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Fingerprint image frame sequence combination method based on wave form match

A fingerprint image and image frame technology, applied in the field of image processing process control

Inactive Publication Date: 2007-12-12
BEIHANG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Its disadvantage is that each image frame needs to be Fourier transformed to achieve stitching
Its disadvantage is that the gray variance needs to be calculated for each image frame to achieve splicing

Method used

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  • Fingerprint image frame sequence combination method based on wave form match
  • Fingerprint image frame sequence combination method based on wave form match

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0048] The image splicing process in this embodiment will be described in detail below in conjunction with FIGS. 1 , 2 , and 3 .

[0049] First of all, it is assumed that the image size of each frame is 16 rows, 192 columns, and the gray level is 0-255. The maximum size of the image after normal splicing is 192 rows, 192 columns. A total of 3 reference area extraction templates are used, which extract the 65th to 128th columns, the 1st to 64th columns and the 129th to 192nd columns in the 1st to 14th rows of the reduced image frame respectively.

[0050] After receiving the first image frame, it is reduced, and the remaining part is stitched into the bottom of the stitched image. Select a reference area extraction template to extract the benchmark reference area, and calculate the binarized gray threshold as the average gray value of the reference area minus 16. Binarize the benchmark reference area according to this threshold, convert each image line into a binary sequence, ...

Embodiment 2

[0056] The image splicing process in this embodiment will be described in detail below in conjunction with FIGS. 1 , 2 , and 3 .

[0057] First of all, it is assumed that the image size of each frame is 16 rows, 192 columns, and the gray level is 0-255. The maximum size of the image after normal splicing is 192 rows, 192 columns. A total of 3 reference area extraction templates are used, which extract the 73rd to 120th columns, the 25th to 72nd columns and the 121st to 168th columns in the 1st to 14th rows of the reduced image frame respectively.

[0058] After receiving the first image frame, it is reduced, and the remaining part is stitched into the bottom of the stitched image. Select a reference area extraction template to extract the benchmark reference area, and calculate the binarized gray threshold as the average gray value of the reference area minus 16. Binarize the benchmark reference area according to this threshold, convert each image line into a binary sequence,...

Embodiment 3

[0064] The image splicing process in this embodiment will be described in detail below in conjunction with FIGS. 1 , 2 , and 3 .

[0065] First of all, it is assumed that the image size of each frame is 16 rows, 192 columns, and the gray level is 0-255. The maximum size of the image after normal splicing is 192 rows, 192 columns. A total of 3 reference area extraction templates are used to extract the 81st to 112th columns, the 49th to 80th columns and the 113th to 144th columns in the 1st to 14th rows of the reduced image frame respectively.

[0066] After receiving the first image frame, it is reduced, and the remaining part is stitched into the bottom of the stitched image. Select a reference area extraction template to extract the benchmark reference area, and calculate the binarized gray threshold as the average gray value of the reference area minus 16. Binarize the benchmark reference area according to this threshold, convert each image line into a binary sequence, and...

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Abstract

The invention relates to a finger print image jointing method for image processing control. For each picture, it only selects part of it as the reference area to get the grey binary wave and to extract the wave jumping information of the current picture with the jointed image overlapping area to realize the jointing. It has small amount of computation, simple in realization, quick to complete the jointing of finger print image, applicable for all kinds of scrape finger print image collection module.

Description

technical field [0001] The invention relates to an image processing process control method, in particular to a splicing method of fingerprint image frames. Background technique [0002] Now the automatic fingerprint identification system is more and more widely used, such as mobile phones and computers, especially when the automatic fingerprint identification system is applied to mobile phones, it is necessary to use a small fingerprint sensor, otherwise it will affect the appearance layout and volume of the mobile phone. In terms of product cost, a small fingerprint sensor requires a smaller integrated circuit chip area and lower cost. Therefore, the scratch sensor has been widely used. [0003] The working principle of the scratch-type fingerprint sensor is: collect many frames of fingerprint images, and there is a certain overlapping area between adjacent frames. It is necessary to stitch each frame of images into a complete fingerprint image through image stitching. Th...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00
Inventor 王朋张有光
Owner BEIHANG UNIV
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