National Instruments Corporation

University of Texas System

Eduardo Perez

Dinesh R Nair

Alan C Bovik

Sina Jahanbin

Method for detecting textural defects in an image. The image, which may have an irregular visual texture, may be received. The image may be decomposed into a plurality of subbands. The image may be portioned into a plurality of partitions. A plurality of grey-level co-occurrence matrices (GLCMs) may be determined for each partition. A plurality of second-order statistical attributes may be extracted for each GLCM. A feature vector may be constructed for each partition, where the feature vector includes the second order statistical attributes for each GLCM for the partition. Each partition may be classified based on the feature vector for the respective partition. Classification of the partitions may utilize a one-class support vector machine, and may determine if a defect is present in the image.

Detection of textural defects using a one class support vector machine

A system and method for estimating a signal based on a stream of randomly generated samples. The method includes: (a) receiving a sample; (b) generating a sampling vector; (c) multiplying the sample and the sampling vector to obtain a current back projection; (d) computing a first intermediate vector that represents an average of the current back projection and previous back projections; (e) transforming the first intermediate vector to determine a second intermediate vector; (f) identifying locations where the second intermediate vector attains its k largest values; (g) computing an estimate for the transformation of the signal by solving a system of equations based on the identified locations, the received sample value, previously received sample values, the sampling vector and previously generated sampling vectors; (h) inverse transforming the transformation estimate to determine an estimate of the signal; and (i) storing the signal estimate.

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Cedar Park, TX, United States of America

Eduardo Perez