EURASIP Journal on Applied Signal Processing

  Special Issue on

  Search and Retrieval of 3D Content and Associated Knowledge
  Extraction and Propagation

  Call for Papers

With the general availability of 3D digitizers, scanners, and the
technology innovation in 3D graphics and computational equipment,
large collections of 3D graphical models can be readily built up
for different applications (e.g., in CAD/CAM, games design, computer
animations, manufacturing and molecular biology). For such large
databases, the method whereby 3D models are sought merits careful
consideration. The simple and efficient query-by-content approach
has, up to now, been almost universally adopted in the literature.
Any such method, however, must first deal with the proper positioning
of the 3D models. The two prevalent-in-the-literature methods for the
solution to this problem seek either

 o Pose Normalization: Models are first placed into a canonical
   coordinate frame (normalizing for translation, scaling, and
   rotation). Then, the best measure of similarity is found by
   comparing the extracted feature vectors, or
 o Descriptor Invariance: Models are described in a transformation
   invariant manner, so that any transformation of a model will be
   described in the same way, and the best measure of similarity
   is obtained at any transformation.

The existing 3D retrieval systems allow the user to perform
queries by example. The queried 3D model is then processed,
low-level geometrical features are extracted, and similar
objects are retrieved from a local database. A shortcoming
of the methods that have been proposed so far regarding the
3D object retrieval, is that neither is the semantic information
(high-level features) attached to the (low-level) geometric features
of the 3D content, nor are the personalization options taken into
account, which would significantly improve the retrieved results.
Moreover, few systems exist so far to take into account annotation
and relevance feedback techniques, which are very popular among
the corresponding content-based image retrieval systems (CBIR).

Most existing CBIR systems using knowledge either annotate all the
objects in the database (full annotation) or annotate a subset of
the database manually selected (partial annotation). As the database
becomes larger, full annotation is increasingly difficult because
of the manual effort needed. Partial annotation is relatively affordable
and trims down the heavy manual labor. Once the database is partially
annotated, traditional image analysis methods are used to derive semantics
of the objects not yet annotated. However, it is not clear ^Óhow much^Ô
annotation is sufficient for a specific database and what the best subset
of objects to annotate is. In other words how the knowledge will be
propagated. Such techniques have not been presented so far regarding
the 3D case.

Relevance feedback was first proposed as an interactive tool in text-based
retrieval. Since then it has been proven to be a powerful tool and has
become a major focus of research in the area of content-based search
and retrieval. In the traditional computer centric approaches, which
have been proposed so far, the ^Óbest^Ô representations and weights are fixed
and they cannot effectively model high-level concepts and user's perception
subjectivity. In order to overcome these limitations of the computer centric
approach, techniques based on relevant feedback, in which the human and
computer interact to refine high-level queries to representations based
on low-level features, should be developed.

The aim of this special issue is to focus on recent developments in this
expanding research area. The special issue will focus on novel approaches
in 3D object retrieval, transforms and methods for efficient geometric
feature extraction, annotation and relevance feedback techniques, knowledge
propagation (e.g., using Bayesian networks), and their combinations so as
to produce a single, powerful, and dominant solution.

Topics of interest include (but are not limited to):

    o 3D content-based search and retrieval methods (volume/surface-based)
    o Partial matching of 3D objects
    o Rotation invariant feature extraction methods for 3D objects
    o Graph-based and topology-based methods
    o 3D data and knowledge representation
    o Semantic and knowledge propagation over heterogeneous metadata types
    o Annotation and relevance feedback techniques for 3D objects


Authors should follow the EURASIP JASP manuscript format
described at the journal site http://www.hindawi.com.eg/asp/ .
Prospective authors should submit an electronic copy of their
complete manuscript through the EURASIP JASP's manuscript
tracking system at journal's web site, according
to the following timetable.

  Manuscript Due            February 1, 2006
  Acceptance Notification   June 1, 2006
  Final Manuscript Due      September 1, 2006
  Publication Date          4th Quarter, 2006

GUEST EDITORS:

Tsuhan Chen, Carnegie Mellon University, Pittsburgh, PA 15213, USA;
tsuhan@cmu.edu

Ming Ouhyoung, National Taiwan University, Taipei 106, Taiwan;
ming@csie.ntu.edu.tw

Petros Daras, Informatics and Telematics Institute, Centre for
Research and Technology Hellas, 57001 Thermi, Thessaloniki,
Greece; daras@iti.gr