Multiscale Object Features From Clustered Complex Wavelet

This paper introduces a method by which intuitive feature entities can be created from ILP coefficients. The ILP transform is a pyramid of decimated complex-valued coefficients at multiple scales, derived from dual-tree complex wavelets, whose phases indicate the presence of different feature types (edges and ridges). We use an Expectation-Maximization algorithm to cluster large ILP coefficients that are spatially adjacent and similar in phase. We then demonstrate the relationship that these clusters possess with respect to observable image content, and conclude with a look at potential applications of these clusters, such as rotation- and scaleinvariant object recognition

MODDEMEIJER: AN EFFICIENT ALGORITHM FOR SELECTING OPTIMAL CONFIGURATIONS OF AR-COEFFICIENTS 1

An efficient algorithm for selecting optima

Video Structuring, Indexing and Retrieval Based on Global Motion Wavelet

This paper describes an approach for video structuring and indexing. It relies on motion wavelet coefficients directly estimated from image sequence. These coefficients provide a multiscale characterization of optical flow. They allow to define dominant and local motion descriptors, respectively related to camera and object displacements. We use dominant motion descriptors to perform a temporal segmentation of the sequence. Shots extracted are characterized in term of dominant motion properties and indexed by using descriptors related to local motion content. These operations allow to retrieve shots, by example queries, according to only dynamic content of the scene and not camera displacements