Auto-rectification of user photos

Chaudhury, K.; DiVerdi, Stephen; Ioffe, Sergey

doi:10.1109/icip.2014.7025706

Cited by 32 publications

(38 citation statements)

References 9 publications

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“…Then we detect straight lines [36] in the correc- tion results using the line segment detector within this range and assume that the angle of these lines should be 90 • after correction and calculate their average angle deviation. As shown in Table 2 and Figure 9, our method outperforms the previous approach [6].…”

Section: Comparison With Previous Techniquesmentioning

confidence: 67%

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Blind Geometric Distortion Correction on Images Through Deep Learning

Zhang

Sander³

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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We propose the first general framework to automatically correct different types of geometric distortion in a single input image. Our proposed method employs convolutional neural networks (CNNs) trained by using a large synthetic distortion dataset to predict the displacement field between distorted images and corrected images. A model fitting method uses the CNN output to estimate the distortion parameters, achieving a more accurate prediction. The final corrected image is generated based on the predicted flow using an efficient, high-quality resampling method. Experimental results demonstrate that our algorithm outperforms traditional correction methods, and allows for interesting applications such as distortion transfer, distortion exaggeration, and co-occurring distortion correction. Multi-model distortion estimationThe GeoNetS network is only able to capture a specific distortion type with a distortion model at a time. For a new type, the entire network has to be retrained. Furthermore, the distortion type and model can be unknown in some cases. In view of these limitations, we designed a second network for multi-model distortion estimation. However, since the distortion model and the parameters ρ β can vary drastically across types, it is impossible to train a multimodel network with the model constraints. We train a network to regress the distortion flow without model constraints and at the same time classify the distortion type. The network is illustrated in Figure 3. The multi-model network N parameterized by θ is jointly trained for two tasks. The first task estimates the distortion flow, learning the mapping from the image domain I to the flow domain F. The second task classifies the distortion type, learning the mapping from image domain I to type domain T .Architecture The entire network adopts an encoderdecoder structure, which includes an encoder part, a decoder part, and a classification part. The input image is fed

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Section: Comparison With Previous Techniquesmentioning

confidence: 67%

“…These images include a variety of scene types (e.g., nature, man-made, water) and are distorted with random distortion parameters to generate our Perspective distortion. For the perspective distortion, we compare with [6]. Here we use angle deviation as the metric.…”

Section: Comparison With Previous Techniquesmentioning

confidence: 99%

Blind Geometric Distortion Correction on Images Through Deep Learning

Zhang

Sander³

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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“…Gallagher et al [15] used vanishing points to calculate the rotation towards the yaw angle of the camera and corrected the image via a simple back rotation of the image. Later, Chaudhury et al [16] proposed a Ransac based approach to estimate two vanishing points and aligned the closer vanishing point with the Y-axis of the image via a post-multiplication operation. Santana et al [17] utilized several long lines in the image to locate the vanishing points and performed image rectification based on a camera motion simulation.…”

Section: Related Workmentioning

confidence: 99%

“…The transformation results are usually measured by conducting a user study to judge the effectiveness of the methods or simply looking at the transformed images [6,9,16,17]. Apart from directly comparing the transformed results, this paper further performs a quantitative comparison by measuring the angle difference of a specific line that should be horizontal after the transformation.…”

Section: B Accuracy Measurementmentioning

confidence: 99%

“…The original images are shown in the first column. The results of the proposed method and Google's method [16] are positioned in the second column and third column receptively. Although both methods achieve satisfactory results in these circumstances, the details from the enlarged TV object show that the proposed method outperforms Google's method in most of the cases.…”

Section: E Comparison To the State-of-the-artmentioning

confidence: 99%

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Assembling Convolution Neural Networks for Automatic Viewing Transformation

Cai

Jiang

Liu

et al. 2020

IEEE Trans. Ind. Inf.

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Images taken under different camera poses are rotated or distorted, which leads to poor perception experiences. This paper proposes a new framework to automatically transform the images to the conformable view setting by assembling different convolution neural networks. Specifically, a referential 3D ground plane is firstly derived from the RGB image and a novel projection mapping algorithm is developed to achieve automatic viewing transformation. Extensive experimental results demonstrate that the proposed method outperforms the state-ofthe-art vanishing points based methods by a large margin in terms of accuracy and robustness.

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