Method and device for parallel processing of images
First Claim
1. A method configured for parallel computation for obtaining a first plurality of difference images and a number, n, of subsequent plurality of difference images, said method comprising:
- a. Obtaining a first plurality of difference images from an original image defined by a plurality of pixels, the first plurality of difference images obtained by;
i. Providing a plurality of blurring convolution functions, each of said blurring functions providing increasing degree of blurring of said original image upon convolution of said original image;
ii. Establishing a plurality of difference convolution functions, Dif, by calculating a difference between any two of said blurring convolution functions, each of said two blurring convolution functions providing different degrees of blurring of said original image upon convolution of said original image; and
iii. Calculating a plurality of difference images from said original image, by convolving each of said difference convolution functions, Dif, with said original image to obtain difference images,b. Obtaining a first down sampling anti-aliasing convolution function, DAcon(l), by convolving said plurality of blurring functions with a down sampling function and an anti-aliasing function;
c. When n is larger than 1, obtaining additional down sampling anti-aliasing convolution function(s), DAcon(t), by convolving the previous down sampling antialiasing convolution function, DAcon(t-l), with said plurality of blurring functions, a down sampling function and an anti-aliasing function, beginning this step c with t =2, and repeating this step while increasing t with unitary increments until t reaches n;
d. Convolving all of the down sampling anti-aliasing convolution functions, DAcon, with said original image to obtain an image(x) for each down sampling antialiasing convolution function DAcon(x);
e. Obtaining the n subsequent pluralities of difference images, obtaining each plurality(x) from each image(x),wherein said blurring convolution functions are Gaussian.
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Abstract
The present invention relates to the parallel calculation of convoluted data. In particular, the invention relates to Gaussian pyramid construction and parallel processing of image data, such as parallel calculation of repeatedly convoluted data for use in a SIFT algorithm. This is achieved by providing a method for obtaining a plurality of difference images from an original image defined by a plurality of pixels, said method comprising: Providing a plurality of blurring convolution functions, each of said blurring functions providing increasing degree of blurring of an original image upon convolution of said original image; establishing a plurality of difference convolution functions, Dif, by calculating the difference between two of said blurring convolution functions, each of said two blurring convolution functions providing different degrees of blurring of an original image upon convolution of said original image; and calculating a plurality of difference images from said original image, by convolving each of said difference convolution functions, Dif, with said original image to obtain difference images.
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Citations
10 Claims
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1. A method configured for parallel computation for obtaining a first plurality of difference images and a number, n, of subsequent plurality of difference images, said method comprising:
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a. Obtaining a first plurality of difference images from an original image defined by a plurality of pixels, the first plurality of difference images obtained by; i. Providing a plurality of blurring convolution functions, each of said blurring functions providing increasing degree of blurring of said original image upon convolution of said original image; ii. Establishing a plurality of difference convolution functions, Dif, by calculating a difference between any two of said blurring convolution functions, each of said two blurring convolution functions providing different degrees of blurring of said original image upon convolution of said original image; and iii. Calculating a plurality of difference images from said original image, by convolving each of said difference convolution functions, Dif, with said original image to obtain difference images, b. Obtaining a first down sampling anti-aliasing convolution function, DAcon(l), by convolving said plurality of blurring functions with a down sampling function and an anti-aliasing function; c. When n is larger than 1, obtaining additional down sampling anti-aliasing convolution function(s), DAcon(t), by convolving the previous down sampling antialiasing convolution function, DAcon(t-l), with said plurality of blurring functions, a down sampling function and an anti-aliasing function, beginning this step c with t =2, and repeating this step while increasing t with unitary increments until t reaches n; d. Convolving all of the down sampling anti-aliasing convolution functions, DAcon, with said original image to obtain an image(x) for each down sampling antialiasing convolution function DAcon(x); e. Obtaining the n subsequent pluralities of difference images, obtaining each plurality(x) from each image(x), wherein said blurring convolution functions are Gaussian. - View Dependent Claims (2, 3, 4, 5, 10)
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6. An image processing device configured for parallel computation for obtaining a plurality of difference images from an original image defined by a plurality of pixels, said device comprising means for receiving data in respect of said first image, means for transmitting or storing said difference images, and
a processor circuit, configured to: -
a. Obtain said a first plurality of difference images from said original, the first plurality of difference images obtained by; i. Provide a plurality of blurring convolution functions, each of said blurring functions providing increasing degree of blurring of said original image upon convolution of said original image; ii. Establish a plurality of difference convolution functions, Dif, by calculating a difference between any two of said blurring convolution functions, each of said two blurring convolution functions providing different degrees of blurring of said original image upon convolution of said original image; and iii. Calculate a plurality of difference images from said original image, by convolving each of said difference convolution functions, Dif, with said original image to obtain difference images; b. Obtain a first down sampling anti-aliasing convolution function, DAcon(l), by convolving said plurality of blurring functions with a down sampling function and an anti-aliasing function; c. When n is larger than 1, obtaining additional down sampling anti-aliasing convolution function(s), DAcon(t), by convolving the previous down sampling antialiasing convolution function, DAcon(t-l), with said plurality of blurring functions, a down sampling function and an anti-aliasing function, beginning this step c with t =2, and repeating this step while increasing t with unitary increments until t reaches n; d. Convolve all of the down sampling anti-aliasing convolution functions, DAcon, with said original image to obtain an image(x) for each down sampling antialiasing convolution function DAcon(x); e. Obtain the n subsequent pluralities of difference images, obtaining each plurality(x) from each image(x), wherein said blurring convolution functions are Gaussian. - View Dependent Claims (7, 8, 9)
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Specification