Clustering is the problem of identifying the distribution of patterns and intrinsic correlations in large data sets by partitioning the data points into similarity classes. This p...
We introduce a method to discover optimal local patterns, which concisely describe the main trends in a time series. Our approach examines the time series at multiple time scales ...
We describe a general method for building cascade classifiers from part-based deformable models such as pictorial structures. We focus primarily on the case of star-structured mod...
Pedro Felzenszwalb, Ross Girshick, David McAlleste...
Image matching has been a central research topic in computer vision over the last decades. Typical approaches to correspondence involve matching features between images. In this pa...
—We present the Hermite radial basis function (HRBF) implicits method to compute a global implicit function which interpolates scattered multivariate Hermite data (unstructured p...