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Augmented reality applications using stereo head-mounted displays are not capable of perfectly blending real and virtual objects. For example, depth in the real world is perceived through cues such as accommodation and vergence. However, in stereo head-mounted displays these cues are disconnected since the virtual is generally projected at a static distance, while vergence changes with depth. This conflict can result in biased depth estimation of virtual objects in a real environment. In this research, we examined whether redundant tactile feedback can reduce the bias in perceived depth in a reaching task. In particular, our experiments proved that a tactile mapping of distance to vibration intensity or vibration position on the skin can be used to determine a virtual object's depth. Depth estimation when using only tactile feedback was more accurate than when using only visual feedback, and when using visuotactile feedback it was more precise and occurred faster than when using unimodal feedback. Our work demonstrates the value of multimodal feedback in augmented reality applications that require correct depth perception, and provides insights on various possible visuotactile implementations.

This paper describes the design of Tonic, a novel web interface for music discovery and playlist creation. Tonic maps songs into a two dimensional space using a combination of free tags, metadata, and audio-derived features. Search results are presented in this two dimensional space using a combination of clustering and ranking visualization strategies. Tonic was ranked first in the 2014 MIREX User Experience Grand Challenge, where it was evaluated in terms of learnability, robustness and overall user satisfaction, amongst others.

3D media has emerged rapidly as a new type of content within the multimedia domain. The recent acceleration of 3D content production, witnessed across all fields up to user-generated content, is causing a huge amount of traffic and data stored and transmitted using Internet technologies. Recent advances in 3D acquisition and 3D graphics rendering technologies boosted the creation of 3D model archives for several application domains. These include archaeology and cultural heritage, computer-assisted design (CAD), medicine and bioinformatics, 3D face recognition and security, entertainment and serious gaming, spatial data and 3D city management. Search engines will soon become a key interaction tool for engaging with this data deluge, and 3D content-based retrieval methods will be crucial in the development of effective 3D search engines: visual media are meant to be seen and should be searched accordingly. 3D content-based retrieval is attracting researchers from different fields: computer vision, computer graphics, machine learning, human-computer interaction, and the semantic web. Since 2008, a series of workshops specifically devoted to the topic was initiated under the auspices of the Eurographics association. The first EG 3D Object Retrieval (3DOR) workshop took place in Crete, April 2008, followed by 3DOR'09 in Munich, March 2009, and 3DOR'10 in Norkopping, May 2010. The response of the community in all these years was encouraging in terms of number of submission and attendance rate. Due to the co-location of the 3DOR workshop with the Eurographics conference, the events primarily addressed the computer graphics community. Now, the co-location with ACM Multimedia 2010, the worldwide premier multimedia conference, gave us the opportunity to meet the multimedia community and further promote a cross-fertilization ground that hopefully will stimulate further discussions on the next steps in this important research area. The response to the call for participation was a success: even if scheduled shortly after the EG 3DOR'10 workshop, the ACM 3DOR'10 received 24 full paper submissions on various topics related to 3D retrieval, ranging from new indexing methods for generic 3D models to context-specific methods, such as face recognition and molecular data analysis. Out of the 24 submissions received, 7 contributions were accepted as oral papers (acceptance rate 30%), and 7 as poster papers. The ACM 3DOR'10 workshop will feature a one-day technical programme, with the presentation of the full papers and poster session. The invited talk given by Prof. Anuj Srivastava on Elastic Riemannian Frameworks and Statistical Tools for Shape Analysis complements the programme. The 3D Object Retrieval workshops gathered and continues to gather great interest in the research community and there are several people we would like to thank for keeping alive this interest: first of all, we would like to acknowledge and thank Ioannis Patrikakis (Democritus University of Thrace, Greece) and Theoharis Theoharis (University of Athens, Greece) for having started the 3DOR workshop series; Alberto Del Bimbo, for the encouragement to bring 3DOR closer to ACM Multimedia 2010; the ACM - 3DOR'10 PC members and reviewers for their efforts and commitment; all the authors of the submitted papers that are demonstrating the importance of the topic. We would like to thank the Institut TELECOM for the financial support. We look forward to the next event on 3D Object Retrieval.

In this paper, we outline some of the main challenges facing trademark searchers today, and discuss the extent to which current automated systems are meeting those challenges.

Ensuring the uniqueness of trademark images and protecting their identities are the most important objectives for the trademark registration process. To prevent trademark infringement, each new trademark must be compared to a database of existing trademarks. Given a newly designed trademark image, trademark retrieval systems are not only concerned with finding images with similar shapes but also locating images with similar layouts. Performing a linear-search, i.e., computing the similarity between the query and each database entry and selecting the closest one, is inefficient for large database systems. An effective and efficient indexing mechanism is, therefore, essential to select a small collection of candidates. This paper proposes a framework in which a graph-based indexing schema will be applied to facilitate efficient trademark retrieval based on spatial relations between image components, regardless of mutual shape similarity.Our framework starts by segmenting trademark images into distinct shapes using a shape identification algorithm. Identified shapes are then encoded automatically into an attributed graph whose vertices represent shapes and whose edges show spatial relations (both directional and topological) between the shapes. Using a graph-based indexing schema, the topological structure of the graph as well as that of its subgraphs are represented as vectors in which the components correspond to the sorted Laplacian eigenvalues of the graph or subgraphs. Having established the signatures, the indexing amounts to a nearest neighbour search in a model database. For a query graph and a large graph data set, the indexing problem is reformulated as that of fast selection of candidate graphs whose signatures are close to the query signature in the vector space. An extensive set of recognition trials, including a comparison with manually constructed graphs, show the efficacy of both the automatic graph construction process and the indexing schema.

This paper introduces a measure for computing the dissimilarity between multiple polylines and a polygon based on the turning function, and describes a part-based retrieval system using that dissimilarity measure. This dissimilarity can be efficiently computed in time O(kmn log mn), where m denotes the number of vertices in the polygon, and n is the total number of vertices in the k polylines that are matched against the polygon. This dissimilarity measure identifies similarities even when a significant portion of one shape is different from the other, for example because the shape is articulated, or because of occlusion or distortion. The effectiveness of the dissimilarity measure in demonstrated in a part-based shape retrieval system. Quantitative experimental verification is performed with a known ground-truth, the MPEG-7 Core Experiment test set, in a comparison with the Curvature Scale Space method, and a global turning angle function method.