scholarly journals Adaptive Multi-View Image Mosaic Method for Conveyor Belt Surface Fault Online Detection

2021 ◽  
Vol 11 (6) ◽  
pp. 2564
Author(s):  
Rui Gao ◽  
Changyun Miao ◽  
Xianguo Li
Keyword(s):  
Conveyor Belt ◽  
Detection Algorithm ◽  
Image Mosaic ◽  
Online Detection ◽  
Estimation Model ◽  
Registration Method ◽  
Speeded Up Robust Features ◽  
Registration Rate ◽  
Feature Based ◽  
Overlapping Region

In order to improve the accuracy and real-time of image mosaic, realize the multi-view conveyor belt surface fault online detection, and solve the problem of longitudinal tear of conveyor belt, we in this paper propose an adaptive multi-view image mosaic (AMIM) method based on the combination of grayscale and feature. Firstly, the overlapping region of two adjacent images is preliminarily estimated by establishing the overlapping region estimation model, and then the grayscale-based method is used to register the overlapping region. Secondly, the image of interest (IOI) detection algorithm is used to divide the IOI and the non-IOI. Thirdly, only for the IOI, the feature-based partition and block registration method is used to register the images more accurately, the overlapping region is adaptively segmented, the speeded up robust features (SURF) algorithm is used to extract the feature points, and the random sample consensus (RANSAC) algorithm is used to achieve accurate registration. Finally, the improved weighted smoothing algorithm is used to fuse the two adjacent images. The experimental results showed that the registration rate reached 97.67%, and the average time of stitching was less than 500 ms. This method is accurate and fast, and is suitable for conveyor belt surface fault online detection.

scholarly journals An Image Registration Method Using Deep Residual Network Features for Multisource High-Resolution Remote Sensing Images

2021 ◽  
Vol 13 (17) ◽  
pp. 3425
Author(s):  
Xin Zhao ◽  
Hui Li ◽  
Ping Wang ◽  
Linhai Jing
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Registration ◽  
Feature Descriptor ◽  
Remote Sensing Images ◽  
Residual Network ◽  
Scale Invariant ◽  
Registration Method ◽  
Sensing Applications ◽  
Feature Based

Accurate registration for multisource high-resolution remote sensing images is an essential step for various remote sensing applications. Due to the complexity of the feature and texture information of high-resolution remote sensing images, especially for images covering earthquake disasters, feature-based image registration methods need a more helpful feature descriptor to improve the accuracy. However, traditional image registration methods that only use local features at low levels have difficulty representing the features of the matching points. To improve the accuracy of matching features for multisource high-resolution remote sensing images, an image registration method based on a deep residual network (ResNet) and scale-invariant feature transform (SIFT) was proposed. It used the fusion of SIFT features and ResNet features on the basis of the traditional algorithm to achieve image registration. The proposed method consists of two parts: model construction and training and image registration using a combination of SIFT and ResNet34 features. First, a registration sample set constructed from high-resolution satellite remote sensing images was used to fine-tune the network to obtain the ResNet model. Then, for the image to be registered, the Shi_Tomas algorithm and the combination of SIFT and ResNet features were used for feature extraction to complete the image registration. Considering the difference in image sizes and scenes, five pairs of images were used to conduct experiments to verify the effectiveness of the method in different practical applications. The experimental results showed that the proposed method can achieve higher accuracies and more tie points than traditional feature-based methods.

scholarly journals A Radiomic feature–based Nipple Detection Algorithm on Digital Mammography

2019 ◽  
Vol 46 (10) ◽  
pp. 4381-4391 ◽  
Author(s):  
Jiayu Jiang ◽  
Yaqin Zhang ◽  
Yao Lu ◽  
Yanhui Guo ◽  
Haibin Chen
Keyword(s):  
Digital Mammography ◽  
Detection Algorithm ◽  
Feature Based ◽  
Nipple Detection

A Fast and Accurate Planar-Feature-Based Global Scan Registration Method

2019 ◽  
Vol 19 (24) ◽  
pp. 12333-12345
Author(s):  
Wenpeng Zong ◽  
Minglei Li ◽  
Yanglin Zhou ◽  
Li Wang ◽  
Fengzhuo Xiang ◽  
...  
Keyword(s):  
Registration Method ◽  
Scan Registration ◽  
Feature Based ◽  
Planar Feature

Improvement of Image Mosaic Algorithm Based on SURF

2013 ◽  
Vol 427-429 ◽  
pp. 1625-1630
Author(s):  
Xu Lin Long ◽  
Qiang Chen ◽  
Jun Wei Bao
Keyword(s):  
Coordinate System ◽  
Random Sample ◽  
Clustering Algorithm ◽  
Feature Matching ◽  
Consensus Method ◽  
Image Mosaic ◽  
Matching Accuracy ◽  
Speeded Up Robust Features ◽  
Random Sample Consensus ◽  
Improved Algorithm

