A fast algorithm for material image sequential stitching

Ma, Boyuan; Ban, Xiaojuan; Huang, Haiyou; Liu, Wanbo; Liu, Chuni; Wu, Di; Zhi, Yonghong

doi:10.1016/j.commatsci.2018.10.044

Cited by 14 publications

(4 citation statements)

References 21 publications

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“…Due to the limitation of image blocks, the model cannot identify the tampered area less than 10% of the whole area well [17]. Ma et al put forward a method of constraining the convolution layer, which can restrain the influence of image content on tampering marks and adaptively extract the tampering features of images [18]. Xie et al proposed a multitask all-CNN method to tamper with images.…”

Section: Related Workmentioning

confidence: 99%

Color Image Mosaic Detection Algorithm Based on Cascaded Multiscale Residual Neural Networks

Fan

2022

Mobile Information Systems

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The appearance of a large number of image editing software packages allows people to easily tamper with image content information, resulting in a significant decrease in image credibility. A color image mosaic detection model based on CNN is proposed in this study. The cascade network structure of shallow thin neurons replaces the single network structure of deep multineurons in this study, and it compensates for the shortcomings of the previous image tampering detection algorithm using the single network structure of deep multineurons by relearning the characteristics of difficult samples. A multiscale convolution layer and a residual module are included in the model at the same time. Feature maps with different receptive fields can be fused with the multiscale convolution layer. By establishing a short connection between the input and output feature maps, the residual module can effectively reduce the risk of gradient disappearance in the model’s training process while also speeding up the network’s convergence speed. The simulation results show that this algorithm has an accuracy of 92.14% and an F1 value of 95.7%. This detection method outperforms other detection methods in terms of detection ability, reliability, and usability. This research gives users more information on which to base their judgments on when judging color mosaic images.

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Section: Related Workmentioning

confidence: 99%

Color Image Mosaic Detection Algorithm Based on Cascaded Multiscale Residual Neural Networks

Fan

2022

Mobile Information Systems

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“…These algorithms, called detectors and descriptors, are used to find these features on maps. Several detectors exist, such as Harris Corner [9] and FAST [10]; several descriptors also exist, such as SIFT [11], SURF [11,12], BRIEF [9], BRISK [13], ORB [14], and FREAK [15]. The difference between them is that detectors just detect the POI, while descriptors detect, compare, and match the POI.…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis of Measurement Processes Using Multi-Physic Instruments: Insights from Stitched Maps

Moreau,

Lemesle,

Páez Margarit

et al. 2024

Metrology

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Stitching methods allow one to measure a wider surface without the loss of resolution. The observation of small details with a better topographical representation is thus possible. However, it is not excluded that stitching methods generate some errors or aberrations on topography reconstruction. A device including confocal microscopy (CM), focus variation (FV), and coherence scanning interferometry (CSI) instrument modes was used to chronologically follow the drifts and the repositioning errors on stitching topographies. According to a complex measurement plan, a wide measurement campaign was performed on TA6V specimens that were ground with two neighboring SiC FEPA grit papers (P#80 and P#120). Thanks to four indicators (quality, drift, stability, and relevance indexes), no measurement drift in the system was found, indicating controlled stitching and repositioning processes for interferometry, confocal microscopy, and focus variation. Measurements show commendable stability, with interferometric microscopy being the most robust, followed by confocal microscopy, and then focus variation. Despite variations, robustness remains constant for each grinding grit, minimizing interpretation biases. A bootstrap analysis reveals time-dependent robustness for confocal microscopy, which is potentially linked to human presence. Despite Sa value discrepancies, all three metrologies consistently discriminate between grinding grits, highlighting the reliability of the proposed methodology.

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“…Image registration has applications in diverse fields, such as art restoration [7], astronomy [8], geology [9], archaeology [10], oceanography [11], agriculture [12], remote sensing [13], materials science [11], medicine [14], robotics [15], augmented reality [9], military [16], etc. Despite the advancements in modern machine learning-based image registration algorithms, feature-based image registration, the concept of which dates back to at least the 1980s [17], still remains relevant even in recent literature, owing to its simplicity, efficiency, and robustness.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Color Space and Channel, Detector, and Descriptor for Feature-Based Image Registration

Yuan,

Poosa,

Dirks

2024

J. Imaging

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The current study aimed to quantify the value of color spaces and channels as a potential superior replacement for standard grayscale images, as well as the relative performance of open-source detectors and descriptors for general feature-based image registration purposes, based on a large benchmark dataset. The public dataset UDIS-D, with 1106 diverse image pairs, was selected. In total, 21 color spaces or channels including RGB, XYZ, Y′CrCb, HLS, L*a*b* and their corresponding channels in addition to grayscale, nine feature detectors including AKAZE, BRISK, CSE, FAST, HL, KAZE, ORB, SIFT, and TBMR, and 11 feature descriptors including AKAZE, BB, BRIEF, BRISK, DAISY, FREAK, KAZE, LATCH, ORB, SIFT, and VGG were evaluated according to reprojection error (RE), root mean square error (RMSE), structural similarity index measure (SSIM), registration failure rate, and feature number, based on 1,950,984 image registrations. No meaningful benefits from color space or channel were observed, although XYZ, RGB color space and L* color channel were able to outperform grayscale by a very minor margin. Per the dataset, the best-performing color space or channel, detector, and descriptor were XYZ/RGB, SIFT/FAST, and AKAZE. The most robust color space or channel, detector, and descriptor were L*a*b*, TBMR, and VGG. The color channel, detector, and descriptor with the most initial detector features and final homography features were Z/L*, FAST, and KAZE. In terms of the best overall unfailing combinations, XYZ/RGB+SIFT/FAST+VGG/SIFT seemed to provide the highest image registration quality, while Z+FAST+VGG provided the most image features.

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A fast algorithm for material image sequential stitching

Cited by 14 publications

References 21 publications

Color Image Mosaic Detection Algorithm Based on Cascaded Multiscale Residual Neural Networks

Color Image Mosaic Detection Algorithm Based on Cascaded Multiscale Residual Neural Networks

Statistical Analysis of Measurement Processes Using Multi-Physic Instruments: Insights from Stitched Maps

Comparative Analysis of Color Space and Channel, Detector, and Descriptor for Feature-Based Image Registration

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