Alan Conrad Bovik

8164039, Apr 24, 2012

Apr 30, 2009

12/433883

Alan C. Bovik - Austin TX, US
Mia K. Markey - Austin TX, US
Mehul Sampat - Sunnyvale CA, US

Board of Regents, The University of Texas System - Austin TX

H01L 27/00

2502081, 250214 R

A method and apparatus detects one or more spiculated masses in an image using a processor. The image is received in the processor. The received image is filtered using one or more Gaussian filters to detect one or more central mass regions. The received image is also filtered using one or more spiculated lesion filters to detect where the one or more spiculated masses converge. In addition, the received image is filtered using one or more Difference-of-Gaussian filters to suppress one or more linear structures. An enhanced image showing the detected spiculated masses is created by combining an output from all of the filtering steps. The enhanced image is then provided to an output of the processor.

8457414, Jun 4, 2013

Dec 7, 2009

12/632389

Sina Jahanbin - Austin TX, US
Alan C. Bovik - Austin TX, US
Eduardo Perez - Cedar Park TX, US
Dinesh Nair - Austin TX, US

National Instruments Corporation - Austin TX
Board of Regents of the University of Texas System - Austin TX

G06T 9/62

382224

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.

20110274361, Nov 10, 2011

May 10, 2011

13/104801

Alan Bovik - Austin TX, US
Anush Moorthy - Austin TX, US

Board of Regents, The University of Texas System - Austin TX

G06K 9/62

382224

Techniques and structures are disclosed in which one or more distortion categories are identified for an image or video, and a quality of the image or video is determined based on the one or more distortion categories. The image or video may be of a natural scene, and may be of unknown provenance. Identifying a distortion category and/or determining a quality may be performed without any corresponding reference (e.g., undistorted) image or video. Identifying a distortion category may be performed using a distortion classifier. Quality may be determined with respect to a plurality of human opinion scores that correspond to a particular distortion category to which an image or video of unknown provenance is identified as belonging. Various statistical methods may be used in performing said identifying and said determining, including use of generalized Gaussian distribution density models and natural scene statistics.

52822559, Jan 25, 1994

Jun 17, 1991

7/717214

Alan C. Bovik - Buda TX
Dapang Chen - Austin TX

Board of Regents, The University of Texas System - Austin TX

G06K 936
G06K 946
H04N 712

382 56

An improved method for coding and decoding still or moving visual pattern images by partitioning images into blocks or cubes, respectively, and coding each image separately according to visually significant responses of the human eye. Coding is achieved by calculating and subtracting a mean intensity value from digital numbers within each block or cube and detecting visually perceivable edge locations within the resultant residual sub-image. If a visually perceivable edge is contained within the block or cube, gradient magnitude and orientation at opposing sides of the edge within each edge block or cube are calculated and appropriately coded. If no perceivable edge is contained within the block or cube, the sub-image is coded as a uniform intensity block. Decoding requires receiving coded mean intensity value, gradient magnitude and pattern code, and then decoding a combination of these three indicia to be arranged in an orientation substantially similar to the original digital image or original sequence of digital images. Coding and decoding can be accomplished in a hierarchical pattern.

20180286032, Oct 4, 2018

Mar 8, 2018

15/916021

- Austin TX, US
Alan Bovik - Austin TX, US

G06T 7/00

A method, system and computer program product for assessing quality of images or videos. A quality assessment of an image or video to be processed is performed using a no-reference reference quality assessment algorithm. A quality measurement, such as a score, reflecting the quality of the image or video, is generated from the no-reference reference quality assessment algorithm. The image or video is then processed and a quality assessment of the processed image or video is performed using a reference quality assessment algorithm that is conditional on the quality measurement provided by the no-reference quality assessment algorithm. In this manner, a more accurate quality measurement of the image or video is provided by the reference quality assessment algorithm.