Exploring dance movement data using sequence alignment methods

Despite the abundance of research on knowledge discovery from moving object databases, only a limited number of studies have examined the interaction between moving point objects in space over time. This paper describes a novel approach for measuring similarity in the interaction between moving objects. The proposed approach consists of three steps. First, we transform movement data into sequences of successive qualitative relations based on the Qualitative Trajectory Calculus (QTC). Second, sequence alignment methods are applied to measure the similarity between movement sequences. Finally, movement sequences are grouped based on similarity by means of an agglomerative hierarchical clustering method. The applicability of this approach is tested using movement data from samba and tango dancers

Evolution in the Clustering of Galaxies for Z < 1

Measuring the evolution in the clustering of galaxies over a large redshift range is a challenging problem. For a two-dimensional galaxy catalog, however, we can measure the galaxy-galaxy angular correlation function which provides information on the density distribution of galaxies. By utilizing photometric redshifts, we can measure the angular correlation function in redshift shells (Brunner 1997, Connolly et al. 1998) which minimizes the galaxy projection effect, and allows for a measurement of the evolution in the correlation strength with redshift. In this proceedings, we present some preliminary results which extend our previous work using more accurate photometric redshifts, and also incorporate absolute magnitudes, so that we can measure the evolution of clustering with either redshift or intrinsic luminosity.Comment: 6 pages, 6 figures requires paspconf.sty. To be published in "Photometric Redshifts and High Redshift Galaxies", eds. R. Weymann, L. Storrie-Lombardi, M. Sawicki & R. Brunner, (San Francisco: ASP Conference Series

Robust Temporally Coherent Laplacian Protrusion Segmentation of 3D Articulated Bodies

In motion analysis and understanding it is important to be able to fit a suitable model or structure to the temporal series of observed data, in order to describe motion patterns in a compact way, and to discriminate between them. In an unsupervised context, i.e., no prior model of the moving object(s) is available, such a structure has to be learned from the data in a bottom-up fashion. In recent times, volumetric approaches in which the motion is captured from a number of cameras and a voxel-set representation of the body is built from the camera views, have gained ground due to attractive features such as inherent view-invariance and robustness to occlusions. Automatic, unsupervised segmentation of moving bodies along entire sequences, in a temporally-coherent and robust way, has the potential to provide a means of constructing a bottom-up model of the moving body, and track motion cues that may be later exploited for motion classification. Spectral methods such as locally linear embedding (LLE) can be useful in this context, as they preserve "protrusions", i.e., high-curvature regions of the 3D volume, of articulated shapes, while improving their separation in a lower dimensional space, making them in this way easier to cluster. In this paper we therefore propose a spectral approach to unsupervised and temporally-coherent body-protrusion segmentation along time sequences. Volumetric shapes are clustered in an embedding space, clusters are propagated in time to ensure coherence, and merged or split to accommodate changes in the body's topology. Experiments on both synthetic and real sequences of dense voxel-set data are shown. This supports the ability of the proposed method to cluster body-parts consistently over time in a totally unsupervised fashion, its robustness to sampling density and shape quality, and its potential for bottom-up model constructionComment: 31 pages, 26 figure

Science Objectives and Early Results of the DEEP2 Redshift Survey

The DEIMOS spectrograph has now been installed on the Keck-II telescope and commissioning is nearly complete. The DEEP2 Redshift Survey, which will take approximately 120 nights at the Keck Observatory over a three year period and has been designed to utilize the power of DEIMOS, began in the summer of 2002. The multiplexing power and high efficiency of DEIMOS enables us to target 1000 faint galaxies per clear night. Our goal is to gather high-quality spectra of \~60,000 galaxies with z>0.75 in order to study the properties and large scale clustering of galaxies at z ~ 1. The survey will be executed at high spectral resolution, $R=\lambda/\Delta \lambda \approx 5000$, allowing us to work between the bright OH sky emission lines and to infer linewidths for many of the target galaxies (for several thousand objects, we will obtain rotation curves as well). The linewidth data will facilitate the execution of the classical redshift-volume cosmological test, which can provide a precision measurement of the equation of state of the Universe. This talk reviews the project, summarizes our science goals and presents some early DEIMOS data.Comment: 12 pages, 5 figures, talk presented at SPIE conference, Aug. 200