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