The present study concerns about feature matching in image mosaic. In order to solve the problems of low accuracy and poor applicability in the traditional speeded up robust features algorithm, this paper presents an improved algorithm. Clustering algorithm based on density instead of random sample consensus method is used to eliminate mismatching pairs. The initial matching pairs are mapped onto a plane coordinate system, which can be regarded as points, by calculating the density of each point to extract the final matching pairs. The results show that this algorithm overcomes the limitations of the traditional speeded up robust features mosaic method, improving the matching accuracy and speed, and the mosaic effect. It has certain theoretical and practical value.

scholarly journals Configuration and Registration of Multi-Camera Spectral Image Database of Icon Paintings

Computation ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 47
Author(s):  
Arash Mirhashemi
Keyword(s):  
Dynamic Range ◽  
Specular Reflection ◽  
Signal To Noise Ratio ◽  
Simulated Data ◽  
Image Database ◽  
High Dynamic Range ◽  
Registration Method ◽  
Spatial Registration ◽  
Feature Based ◽  
The Cost

At the cost of added complexity and time, hyperspectral imaging provides a more accurate measure of the scene’s irradiance compared to an RGB camera. Several camera designs with more than three channels have been proposed to improve the accuracy. The accuracy is often evaluated based on the estimation quality of the spectral data. Currently, such evaluations are carried out with either simulated data or color charts to relax the spatial registration requirement between the images. To overcome this limitation, this article presents an accurately registered image database of six icon paintings captured with five cameras with different number of channels, ranging from three (RGB) to more than a hundred (hyperspectral camera). Icons are challenging topics because they have complex surfaces that reflect light specularly with a high dynamic range. Two contributions are proposed to tackle this challenge. First, an imaging configuration is carefully arranged to control the specular reflection, confine the dynamic range, and provide a consistent signal-to-noise ratio for all the camera channels. Second, a multi-camera, feature-based registration method is proposed with an iterative outlier removal phase that improves the convergence and the accuracy of the process. The method was tested against three other approaches with different features or registration models.

scholarly journals A Novel Coarse-to-Fine Scheme for Remote Sensing Image Registration Based on SIFT and Phase Correlation

2019 ◽  
Vol 11 (15) ◽  
pp. 1833 ◽  
Author(s):  
Han Yang ◽  
Xiaorun Li ◽  
Liaoying Zhao ◽  
Shuhan Chen
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Phase Correlation ◽  
Mean Square ◽  
Extended Phase ◽  
Registration Method ◽  
Coarse Registration ◽  
Feature Based ◽  
Coarse To Fine ◽  
Fine Registration

Automatic image registration has been wildly used in remote sensing applications. However, the feature-based registration method is sometimes inaccurate and unstable for images with large scale difference, grayscale and texture differences. In this manuscript, a coarse-to-fine registration scheme is proposed, which combines the advantage of feature-based registration and phase correlation-based registration. The scheme consists of four steps. First, feature-based registration method is adopted for coarse registration. A geometrical outlier removal method is applied to improve the accuracy of coarse registration, which uses geometric similarities of inliers. Then, the sensed image is modified through the coarse registration result under affine deformation model. After that, the modified sensed image is registered to the reference image by extended phase correlation. Lastly, the final registration results are calculated by the fusion of the coarse registration and the fine registration. High universality of feature-based registration and high accuracy of extended phase correlation-based registration are both preserved in the proposed method. Experimental results of several different remote sensing images, which come from several published image registration papers, demonstrate the high robustness and accuracy of the proposed method. The evaluation contains root mean square error (RMSE), Laplace mean square error (LMSE) and red–green image registration results.

Algorithm of Lane Detection on an Appropriate Limited Region

2013 ◽  
Vol 433-435 ◽  
pp. 267-272
Author(s):  
Xing Ma ◽  
Chun Yang Mu ◽  
Chun Tao Zhang ◽  
Lu Ming Zhang
Keyword(s):  
Urban Environment ◽  
Hough Transform ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Limited Region ◽  
Feature Based

This paper proposed a lane detection algorithm for urban environment. The algorithm was concerned on selecting an appropriate limited region of interest (ROI) by OTSU segmentation. Then candidates of lane markers were extracted by Canny, finally the lane boundaries were detected by Hough transform. The limited ROI helps to identification lane in an appropriate region. This process have the effect of enhancement in the speed of operation. The proposed algorithm was simulated in MATLAB. The test databases were shared by Fondazione Bruno Kessler (FBK). The experiments show that lane boundaries can be detected correctly although they are fade. Feature-based method is usually affected by intension of image. Several characteristics of roads need to be considered further for detection more precisely.

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