popularity (GREC 2011), held in Seoul, Korea, September 15-16, 2011. The 25 revised complete papers awarded have been rigorously chosen from a variety of submissions. photos acceptance is a subfield of record photo research that offers with graphical entities in engineering drawings, sketches, maps, architectural plans, musical rankings, mathematical notation, tables, and diagrams. consequently the convention papers are prepared in five technical classes, protecting the themes equivalent to map and old files, image and emblem attractiveness, comic strip and drawings, functionality evaluate and problem processing.
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Extra info for Graphics Recognition. New Trends and Challenges: 9th International Workshop, GREC 2011, Seoul, Korea, September 15-16, 2011, Revised Selected Papers
1007/s10851-005-4783-8 2. : Dual norms and image decomposition models. 1007/s11263-005-4948-3 24 M. Coustaty et al. 3. : Structure-texture image decomposition - modeling, algorithms, and parameter selection. 1007/s11263-006-4331-z 4. : Total Variation Minimization and a Class of Binary MRF Models. L. ) EMMCVPR 2005. LNCS, vol. 3757, pp. 136–152. Springer, Heidelberg (2005) 5. : Using ontologies to reduce the semantic gap between historians and image processing algorithms. In: IEEE International Conference on Document Analysis and Recognition, Beijing, China, pp.
CVIU 90(3), 258–294 (2003) 2. : On the Joint Use of a Structural Signature and a Galois Lattice Classiﬁer for Symbol Recognition. -M. ) GREC 2007. LNCS, vol. 5046, pp. 61–70. : Symbol Recognition 3. , Combining Vectorial and Statistical Features. , Llad´ os, J. ) GREC 2005. LNCS, vol. 3926, pp. 76–87. Springer, Heidelberg (2006) 4. : Symbol spotting using full visibility graph representation. In: GREC, pp. 1–7 (2007) 5. : Graphic symbol recognition using graph based signature and bayesian network classiﬁer.
These parameterized image-processing filters are then applied to efficiently clean the map image and remove existing noise. -Y. Chiang, S. A. Knoblock In this road vectorization example, the first step of our cleaning process removes large non-road objects (noise objects that are thicker than road lines); the second step removes small non-road objects. To identify large non-road objects in the road-layerextraction result (Figure 2(c)), first, an image (Figure 2(d)) is created in which the majority of road lines are eliminated by applying the erosion operator and thus nonroad objects remained.