3D Object Localization and Shape Matching


July 17, 2008


Radu Horaud


INRIA Grenoble Rhône-Alpes, France


This talk consists of two short talks.

Localization of 3D Audio-Visual Objects Using Unsupervised Clustering
We address the problem of localizing objects that can be both seen and heard. We exploit the benefits of a human-like configuration of sensors (binaural and binocular) for gathering auditory and visual observations. It is shown that the localization problem can be recast as the task of clustering the audio-visual observations into coherent groups. We propose a probabilistic generative model that captures the relations between audio and visual observations. This model maps the data into a common audio-visual 3D representation via a pair of mixture models. Inference is performed by a version of EM which provides cooperative estimates of both the auditory activity and the 3D position of each object. We describe several experiments with single- and multiple-speaker localization, in the presence of other audio sources.

Robust Shape and Graph Matching using Laplacian Embedding and EM
Shape matching is a central topic in computational vision, medical image analysis, etc. One instance of shape matching is to find dense correspondences between point representations. The problem of matching 3-D articulated shapes remains very difficult, mainly because it is not clear how to choose a transformation group under whose action the shapes could be studied. One possible approach is to represent shapes by locally connected sets of points, i.e. sparse graphs, and to use a spectral embedding method in order to map these graphs onto a lower dimensional space. As a result, a dense match between shapes can be found through rigid point registration of their embeddings. We will describe in detail the matching method and show numerous results with voxel- and mesh-data.


Radu Horaud

Radu Horaud holds a position of director of research at INRIA Grenoble Rhône-Alpes, France. He is the leader of the PERCEPTION group. Radu Horaud was born in Romania. Radu Horaud’s research interests cover computer vision, image understanding, augmented reality, and robotics. He is the author of over 100 scientific publications. He is an area editor for Computer Vision and Image Understanding (Elsevier), and he is a member of the editorial boards of The International Journal of Robotics Research (Sage), The International Journal of Computer Vision (Kluwer), The Image and Vision Computing Journal, and The Machine Vision and Applications Journal (Springer). In 2001 he was programme co-chair of the IEEE Eighth International Conference on Computer Vision (ICCV’01) and chaired the computer vision David Marr prize awarded in 2001.