scholarly journals An Improved Automatic Shape Feature Extraction Method Based on Template Matching

2021 ◽  
Vol 2095 (1) ◽  
pp. 012053
Author(s):  
Xiaoqi Wang ◽  
Jian Zhang
Keyword(s):  
Feature Extraction ◽  
Template Matching ◽  
Extraction Method ◽  
Traditional Approach ◽  
Color Space ◽  
Shape Feature ◽  
Feature Extraction Method ◽  
Noise Interference ◽  
Image Shape ◽  
Shape Feature Extraction

Abstract Image shape extraction is an important step in the image analysis, AI electronic industry and automation, as well as a significant part of content-based image retrieval(CBIR), which cannot be separated from contour extraction. However, traditional approach of the border following algorithm is susceptible to noise interference, thus the shape extracted is always complex in real images and cannot express feature of the target image well. Therefore, an improved shape feature extraction method is proposed, which converts color space into HSV model when preprocessing, filters contour by area size, merges adjacent contours by drawing convex hull and filters with template shapes. Lastly, this method is tested on UAV123 and YCB_Video dataset, which showed that the proportion of valid contour improved from less than 10% to 87.7% based on border following algorithm. In the experiment of OPenCV open source library in Visual Studio environment, we hope to improve the extraction efficiency of shape features.

Texture feature extraction method for scale and rotation invariant image retrieval

2012 ◽  
Vol 48 (11) ◽  
pp. 626 ◽  
Author(s):  
M.H. Rahman ◽  
M.R. Pickering ◽  
M.R. Frater ◽  
D. Kerr
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Extraction Method ◽  
Texture Feature ◽  
Texture Feature Extraction ◽  
Rotation Invariant ◽  
Feature Extraction Method

scholarly journals An Image Similarity Invariant Feature Extraction Method Based on Radon Transform

2021 ◽  
Vol 15 ◽  
pp. 288-296
Author(s):  
Hongjun Guo ◽  
Lili Chen
Keyword(s):  
Feature Extraction ◽  
Image Recognition ◽  
Radon Transform ◽  
Extraction Method ◽  
Image Similarity ◽  
Shape Feature ◽  
Feature Extraction Method ◽  
Dimensional Image ◽  
Invariant Feature ◽  
Similarity Invariant

With the advancements of computer technology, image recognition technology has been more and more widely applied and feature extraction is a core problem of image recognition. In study, image recognition classifies the processed image and identifies the category it belongs to. By selecting the feature to be extracted, it measures the necessary parameters and classifies according to the result. For better recognition, it needs to conduct structural analysis and image description of the entire image and enhance image understanding through multi-object structural relationship. The essence of Radon transform is to reconstruct the original N-dimensional image in N-dimensional space according to the N-1 dimensional projection data of N-dimensional image in different directions. The Radon transform of image is to extract the feature in the transform domain and map the image space to the parameter space. This paper study the inverse problem of Radon transform of the upper semicircular curve with compact support and continuous in the support. When the center and radius of a circular curve change in a certain range, the inversion problem is unique when the Radon transform along the upper semicircle curve is known. In order to further improve the robustness and discrimination of the features extracted, given the image translation or proportional scaling and the removal of impact caused by translation and proportion, this paper has proposed an image similarity invariant feature extraction method based on Radon transform, constructed Radon moment invariant and shown the description capacity of shape feature extraction method on shape feature by getting intra-class ratio. The experiment result has shown that the method of this paper has overcome the flaws of cracks, overlapping, fuzziness and fake edges which exist when extracting features alone, it can accurately extract the corners of the digital image and has good robustness to noise. It has effectively improved the accuracy and continuity of complex image feature extraction.

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