Vectorial Signatures for Symbol Discrimination

Colloque avec actes et comité de lecture. internationale.International audienceIn this paper, we present a method based on vectorial signatures, which aims at discriminating, by a fast technique, symbols represented within technical documents. The use of signatures on this kind of document has an obvious interest. Indeed, considering a raw vectorial description of the graphical layer of a technical document (e.g. a set of arcs and segments), signatures can be used to perform a pre-processing step before a "traditional" graphics recognition processing, or can be used to establish a classification that can be sufficient to feed a further indexation step. To compute vectorial signatures, we have based our approach on a method proposed by Etemadi et al., who study spatial relations between primitives to solve a vision problem. We considerer five types of relations, invariant to transformations like rotation or scaling, between neighboring segments: parallelism with or without overlapping, collinearity, L junctions and V junctions. A quality factor is computed for each of the relations, computable with low requirements of power. The signature of all models of symbols that could be found in a given document are computed and matched against the signature of the document, in order to determine what symbols the document is likely to contain. The quality factor associated with each relation is used to prune relations whose quality factor is too low. We present finally the first tests obtained with this method, and we discuss the improvements we plan to do

Superquadric-Based Object Recognition

This paper proposes a technique for object recognition using superquadric built models. Superquadrics, which are three dimensional models suitable for part-level representation of objects, are reconstructed from range images using the recover-and-select paradigm. Using an interpretation three, the presence of an object in the scene from the model database can be hypothesized. These hypotheses are verified by projecting and re-fitting the object model to the range image which at the same time enables a better localization of the object in the scene

Consistent Matting for Light Field Images

Abstract. We present a new image matting algorithm to extract consistent alpha mattes across sub-images of a light field image. Instead of matting each sub-image individually, our approach utilizes the epipolar plane image (EPI) to construct comprehensive foreground and background sample sets across the sub-images without missing a true sample. The sample sets represent all color variation of foreground and background in a light field image, and the optimal alpha matte is obtained by choosing the best combination of foreground and background samples that minimizes the linear composite error subject to the EPI correspondence constraint. To further preserve consistency of the estimated alpha mattes across different sub-images, we impose a smoothness constraint along the EPI of alpha mattes. In experimental evaluations, we have created a dataset where the ground truth alpha mattes of light field images were obtained by using the blue screen technique. A variety of experiments show that our proposed algorithm produces both visually and quantitatively high-quality matting results for light field images