Strokes detection for skeletonisation of characters shapes

Skeletonisation is a key process in character recognition in natural images. Under the assumption that a character is made of a stroke of uniform colour, with small variation in thickness, the process of recognising characters can be decomposed in the three steps. First the image is segmented, then each segment is transformed into a set of connected strokes (skeletonisation), which are then abstracted in a descriptor that can be used to recognise the character. The main issue with skeletonisation is the sensitivity with noise, and especially, the presence of holes in the masks. In this article, a new method for the extraction of strokes is presented, which address the problem of holes in the mask and does not use any parameters.CADICS; ELLIIT; CUA

On the complexity of Submap Isomorphism

International audienceGeneralized maps describe the subdivision of objects in cells, and incidence and adjacency relations between cells, and they are widely used to model 2D and 3D images. Recently, we have defined submap isomorphism, which involves deciding if a copy of a pattern map may be found in a target map, and we have described a polynomial time algorithm for solving this problem when the pattern map is connected. In this paper, we show that submap isomorphism becomes NP-complete when the pattern map is not connected, by reducing the NP-complete problem Planar-4 3-SAT to it

Map Edit Distance vs. Graph Edit Distance for Matching Images

International audienceGeneralized maps are widely used to model the topology of nD objects (such as 2D or 3D images) by means of incidence and adjacency relationships between cells (0D vertices, 1D edges, 2D faces, 3D volumes, ...). Recently, we have introduced a map edit distance. This distance compares maps by means of a minimum cost sequence of edit operations that should be performed to transform a map into another map. In this paper, we introduce labelled maps and we show how the map edit distance may be extended to compare labeled maps. We experimentally compare our map edit distance to the graph edit distance for matching regions of different segmentations of a same image

A causal extraction scheme in top-down pyramids for large images segmentation

International audienceApplicative fields based on the analysis of large images must deal with two important problems. First, the size in memory of such images usually forbids a global image analysis hereby inducing numerous problems for the design of a global image partition. Second, due to the high resolution of such images, global features only appear at low resolutions and a single resolution analysis may loose important information. The tiled top-down pyramidal model has been designed to solve this two major challenges. This model provides a hierarchical encoding of the image at single or multiple resolutions using a top-down construction scheme. Moreover, the use of tiles bounds the amount of memory required by the model while allowing global image analysis. The main limitation of this model is the splitting step used to build one additional partition from the above level. Indeed, this step requires to temporary refine the split region up to the pixel level which entails high memory requirements and processing time. In this paper, we propose a new splitting step within the tiled top-down pyramidal framework which overcomes the previously mentioned limitations