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Method for detecting contour of image target object by simulated vision mechanism

A target object, contour detection technology, applied in the field of biological information, can solve the problem of inability to distinguish the target contour and other cluttered boundaries

Inactive Publication Date: 2011-09-14
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

These methods mainly use the brightness difference information in the image for detection, and cannot distinguish the target contour from other cluttered boundaries.
Therefore, it is difficult for these methods to obtain satisfactory results when the contrast changes in the image are relatively large and the background interference is relatively large.

Method used

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  • Method for detecting contour of image target object by simulated vision mechanism
  • Method for detecting contour of image target object by simulated vision mechanism
  • Method for detecting contour of image target object by simulated vision mechanism

Examples

Experimental program
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Effect test

Embodiment 1

[0017] Embodiment 1: Take the contour detection of artificial images as an example:

[0018] This embodiment first constructs an image containing a "Z"-shaped continuous line (i.e. a contour line) embedded in a background of random line segments with random orientations (such as figure 2 a) As an input image, the image size is 512×512 (pixels); the diameter of the small-scale (high-frequency) Gabor filter used is 15 pixels, and the diameter of the large-scale Gabor filter is 60 pixels; non-classical receptive field filtering The outer diameter of the filter is 48 pixels.

[0019] The specific detection method is as follows:

[0020] A. Gabor filter processing: given the small-scale (high-frequency) parameter σ of the Gabor filter f =1.5, take 12 orientations in the range of 180°, and their orientation parameters are respectively iπ / 12, (i=0, 1, ..., 11), and a small scale (high frequency) parameter is obtained as σ f =1.5 and 12 Gabor filters in different orientations; the...

Embodiment 2

[0027] Embodiment 2: Take the contour detection of actual natural images as an example:

[0028] The image of this embodiment is the Hyena (hyena) image and its corresponding standard contour image downloaded from the currently internationally recognized image database website for verifying the effect of the contour extraction method, and the image size is 512 × 512; the small scale (high frequency) used The diameter of the Gabor filter is 19 pixels, the diameter of the large-scale Gabor filter is 95 pixels; the outer diameter of the non-classical receptive field filter is 64 pixels.

[0029] The specific implementation process of this embodiment is as follows:

[0030] A. Gabor filter processing: given the small-scale (high-frequency) parameter σ of the Gabor filter f =2.0, take 12 orientations in the range of 180°, and their orientation parameters are respectively iπ / 12, (i=0, 1, ..., 11), and a small scale (high frequency) parameter is obtained as σ f =2.0 and 12 Gabor fi...

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Abstract

The invention belongs to the technology for detecting a contour of an image target object by adopting a simulated vision mechanism in the bioinformatics technology, which comprises the following steps: determining an azimuth of a filter corresponding to a nonclassical receptive field and restraint quantities of a lateral area and an end area thereof to a central pixel by adopting large and small two scale parameters and performing Gabor filtering in multiple directions, and preparing a restrained image; and performing conventional binarization processing on the restrained image to obtain a target contour plot. In the technology, a Gabor filter bank respectively filters the image in different azimuths under two different scale parameters so as to obtain a high frequency information distributing map and a low frequency information distributing map, a filter of the nonclassical receptive field is utilized to perform restraint processing on non-contour information such as textures, and the like. Therefore, the technology has the characteristics of strong adaptability along with the change of outside input information, capacity of effectively improving the capability of a contour detection system of quickly and accurately extracting the target contour from a complex scene, effect and contour definition, and the like.

Description

technical field [0001] The invention belongs to the image processing technology in the technical field of biological information, in particular to a method for detecting the outline of a target object in an image by using a simulated vision mechanism. This method can be applied to object detection and recognition in computer vision, etc. Background technique [0002] Contour detection is an important part of image processing and computer vision. Correctly detecting (highlighting) object contours from complex backgrounds is a very important and difficult task. Among many traditional image processing methods, the classic methods that are relatively successful in contour detection include Canny operator (Canny JF 1986Acomputational approach to edge detection IEEE Trans.Pattern Anal.Mach.Intell.8 679-698.), active contour Models (Kass M, Witkin A, Terzopoulos D 1987 Snakes: active contour models International Journal of Computer Vision 1321-331.; CasellesV, Kimmel R, Sapiro G ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00G06T5/00G06T7/13
Inventor 李永杰李朝义曾驰杨开富
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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