SECOND-MVD (Synthesis Error COmpeNsateD Multiview Video plus Depth) is an alternative 3D format that we introduce for representation of multiview video. In this data format, images at some viewpoint remain original, and the others are converted to a novel format. Residual based representation, such as layered depth video and free-viewpoint TV data unit were proposed. We propose hybrid image that not only consists of residual but also remainder pixels. Generation and reconstruction process of a hybrid image uses virtual image synthesized by the images that remained original in SECOND-MVD. In this paper, we investigate the compression performance of SECOND-MVD using hybrid image. Experiments demonstrate reduction in bit rate using hybrid image against residual image in SECOND-MVD framework.

Head-related transfer functions (HRTFs) are essential for creating a virtual sound source when sound waves are transmitted through headphones. The measurement of HRTFs is a complicated and time-consuming task. Therefore, the interpolation of HRTFs is crucial for virtual auditory display systems in which both listeners and sound objects are likely to move in a virtual auditory space. In this study, HRTFs are measured with a high spatial resolution in order to develop an effective interpolation method. An analysis of HRTFs indicates that the functions exhibit periodicity in amplitude along the azimuthal angle. The optimum filter coefficients required to interpolate HRTFs from several functions measured along multiple azimuthal directions are derived. Computer simulations indicate that in comparison to conventional methods, the proposed method yields less estimation error in the interpolation of HRTFs. Listening tests indicate that the proposed method can provide better perception of virtual sound.

In this paper, we describe a real-time 3D human head pose estimation algorithm by means of eye tracking and on-line reconstructed 3D face data from a stereo image pair. A frame by frame based face coordinate system is formed using eye locations and a face plane which doesn't rely on distinctive facial features. The rest of the algorithm follows the conventional approach of pose estimation between the face coordinate system and a reference coordinate system without any initialization and model fitting. By not relying on arbitrary data on the face such as nose ridge line or a mouth corner points, we minimize the impact of errors which may come from their 3D estimates and facial expression changes. Experimental results demonstrate the accuracy and robustness of the algorithm.

Numerous efforts on content-based music information retrieval have been presented in recent years. However, the object of such existing research is to retrieve a specific song from a large music database. In this research, we propose a music retrieval method which retrieves songs based on the user's musical preferences. This enables users to discover new songs which they are expected to like. Since music preferences are expected to be highly ambiguous, we propose the implementation of relevance feedback methods to improve the performance of our music information retrieval method. In order to reduce the burden of users to input learning data to the system, we also propose a method to generate user profiles based on genre preferences, and refinement of such profiles based on relevance feedback. Evaluation experiments are conducted based on a corpus of music data with user ratings. Results of these experiments prove the effectiveness of our method.

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Document filtering is a task to retrieve documents relevant to a user's profile from a flow of documents. Generally, filtering systems calculate the similarity between the profile and each incoming document, and retrieve documents with similarity higher than a threshold. However, many systems set a relatively high threshold to reduce retrieval of non-relevant documents, which results in the ignorance of many relevant documents. In this paper, we propose the use of a non-relevant information profile to reduce the mistaken retrieval of non-relevant documents. Results from experiments show that this filter has successfully rejected a sufficient number of non-relevant documents, resulting in an improvement of filtering performance.

This paper describes a method of classifying semantically similar nouns. The approach is based on the "distributional hypothesis". Our approach is characterized by distinguishing among senses of the same word in order to resolve the "polysemy" issue. The classification result demonstrates that our approach is successful.