Position-invariant, rotation-invariant, and scale-invariant process for binary image recognition

1997 ◽  
Vol 36 (14) ◽  
pp. 3035 ◽  
Author(s):  
J. Levkovitz ◽  
E. Oron ◽  
M. Tur
Keyword(s):  
Image Recognition ◽  
Binary Image ◽  
Rotation Invariant ◽  
Scale Invariant ◽  
Invariant Process

Based the Morphological Filtering BP Algorithm of SAR Image Recognition

2013 ◽  
Vol 760-762 ◽  
pp. 1486-1490
Author(s):  
Ding Ding Jiang ◽  
De Rong Cai ◽  
Qiang Wei
Keyword(s):  
Image Recognition ◽  
Learning Algorithm ◽  
Image Interpretation ◽  
Practical Significance ◽  
Sar Image ◽  
Detection Capability ◽  
Rotation Invariant ◽  
Sar Images ◽  
Diverse Background ◽  
Morphological Filtering

SAR image recognition is an important content of of aviation image interpretation work. In this paper, the characteristics of SAR images a practical significance of morphological filtering neural network model and its adaptive BP learning algorithm. As can be seen through the experimental results, the algorithm can not only adapt to the complex and diverse background environment, and has a displacement of the same continuous moving target detection capability, telescopic invariant and rotation invariant features.

scholarly journals Cement Pavement Surface Crack Detection Based on Image Processing

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Hongwei Lei ◽  
Jianlian Cheng ◽  
Qi Xu
Keyword(s):  
Image Processing ◽  
Laplace Operator ◽  
Image Recognition ◽  
Surface Crack ◽  
Iteration Method ◽  
Crack Detection ◽  
Binary Image ◽  
Threshold Method ◽  
Otsu Method ◽  
Pavement Crack Detection

This article introduces the application of image recognition technology in cement pavement crack detection and put forward to method for determining threshold about grayscale stretching. the algorithm is designed about binarization which has a self-adaptive characteristic. After the image is preprocessed, we apply 2D Wavelet and Laplace operator to process the image. According to the characteristic of pixel of gray image, an algorithm designed on binarization for Binary image. The feasibility of this method can be verified the image processed by comparing with the results of three algorithms: Otsu method, iteration method and fixed threshold method.

scholarly journals Learning How to Extract Rotation-Invariant and Scale-Invariant Features from Texture Images

2008 ◽  
Vol 2008 (1) ◽  
Author(s):  
Javier A. Montoya-Zegarra ◽  
João Paulo Papa ◽  
Neucimar J. Leite ◽  
Ricardo da Silva Torres ◽  
Alexandre Falcão
Keyword(s):  
Rotation Invariant ◽  
Scale Invariant ◽  
Invariant Features

Binary Image Recognition Utilizing Computer Generated Templates

2017 ◽  
Author(s):  
Dustin Bielecki ◽  
Prakhar Jaiswal ◽  
Rahul Rai
Keyword(s):  
Image Recognition ◽  
Hausdorff Distance ◽  
Binary Image ◽  
Sorting Technique ◽  
Milling Operation ◽  
Modified Hausdorff Distance ◽  
Image Templates

This paper covers a method of taking images of physical parts which are then preprocessed and compared against CAD generated templates. A pseudo milling operation was performed on discretized points along CAD generated mill paths to create binary image templates. The computer-generated images were then tested against one another as a preliminarily sorting technique. This was done to reduce the number of sorting approaches used, by selecting the most reliable and discerning ones, and discarding the others. To apply the selected sorting methods for comparing CAD generated images and the images of physical parts, a translational and scaling normalization technique was implemented. Rotational variation occurs while scanning physical parts and it was addressed using two different techniques: first by determination of best rotation based on modified-Hausdorff distance (MHD); and second by comparing against all CAD based images for all template rotations. The proposed approach for automated sorting of physical parts was demonstrated by categorizing multiple geometries.

scholarly journals Image recognition of individual cow based on SIFT in Lαβ color space

2018 ◽  
Vol 176 ◽  
pp. 01023
Author(s):  
Feng Lv ◽  
Chunmei ZHANG ◽  
Changwei Lv
Keyword(s):  
Image Recognition ◽  
Individual Recognition ◽  
Low Cost ◽  
Color Space ◽  
Strong Stability ◽  
Identification Accuracy ◽  
Scale Invariant ◽  
Biological Features ◽  
Electronic Tag ◽  
Scale Invariant Feature

Using image recognition technology to identify individual dairy cattle with her biological features shows strong stability. This kind of non-contact, high precision and low cost individual recognition methods based on image processing are more and more popular recently to replace the electronic tag and ear mark which can hurt the cattle’s psychology and physical health and can affect cattle’s behavior. By comparing the various color space transformations, he proposed a scale-invariant feature transform algorithm based on the Luminace of Lαβ color space. With this algorithm, a biological features recognition and management system of Holstein cow has been developed. The identification accuracy is higher than 98%, which is the best result than all the similar reports for cows’ identification.

scholarly journals A Robust Model Using SIFT and Gamma Mixture Model for Texture Images Classification: Perspectives for Medical Applications

2020 ◽  
Vol 13 (4) ◽  
pp. 1659-1669
Author(s):  
Said Benlakhdar ◽  
Mohammed Rziza ◽  
Rachid Oulad Haj Thami
Keyword(s):  
Mixture Model ◽  
Medical Images ◽  
Rotation Invariant ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Medical Issues ◽  
Robust Model ◽  
Novel Approach ◽  
Benchmark Datasets ◽  
Scale Invariant Feature

The texture analysis of medical images is a powerful calculation tool for the discrimination between pathological and healthy tissue in different organs in medical images. Our paper proposes a novel approach named, GGD-GMM, based on statistical modeling in wavelet domain to describe texture images. Firstly, we propose a robust algorithm based on the combination of the wavelet transform and Scale Invariant Feature Transform (SIFT). Secondly, we implement the aforementioned algorithm and fit the result by using the finite Gamma Mixture Model (GMM). The results, obtained for two benchmark datasets, show that our proposed algorithm has a good relevance as it provides higher classification accuracy compared to some other well known models. Moreover, it displays others advantages relied to Noise-resistant and rotation invariant. Our algorithm could be useful for the analysis of several medical issues.

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