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A feature extraction and recognition method of image rstn invariant attributes based on bionic visual transformation

An attribute feature, bio-mimicry technology, applied in the intersection of biological information and machine vision technology, can solve problems such as insufficient robustness and noise sensitivity

Active Publication Date: 2019-01-29
CENT SOUTH UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, there are two deficiencies. First, the model uses a box filter, which is implemented by using the weighted average value of pixels around the image, which is not consistent with the human visual perception mechanism. Therefore, it is particularly sensitive to noise.
Secondly, the edge detector of the black and white filter recognizes simple structural targets (such as letter I or number 1, etc.), due to the lack of edge features, the robustness is insufficient after adding noise

Method used

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  • A feature extraction and recognition method of image rstn invariant attributes based on bionic visual transformation
  • A feature extraction and recognition method of image rstn invariant attributes based on bionic visual transformation
  • A feature extraction and recognition method of image rstn invariant attributes based on bionic visual transformation

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Effect test

Embodiment 1

[0092] This example is for images with 26 letters and 10 numbers, such as figure 1 As shown, its invariant attribute feature extraction process is carried out in the following five steps:

[0093] Step 1: Perform grayscale processing on the original image, and normalize its grayscale value to be between [0, 1]. And use the bilinear interpolation method to reset the image size to 128×128.

[0094] Step 2: The two-dimensional image M(x, y) is obtained through the preprocessing of step 1, and the intermediate response G(x, y) is obtained by Gabor filtering, and then the horizontal-vertical bipolar filters F and G(x, y) are used. Do convolution. That is, the filter-filter filter based on Gabor and the bipolar filter F detects the direction edge of the target image, and obtains the edge image E.

[0095] Step 3: For the edge image E, measure the spatial resolution of the image lines in different edge directions θ and different distances I. First, dislocation processing is perf...

Embodiment 2

[0163] In order to verify the RSTN invariance of extracted image features, the original images of G and F letters are rotated, scaled, translated and noised to different degrees. For the visual comparison of the results, the output results of the first stage and the second stage are visualized in the form of images. like Figure 7 and Figure 8 (a) is the original image, where Figure 7 and Figure 8 (b) is the output result of (a) first-stage transformation, Figure 7 and Figure 8 (c) is the output feature map of the second stage of (a). Then, will Figure 7 and Figure 8 (a) is rotated 135° counterclockwise, such as Figure 7 and Figure 8 As shown in (d), the output of the first stage is obtained Figure 7 and Figure 8 (e). compare Figure 7 and Figure 8 In terms of (b), it is equivalent to a horizontal translation of 45° to the right. However, the second stage features Figure 7 and Figure 8 (f) of Figure 7 and Figure 8 As far as (c) is concerned, ...

Embodiment 3

[0170] In the process of traffic sign recognition in natural scenes, the image is easily disturbed by factors such as illumination, distance, and camera angle. Usually, the distance between the camera and the traffic sign cannot be obtained accurately, and the size of the traffic sign in the image is also difficult to uniformly determine. For this reason, the robustness of traffic sign feature extraction is insufficient, which constrains the performance of traffic sign recognition. Therefore, applying this method to the feature extraction of traffic sign recognition and extracting the invariant attribute features in the process of traffic sign recognition is of great significance for improving its recognition rate and robustness.

[0171] Figure 9 The first column shows 5 traffic signs with different sizes and rotation angles, which respectively indicate no left, straight or left. The rings and arrows in these two types of signs are in prominent positions. And it has conne...

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Abstract

The invention discloses an image RSTN invariant attribute feature extraction and recognition method based on biological imitation visual transformation, comprising the following steps: 1) performing grayscale processing on the original image, and resetting the image size by using a bilinear interpolation method ; 2) based on the filter-filter filter of Gabor and bipolar filter F, detect the direction edge of the target image, and obtain the edge image E; 3) calculate the spatial resolution distance detection value of the edge image E, and obtain the first stage output image S1; 4) Take the output image S1 of the first stage, and then perform the direction edge detection in step 2, and the spatial resolution distance detection in step 3 to obtain the feature output image S2 in the second stage, and obtain the invariant attribute features. This method simulates human The visual perception mechanism, ingeniously combined with the RSTN invariant attribute characteristics of bionic visual transformation, improves the accuracy of image recognition and enhances the robustness to noise.

Description

technical field [0001] The invention belongs to the cross field of biological information and machine vision technology, and particularly relates to an image RSTN invariant attribute feature extraction and recognition method based on biomimetic visual transformation. Background technique [0002] Image invariant attribute feature extraction is an important means to improve the target recognition rate. It is well known that human vision can accurately perceive rotated, scaled, translated and noised images. However, using traditional computer vision algorithms to achieve object recognition of rotated, scaled, translated and noisy images is an extremely challenging task. With the continuous disclosure of the response mechanism of human visual cerebral cortex, Hubel reported in Nature that biological visual cortex cells respond very strongly to lines of certain lengths or directions. Inspired by this biological visual response mechanism, if machine vision can extract the line ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/46
CPCG06V10/443
Inventor 周开军余伶俐
Owner CENT SOUTH UNIV
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