Semantic-based Segmentation and Annotation of 3D Models
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Presentation of be-smart a semantic web framework for digital shape understanding at ICIAP 2009
![Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],September, 9 2009 ICIAP09 - Laura Papaleo](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Semantic-based Segmentation and Annotation of 3D Models
- 1. Semantic-based Segmentation and Annotation of 3D Models Laura Papaleo , Leila De Floriani Department of Information and Computer Science University of Genova
Editor's Notes
- In this presentation I’m going to present the results in the development of a semantic web system for 3D shape understanding and annotation. The system can be located in a more wide framework of research that research group I belong to has established with the RPI Department of Cognitive Science leaded by Prof J. Hendler. Our research activities in digital shape reasoning, in general and in particular semantic-based annotation of 3d models The work in this direction, bridging semantic web technologies with shape modeling, is being carried out with a collaboration with the Cognitive Science Department leaded by J Hendler at RPI.
- I will briefly present you the main motivations behind our reasearch which suggested us to work in the direction of the desing of a semantic web framework for shape annotation. The core element for our framework is a graph-based representation called the two-level segmentation graph, and i will explain you the two levels. Since semantic annotation relies on segmentation, i will show the segmentation methods implemented and that can be combined, when necessary During the segmentation we can add semantic information to each recognized region thanks to a simple semantic-driven hierarchical tagging The last part of the presentation will be dedicated to future works and conclusions.
- this slide is just to remind that the activity we are working on is related to digital representations of 3D object and 3D objects are becoming widely available on the net and used in many disciplines. There is so a general need of organizing these representations in an intelligent way. One of the first project in this sense was a NSF project in 2002 which concentrated mainly on medical 3D objects and the way in which this objects can be analyzed and organized… to be searched well. In any case the key in this framework is to extract knowledge from digital representations and maintain that in order also to efficiently search and retrieve and eventually reason on them.
- Be-smart è un sistema per l’annotazione e l’analisi di forme o scene digitali che si basa o è ispirato dal web semantico. Questo vuol dire che be-smart ha l’obiettivo di organizzare tutte le informazioni associate alla forma digitale in analisi usando metadata strutturati in ontologie. Le ontologie in questione possono essere relative al significato semantico da associare all’oggetto o a porzioni dello stesso, come pure ontologie che modellano la conoscenza associata alle procedure di creazione dell’oggetto e cose analoghe. (IMMAGINE) L’idea è quindi di formalizzare (mediante ontologie) la conoscenza associata ad uno specifico dominio applicativo, estrarre (in modo semi-automatico) le info dall’oggetto (o la scena), organizzare tali informazioni (rispetto alle ontologie) – quindi annotare al fine di riusare gli oggetti annotati o parti di questi e condividerli in ambienti distribuiti (ad esempio il web).
- In be-SMART different modules act as a team in generating the final enriched shape representation, annotated at different levels of abstraction. We list here the modules we have defined (the architecture of the system is depicted in Fig. 1): Geometry and Topology Analyzer (GTA): it analyses the input shape model and extracts geometrical/topological information which is maintained in the enriched shape model and as instance values of a given ontology. Topological Decomposer (TD): starting from the information extracted by the GTA module, this module produces a graph-based representation ( decomposition graph ) of the shape model into nearly manifold components. Manual Segmentation module (MS): This module offers both simple and advanced editing functionalities allowing a user to select portions of the model. The segmentation is maintained in the decomposition graph . Automatic Segmentation module (AS): This module offers the possibility of applying automatic segmentation algorithms for decomposing the manifold components into meaningful parts (according to context-dependent criteria). The segmentation is maintained in the decomposition graph . Semantic Annotator (SA): This module offers the possibility of associating specific metadata values to specific portions of the decomposed model according to pre-loaded ontologies. Basically, it associates metadata with nodes of the decomposition graph which describe the decomposed model.
- Qui diciamo che tutto il ragionamento relativo all’oggetto (o alla scena) viene fatto basandosi su una rappresentazione a grafo Il grafo si chiama segmentation graph ed è un two-level segmentation graph. Il primo livello (attivo nel caso di modelli non-manifold) etc Il secondo livello si chiama manifodl segmentation graph Le informazioni semantiche verranno associate ai nodi del grafo (o eventualmente, se necessario anche agli archi) e quindi alle porzioni di oggetto…
- In our case, since X3D allows to define shapes using vertices and not edges, the use of an implementation of the original approach would have been computationally expensive. Every time the user would draw the stroke, the system should compute the intersection between a circle of radius r and all the edges present in the scene and projected on the viewplane.We decided to apply the algorithm on the vertices of the model. This change does not modify the general methodology, but it allows us to reduce the number of operations to be performed.With our choice, in fact, we do not have to compute intersections (solving linear systems) but only euclidean distances. Additionally, we have been able to extend the procedure to surface meshes which are not represented only by triangles, using in this way all the faces types defined by the X3D standard. Furthermore, we extended the method to special cases: we can treat the case in which, given a stroke (as a set of circles) there is no vertex inside it connecting the initial and final vertex of the stroke: each time we cannot find a connection inside the stroke, we search for the nearest point in the surface model and we let the path passing from it. This procedure solves also the case in which, given the stroke, the system cannot find an initial and/or final vertex. For automatic computation of the cut in the not visible part of the model, we improved the original method restricting the search of the connections to a subset of vertices. For doing this we compute the visibility of each vertex before performing the cut.
- Some notes: using our tags - organized hierarchically - we are able to re-merge the segmented regions simply by checking the names of the regions and by merging their faces and vertices. looking at the name of a given region C we can access immediately to its history.
- one tab collects the geometrical information, automatically extracted (geometry); another tab (adjacency) describes the adjacency information, again automatically extracted. The last working tab (semantic) is, instead, devoted to the userdefined semantic annotation.
- La visualizzazione del grafo per adesso è per modelli manifold