. We propose a deconstruction framework to recognize and find articulated objects. In particular we are interested in human arm and leg articulations. The deconstruction view of recognition naturally decomposes the problem of finding an object in an image, into the one of (i) extracting key features in an image, (ii) detecting key points in the models, (iii) segmenting an image, and (iv) comparing shapes. All of these subproblems can not be resolved independently. Together, they reconstruct the object in the image. We briefly address (i) and (ii) to focus on solving together shape similarity and segmentation, combining top-down & bottom-up algorithms. We show that the visual deconstruction approach is derived as an optimization for a Bayesian-Information theory, and that the whole process is naturally generated by the guaranteed Dijkstra optimization algorithm. 1 Introduction We investigate the problems of recognizing and finding articulated and deformable objects. In particular we s...

This paper describes an ontology for inland water features built using formal concept analysis and supervaluation semantics. The first is used to generate a complete lattice of the water domain, whereas supervaluation semantics is used to model the variability of the concepts in terms of threshold parameters. We also present an algorithm for a mechanism of individuation and classification of water features, from snapshots of river networks, according to the proposed ontology. 1

We propose a new deformable shape model Active Shape Structural Model (ASSM) for recognition and reconstruction. The main features of ASSM are: (1) It describes variations of shape not only statistically as Active shape/Appearance model but also by structural variations. (2) Statistical and structural prior knowledge is integrated resulting in a multi-resolution shape description such that the statistical variation becomes more constrained as structural information is added. Experiments on hand drawn sketches of mechanical systems using electronic ink demonstrate the ability of the deformable model to recognize objects structurally and reconstruct them statistically.

of the Dissertation Stephan Al-Zubi Abstract This thesis proposes a new shape model called the Active Shape Structural Model (ASSM). The ASSM combines both statistical and structural a-priori knowledge about shape variation. The statistical a-priori knowledge models co-variations between two or more parts of the shape structure (e.g. co-deformation, joint articulation). The structural a-priori knowledge specifies which structural parts can be statistically related.