Peter A. Yoon

Content-based image retrieval using joint correlograms

The comparison of digital images to determine their degree of similarity is one of the fundamental problems of computer vision. Many techniques exist which accomplish this with a certain level of success, most of which involve either the analysis of pixel-level features or the segmentation of images into sub-objects that can be geometrically compared. In this paper we develop and evaluate a new variation of the pixel feature and analysis technique known as the color correlogram in the context of a content-based image retrieval system. Our approach is to extend the autocorrelogram by adding multiple image features in addition to color. We compare the performance of each index scheme with our method for image retrieval on a large database of images. The experiment shows that our proposed method gives a significant improvement over histogram or color correlogram indexing, and it is also memory-efficient.

An algorithm and a stability theory for downdating the ULV decomposition

AbstractAn alternative to performing the singular value decomposition is to factor a matrixA into

An efficient rank detection procedure for modifying the ULV decomposition

A = U\left( {\begin{array}{*{20}c} C \\ 0 \\ \end{array} } \right)V^T

Algebraic DOA estimation and tracking using ULV decomposition

, whereU andV are orthogonal matrices andC is a lower triangular matrix which indicates a separation between two subspaces by the size of its columns. These subspaces are denoted byV = (V1,V2), where the columns ofC are partitioned conformally intoC = (C1,C2) with ‖C2 ‖F ≤ ε. Here ε is some tolerance. In recent years, this has been called the ULV decomposition (ULVD).If the matrixA results from statistical observations, it is often desired to remove old observations, thus deleting a row fromA and its ULVD. In matrix terms, this is called a downdate. A downdating algorithm is proposed that preserves the structure in the downdated matrix

MODIFYING THE SINGULAR VALUE DECOMPOSITION ON THE CONNECTION MACHINE

\bar C

GPU Accelerated Vessel Segmentation Using Laplacian Eigenmaps

to the extent possible. Strong stability results are proven for these algorithms based upon a new perturbation theory.