Intelligent systems and methods for processing image data based upon anticipated regions of visual interest

• US 6,389,169 B1
• Filed: 06/08/1998
• Issued: 05/14/2002
• Est. Priority Date: 06/08/1998
• Status: Expired due to Fees

• Current Assignee: Claudio M. Privitera, Lawrence W. Stark
• Original Assignee: Claudio M. Privitera, Lawrence W. Stark
• Inventors: Stark, Lawrence W., Privitera, Claudio M.
• Primary Examiner(s): Au, Amelia M.
• Assistant Examiner(s): Dastouri, Mehrdad
• Application Number:
  US09/093,743
• Time in Patent Office: 1,436 Days
• Field of Search: 382/225, 382/190, 382/195, 382/276, 382/278, 382/280, 382/128, 382/132, 382/250, 364/728.01, 364/728.02, 364/726.01, 364/726.02, 364/726.03, 708/403, 708/404, 708/405, 708/420, 708/421
• US Class Current: 382/225
• CPC Class Codes: G06T 2207/20052 Discrete cosine transform [DCT]
• CPC Class Codes: G06T 2207/20064 Wavelet transform [DWT]
• CPC Class Codes: G06T 2207/20164 Salient point detection; Corner detection
• CPC Class Codes: G06T 5/00 Image enhancement or restoration
• CPC Class Codes: H04N 19/14 Coding unit complexity, e.g. amount of activity or edge presence estimation H04N19/146 takes precedence
• CPC Class Codes: H04N 19/162 User input
• CPC Class Codes: H04N 19/17 the unit being an image region, e.g. an object
• CPC Class Codes: H04N 19/60 using transform coding
• CPC Class Codes: H04N 19/63 using sub-band based transform, e.g. wavelets

Abstract

Systems and methods for performing intelligent image processing. Image processing systems and methods in accordance with the present invention may select algorithms for processing collections of images by comparing algorithmic region of interest (aROI) data to stored human visual region of interest (hROI) data to select from a database of available transformation algorithms an optimal algorithm or group of algorithms to be used in transforming data comprising the collection or collections of images. The selected algorithm(s) may then be used, for example, in data compression, image enhancement or database query functions.

Claims

1. A method for processing image data, said method comprising the steps of:

establishing for at least one image type a correlation between a plurality of algorithmic regions of interest developed by a respective plurality of transformation algorithms and at least one set of human visual regions of interest, wherein the one set of human visual regions of interest comprises a plurality of human fixation loci, the locations of said loci being determined by monitoring the amount of time that a human eye focuses on a particular loci;

selecting a transformation algorithm for processing data representative of an image based upon an image type and said correlation; and

transforming said image data from a first domain to a second domain using said selected transformation algorithm.

2. The method of claim 1, wherein the plurality of transformation algorithms comprises a Michaelson contrast.

3. The method of claim 1, wherein the plurality of algorithms comprises a discrete cosine transform.

4. The method of claim 1, wherein the plurality of algorithms comprises a discrete wavelet transform.

5. A method for selecting image transformation functions for image processing applications, said method comprising the steps of:

storing within a memory at least one data set descriptive of human visual regions of interest for at least one type of image, wherein said data set comprises a plurality of human fixation loci, the locations of said loci being determined by monitoring the amount of time that a human eye focuses on a particular loci;

applying a plurality of image transformation functions to an image corresponding to said at least one type of image to derive a plurality of respective data sets comprising local maxima;

applying clustering functions to said plurality of respective data sets comprising said local maxima to derive a plurality of respective data sets comprising algorithmic regions of interest within said image; and

comparing said respective data sets comprising said algorithmic regions of interest to said at least one data set descriptive of said human visual regions of interest to select a transformation function for processing additional images corresponding to said at least one type of image.

6. The method of claim 5, wherein the plurality of image transformation functions comprises a Michaelson contrast.

7. The method of claim 5, wherein the plurality of image transformation functions comprises a discrete cosine transform.

8. The method of claim 5, wherein the plurality of image transformation functions comprises a discrete wavelet transform.