Hopfield network-based approach to detect seam-carved images and identify tampered regions

Wei, Jyh-Da; Cheng, Hui-Jun; Chang, Che-Wen

doi:10.1007/s00521-018-3463-8

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…However, the applicability of this method is restricted due to the common occurrence of curved ground lines in panoramic images. Some researchers introduced Seam Carving, an algorithmic approach that alters the size of an image by carving or inserting pixels in different parts of the image, thereby transforming irregular images into rectangular forms [28][29][30][31][32]. Meanwhile, Lang et al [21] proposed DRIS (Deep Rectangling for Image Stitching), employing a residual progressive regression strategy for fully convolutional network prediction of mesh deformations.…”

Section: Related Workmentioning

confidence: 99%

Rectification for Stitched Images with Deformable Meshes and Residual Networks

Fan,

Mao,

et al. 2024

Applied Sciences

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Image stitching is an important method for digital image processing, which is often prone to the problem of the irregularity of stitched images after stitching. And the traditional image cropping or complementation methods usually lead to a large number of information loss. Therefore, this paper proposes an image rectification method based on deformable mesh and residual network. The method aims to minimize the information loss at the edges of the spliced image and the information loss inside the image. Specifically, the method can select the most suitable mesh shape for residual network regression according to different images. Its loss function includes global loss and local loss, aiming to minimize the loss of image information within the grid and global target. The method in this paper not only greatly reduces the information loss caused by irregular shapes after image stitching, but also adapts to different images with various rigid structures. Meanwhile, its validation on the DIR-D dataset shows that the method outperforms the state-of-the-art methods in image rectification.

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Section: Related Workmentioning

confidence: 99%

Rectification for Stitched Images with Deformable Meshes and Residual Networks

Fan,

Mao,

et al. 2024

Applied Sciences

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show abstract

“…Background. Deep learning has been proven to be a powerful tool for pattern classification problems and sensor studies [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. A deep learning model usually has more than three layers, and by using multiple layers, the model extracts hierarchical features from the original data.…”

Section: Introductionmentioning

confidence: 99%

Iterative Deep Neighborhood: A Deep Learning Model Which Involves Both Input Data Points and Their Neighbors

Liu

Yan

et al. 2020

Computational Intelligence and Neuroscience

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Deep learning models, such as deep convolutional neural network and deep long-short term memory model, have achieved great successes in many pattern classification applications over shadow machine learning models with hand-crafted features. The main reason is the ability of deep learning models to automatically extract hierarchical features from massive data by multiple layers of neurons. However, in many other situations, existing deep learning models still cannot gain satisfying results due to the limitation of the inputs of models. The existing deep learning models only take the data instances of an input point but completely ignore the other data points in the dataset, which potentially provides critical insight for the classification of the given input. To overcome this gap, in this paper, we show that the neighboring data points besides the input data point itself can boost the deep learning model’s performance significantly and design a novel deep learning model which takes both the data instances of an input point and its neighbors’ classification responses as inputs. In addition, we develop an iterative algorithm which updates the neighbors of data points according to the deep representations output by the deep learning model and the parameters of the deep learning model alternately. The proposed algorithm, named “Iterative Deep Neighborhood (IDN),” shows its advantages over the state-of-the-art deep learning models over tasks of image classification, text sentiment analysis, property price trend prediction, etc.

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Rectification for Stitched Images with Deformable Meshes and Residual Networks

Fan,

Mao,

et al. 2024

Preprint

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Image stitching is a crucial aspect of image processing. However, factors like perspective and environment often lead to irregular shapes in stitched images. Cropping or completion methods typically result in substantial loss of information. This paper proposes a method for rectifying irregularly images into rectangles using deformable meshes and residual networks. The method utilizes a convolutional neural network to quantify rigid structures of images. Choosing the most suitable mesh structure based on the extraction results, offering options such as triangular, rectangular, and hexagonal. Subsequently, the irregularly image, predefined mesh structure, and predicted mesh structure are input into a wide residual neural network for regression. The loss function comprises local and global, aimed at minimizing the loss of image information within the mesh and global target. This method not only significantly reduces information loss during rectification but also adapting to different images with various rigid structures. Validation on the DIR-D dataset shows this method outperforms state-of-the-art methods in image rectification.

show abstract

Hopfield network-based approach to detect seam-carved images and identify tampered regions

Cited by 3 publications

References 21 publications

Rectification for Stitched Images with Deformable Meshes and Residual Networks

Rectification for Stitched Images with Deformable Meshes and Residual Networks

Iterative Deep Neighborhood: A Deep Learning Model Which Involves Both Input Data Points and Their Neighbors

Rectification for Stitched Images with Deformable Meshes and Residual Networks

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