System and Method For Feature Detection In Image Sequences
A method for processing image data includes inputting image data, determining a plurality of quadrature filter pairs based on filter parameter values to detect features of interest in the image data, applying the quadrature filter pairs to the image data to obtain a set of filter responses, and processing the filter responses to obtain the features of interest in the image data.
1 . A method for processing image data, comprising:
inputting image data;
determining a plurality of quadrature filter pairs based on fitter parameter values to detect features of interest in the image data;
applying the quadrature filter pairs to the image data to obtain a set of filter responses; and
processing the filter responses to obtain the features of interest in the image data.
2 . The method of claim 1 , wherein a log-Gabor function is used to specify a frequency response of the quadrature filter pairs.
3 . The method of claim 1 , further comprising determining the filter parameters values using binarized features of interest that serve as a groundtruth.
4 . The method of claim 3 , wherein a plurality of the filter parameters are tested using a downhill search algorithm to determine a parameter set that can generate results close to the groundtruth.
5 . The method of claim 1 , wherein processing the filter responses for each of the quadrature filter pairs comprises:
performing an application-specific non-linear operation for each filter response; and
generating illumination and contrast invariant measures after integrating and scaling the filter responses over different orientations.
6 . The method of claim 5 , wherein performing the application-specific non-linear operation includes estimating a threshold using a magnitude histogram of each filter response.
7 . The method of claim 5 , wherein scaling the filter responses over different orientations comprises normalizing a summation of thresholded outputs using a summation of a magnitude of the filter response for each of the quadrature filter pairs.
8 . An image data processing system, comprising:
a memory device for storing a program;
a processor in communication with the memory device, the processor operative with the program to:
input image data;
determine a plurality of quadrature filter pairs based on the values of filter parameters to detect features of interest in the image data;
apply the quadrature filter pairs to the image data to obtain a filter response for each of the quadrature filter pairs; and
process the filter responses to obtain the features of interest in the image data.
9 . The image data processing system of claim 8 , wherein a log-Gabor function is used to specify a frequency response of the quadrature filter pairs.
10 . The image data processing system of claim 8 , wherein when processing the filter responses, the processor is further operative with the program to:
perform an application-specific non-linear operation for each filter response; and
generating illumination and contrast invariant measures after integrating and scaling the filter responses over different orientations.
11 . The image data processing system of claim 10 , wherein when performing the application-specific non-linear operation, the processor is further operative with the program to estimate a threshold using a magnitude histogram of each filter response.
12 . The image data processing system of claim 10 , wherein when scaling the filter responses over different orientations the processor is further operative with the program to normalize a summation of thresholded outputs using a summation of a magnitude of the filter response for each of the quadrature filter pairs.
13 . A computer-implemented method of detecting features in image data, comprising:
inputting image data;
determining a plurality of quadrature filter pairs based on filter parameters values to detect features of interest in the image data;
applying the quadrature filter pairs to the image data to obtain a filter response for each of the quadrature filter pairs;
offsetting each filter response by a predetermined value;
applying soft thresholding to each filter response to obtain thresholded outputs;
summing the filter responses over different orientations; and
normalizing a summation of the thresholded outputs using a summation of a magnitude of the filter response for each of the quadrature filter pairs to obtain the features of interest.
14 . The computer-implemented method of claim 13 , wherein the predetermined value is a negative number, a positive number, or zero based on application specific characteristics.
15 . The computer-implemented method of claim 14 , wherein when the features of interest have lower intensities than a background, the predetermined value is a negative number.
16 . The computer-implemented method of claim 14 , wherein when the features of interest have higher intensities than a background, the predetermined value is a positive number.
17 . The computer-implemented method of claim 14 , wherein when there is no preference over dark features or bright features, the predetermined value is zero.
18 . The computer-implemented method of claim 13 , wherein soft thresholding includes estimation of noise characteristics using a high frequency component of the image.