Multimedia Tools and Applications
About: Multimedia Tools and Applications is an academic journal. The journal publishes majorly in the area(s): Feature (computer vision) & Encryption. It has an ISSN identifier of 1380-7501. Over the lifetime, 11779 publication(s) have been published receiving 119368 citation(s).
Topics: Feature (computer vision), Encryption, Digital watermarking, Convolutional neural network, Feature extraction
01 Sep 2008-Multimedia Tools and Applications
Abstract: Recent developments in techniques for modeling, digitizing and visualizing 3D shapes has led to an explosion in the number of available 3D models on the Internet and in domain-specific databases. This has led to the development of 3D shape retrieval systems that, given a query object, retrieve similar 3D objects. For visualization, 3D shapes are often represented as a surface, in particular polygonal meshes, for example in VRML format. Often these models contain holes, intersecting polygons, are not manifold, and do not enclose a volume unambiguously. On the contrary, 3D volume models, such as solid models produced by CAD systems, or voxels models, enclose a volume properly. This paper surveys the literature on methods for content based 3D retrieval, taking into account the applicability to surface models as well as to volume models. The methods are evaluated with respect to several requirements of content based 3D shape retrieval, such as: (1) shape representation requirements, (2) properties of dissimilarity measures, (3) efficiency, (4) discrimination abilities, (5) ability to perform partial matching, (6) robustness, and (7) necessity of pose normalization. Finally, the advantages and limitations of the several approaches in content based 3D shape retrieval are discussed.
01 Jan 2011-Multimedia Tools and Applications
TL;DR: Challenges augmented reality is facing in each of these applications to go from the laboratories to the industry, as well as the future challenges the authors can forecast are also discussed in this paper.
Abstract: This paper surveys the current state-of-the-art of technology, systems and applications in Augmented Reality. It describes work performed by many different research groups, the purpose behind each new Augmented Reality system, and the difficulties and problems encountered when building some Augmented Reality applications. It surveys mobile augmented reality systems challenges and requirements for successful mobile systems. This paper summarizes the current applications of Augmented Reality and speculates on future applications and where current research will lead Augmented Reality's development. Challenges augmented reality is facing in each of these applications to go from the laboratories to the industry, as well as the future challenges we can forecast are also discussed in this paper. Section 1 gives an introduction to what Augmented Reality is and the motivations for developing this technology. Section 2 discusses Augmented Reality Technologies with computer vision methods, AR devices, interfaces and systems, and visualization tools. The mobile and wireless systems for Augmented Reality are discussed in Section 3. Four classes of current applications that have been explored are described in Section 4. These applications were chosen as they are the most famous type of applications encountered when researching AR apps. The future of augmented reality and the challenges they will be facing are discussed in Section 5.
01 Jan 2005-Multimedia Tools and Applications
TL;DR: A unifying and multimodal framework is put forward, which views a video document from the perspective of its author, which forms the guiding principle for identifying index types, for which automatic methods are found in literature.
Abstract: Efficient and effective handling of video documents depends on the availability of indexes. Manual indexing is unfeasible for large video collections. In this paper we survey several methods aiming at automating this time and resource consuming process. Good reviews on single modality based video indexing have appeared in literature. Effective indexing, however, requires a multimodal approach in which either the most appropriate modality is selected or the different modalities are used in collaborative fashion. Therefore, instead of separately treating the different information sources involved, and their specific algorithms, we focus on the similarities and differences between the modalities. To that end we put forward a unifying and multimodal framework, which views a video document from the perspective of its author. This framework forms the guiding principle for identifying index types, for which automatic methods are found in literature. It furthermore forms the basis for categorizing these different methods.
01 Nov 1996-Multimedia Tools and Applications
TL;DR: This paper reviews a number of recently available techniques in content analysis of visual media and their application to the indexing, retrieval, abstracting, relevance assessment, interactive perception, annotation and re-use of visual documents.
Abstract: This paper reviews a number of recently available techniques in content analysis of visual media and their application to the indexing, retrieval, abstracting, relevance assessment, interactive perception, annotation and re-use of visual documents.
01 Feb 2011-Multimedia Tools and Applications
TL;DR: A database of static images of human faces taken in uncontrolled indoor environment using five video surveillance cameras of various qualities to enable robust face recognition algorithms testing, emphasizing different law enforcement and surveillance use case scenarios is described.
Abstract: In this paper we describe a database of static images of human faces. Images were taken in uncontrolled indoor environment using five video surveillance cameras of various qualities. Database contains 4,160 static images (in visible and infrared spectrum) of 130 subjects. Images from different quality cameras should mimic real-world conditions and enable robust face recognition algorithms testing, emphasizing different law enforcement and surveillance use case scenarios. In addition to database description, this paper also elaborates on possible uses of the database and proposes a testing protocol. A baseline Principal Component Analysis (PCA) face recognition algorithm was tested following the proposed protocol. Other researchers can use these test results as a control algorithm performance score when testing their own algorithms on this dataset. Database is available to research community through the procedure described at www.scface.org .
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