Robust pose estimation and recognition using non-Gaussian modeling of appearance subspaces

We present an original appearance model that generalizes the usual Gaussian visual subspace model to non-Gaussian and nonparametric distributions. It can be useful for the modeling and recognition of images under difficult conditions such as large occlusions and cluttered backgrounds. Inference under the model is efficiently solved using the mean shift algorithm

Characterizing bird migration phenology using data from standardized monitoring at bird observatories

Long-term data from standardized monitoring programmes at bird observatories are becoming increasingly available. These data are frequently used for detecting changes in the timing of bird migration that may relate to recent climate change. We present an overview of problematic issues in the analysis of these data, and review approaches to and methods for characterizing bird migration phenology and its change over time. Methods are illustrated and briefly compared using autumn data on garden warbler Sylvia borin from a standardized mist-netting programme at Lista bird observatory, southern Norway. Bird migration phenology is usually characterized rather coarsely using a small number of sample statistics such as mean, median and selected quantiles. We present 2 alternative approaches. Smoothing methods describe the within-season pattern in the data at an arbitrary level of detail, while fitting a parametric seasonal distribution curve offers a coarse description of migration phenology relatively robust to sampling effects. Various methods for analyzing linear trends in the timing of bird migration are reviewed and discussed. Exploratory studies using long-term data gathered at bird observatories can yield more detailed insight into the phenomenon of bird migration and how phenologies relate to climate. Methodological advances are needed, particularly in order to better characterize the shape of phenological distributions and separate between sampling effects and 'true' phenology