Method and apparatus for locating image edge in natural background

Abstract

The invention provides a method and an apparatus for locating an image edge in a natural background. The method comprises the following steps of: for an image in the natural background, extracting a central color feature as a comparison standard; comparing with the central color feature, and extracting a plurality of to-be-selected points for each edge of the image according to the comparison result; grouping the plurality of to-be-selected points corresponding to the edge according to distance and / or direction to obtain a plurality of to-be-selected point groups; fitting a corresponding fitted line with the to-be-selected points in each to-be-selected point group; selecting the fitted line with the maximum number of the to-be-selected points and close to the edge, and locating the selected line as the edge. With the method provided by the invention, calculation load of an executive body is reduced, speed of locating the edge of the image is improved, interference of edges in the natural background is avoided, and accuracy of locating of the edge of the image is improved.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a method and device for locating the edge of an image in a natural background. Background technique [0002] In actual life and work, it is necessary to obtain the information in some framed images in pictures or videos, for example, information on business cards or information on labels, etc. When obtaining such information, the terminal device needs to first The edge of the image is located, that is, its edge is detected. [0003] Traditional image edge detection methods generally use Hough transform or saliency detection. [0004] The Hough transform uses the point-line duality of the image space and the Hough parameter space to convert the detection problem in the image space to the parameter space. Using this method for image edge detection requires mapping each point into a surface in the parameter space, which is a one-to-many mapping, so the amount of calculation is l...

AI Technical Summary

Problems solved by technology

Using this method for image edge detection requires mapping each point into a surface in the parameter space, which is a one-to-many mapping, so the amount of calculation is large, resulting in a slow detection rate, and when the image is disturbed by external noise, the signal-to-noise is relatively low, at this time the performance of the conventional Hough transform will drop sharply, resulting in low edge accuracy of the acquired image

[0005] If the saliency detection method is used, it is more dependent on the consistency of the background, which makes the background interference too large, and at the same time it is easy to ignore the information of the image edge itself, resulting in low accuracy of the acquired image edge

[0006] At present, there is no more accurate positioning method for image edges.

Images