North Carolina State University

Siemens Aktiengesellschaft

Hamid Krim

Mariappan S Nadar

Qiu Wang

Hasan Ertan Cetingul

Siddharth Roheda

Xiao Bian

Lucas Plaetevoet

Sally Ghanem

An exemplified methods and systems provides a Volterra filter network architecture that employs a cascaded implementation and a plurality of kernels, a set of which is configured to execute an norder filter, wherein the plurality of kernels of the norder filters are repeatedly configured in a plurality of cascading layers of interconnected kernels to form a cascading hierarchical structure that approximates a high-order filter.

Volterra neural network and method

A computer-implemented method of detecting a foreground data in an image sequence using a dual sparse model framework includes creating an image matrix based on a continuous image sequence and initializing three matrices: a background matrix, a foreground matrix, and a coefficient matrix. Next, a subspace recovery process is performed over multiple iterations. This process includes updating the background matrix based on the image matrix and the foreground matrix; minimizing an L&#x2212;1 norm of the coefficient matrix using a first linearized soft-thresholding process; and minimizing an L&#x2212;1 norm of the foreground matrix using a second linearized soft-thresholding process. Then, background images and foreground images are generated based on the background and foreground matrices, respectively.

Robust subspace recovery via dual sparsity pursuit

Growing community of inventors

Raleigh, NC, United States of America

Hamid Krim