Dynamic image mosaic via SIFT and dynamic programming

Zeng, Lin; Zhang, Shengping; Zhang, Jun; Zhang, Yunlu

doi:10.1007/s00138-013-0551-8

Cited by 41 publications

(17 citation statements)

References 24 publications

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“…However, how to divide the uneven illumination image adaptively is the key problem to be solved. Seam-line technology is widely used in image stitching [ 34 – 36 ], and an ideal seam-line makes the difference of both intensity and geometrical structure around the stitch line minimal [ 36 ]. Inspired by the success of seam-line technology in image mosaic, in this paper, we focus on dividing the uneven illumination image using a seam-line-like technology named splitting line.…”

Section: Proposed Robust 2d Otsu's Algorithmmentioning

confidence: 99%

Robust 2D Otsu’s Algorithm for Uneven Illumination Image Segmentation

Xing

Yang

Qingge

2020

Computational Intelligence and Neuroscience

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Otsu’s algorithm is one of the most well-known methods for automatic image thresholding. 2D Otsu’s method is more robust compared to 1D Otsu’s method. However, it still has limitations on salt-and-pepper noise corrupted images and uneven illumination images. To alleviate these limitations and improve the overall performance, here we propose an improved 2D Otsu’s algorithm to increase the robustness to salt-and-pepper noise together with an adaptive energy based image partition technology for uneven illumination image segmentation. Based on the partition method, two schemes for automatic thresholding are adopted to find the best segmentation result. Experiments are conducted on both synthetic and real world uneven illumination images as well as real world regular illumination cell images. Original 2D Otsu’s method, MAOTSU_2D, and two latest 1D Otsu’s methods (Cao’s method and DVE) are included for comparisons. Both qualitative and quantitative evaluations are introduced to verify the effectiveness of the proposed method. Results show that the proposed method is more robust to salt-and-pepper noise and acquires better segmentation results on uneven illumination images in general without compromising its performance on regular illumination images. For a test group of seven real world uneven illumination images, the proposed method could lower the ME value by 15% and increase the DSC value by 10%.

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Section: Proposed Robust 2d Otsu's Algorithmmentioning

confidence: 99%

Robust 2D Otsu’s Algorithm for Uneven Illumination Image Segmentation

Xing

Yang

Qingge

2020

Computational Intelligence and Neuroscience

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“…Yu et al [2] proposed to combine the pixel-based similarity defined by color, edge and texture informations with the region-based saliency map based on the human attention model to guide the optimal seamline searching process of the dynamic programming (DP) algorithm. Zeng et al [23] proposed a new optimal searching criterion that combines the gradient difference and the edge-enhanced weighting intensity one, which provides an effective mechanism for avoiding problems caused by moving objects and geometric misalignments. Then, this algorithm applied dynamic programming to find the optimal seamline.…”

Section: Introductionmentioning

confidence: 99%

Optimal Seamline Detection for Orthoimage Mosaicking by Combining Deep Convolutional Neural Network and Graph Cuts

Yao

Liu

et al. 2017

Remote Sensing

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When mosaicking orthoimages, especially in urban areas with various obvious ground objects like buildings, roads, cars or trees, the detection of optimal seamlines is one of the key technologies for creating seamless and pleasant image mosaics. In this paper, we propose a new approach to detect optimal seamlines for orthoimage mosaicking with the use of deep convolutional neural network (CNN) and graph cuts. Deep CNNs have been widely used in many fields of computer vision and photogrammetry in recent years, and graph cuts is one of the most widely used energy optimization frameworks. We first propose a deep CNN for land cover semantic segmentation in overlap regions between two adjacent images. Then, the energy cost of each pixel in the overlap regions is defined based on the classification probabilities of belonging to each of the specified classes. To find the optimal seamlines globally, we fuse the CNN-classified energy costs of all pixels into the graph cuts energy minimization framework. The main advantage of our proposed method is that the pixel similarity energy costs between two images are defined using the classification results of the CNN based semantic segmentation instead of using the image informations of color, gradient or texture as traditional methods do. Another advantage of our proposed method is that the semantic informations are fully used to guide the process of optimal seamline detection, which is more reasonable than only using the hand designed features defined to represent the image differences. Finally, the experimental results on several groups of challenging orthoimages show that the proposed method is capable of finding high-quality seamlines among urban and non-urban orthoimages, and outperforms the state-of-the-art algorithms and the commercial software based on the visual comparison, statistical evaluation and quantitative evaluation based on the structural similarity (SSIM) index.

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“…Registration of images plays a crucial role in the analysis of multi-dimensional visual data in the digital domain, where at least two images captured under different circumstances, such as from different sensors or at different times, need to be aligned for consistent measurement and processing. This can benefit a wide range of applications, including remote sensing [1][2], motion detection [3], image mosaicking [4,36,37], object recognition [5], medical imaging [6] and super-resolution for data visualization [7] as well as surveillance and video compression [7,8]. As a consequence the literature is enormous and any attempt to provide an account of it would be out of the context of this paper.…”

Section: Introductionmentioning

confidence: 99%

Gradient-based subspace phase correlation for fast and effective image alignment

Ren

Vlachos

Zhang

et al. 2014

Journal of Visual Communication and Image Representation

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Phase correlation is a well-established frequency domain method to estimate rigid 2-D translational motion between pairs of images. However, it suffers from interference terms such as noise and non-overlapped regions. In this paper, a novel variant of the phase correlation approach is proposed, in which 2-D translation is estimated by projection-based subspace phase correlation (SPC). Conventional wisdom has suggested that such an approach can only amount to a compromise solution between accuracy and efficiency. In this work, however, we prove that the original SPC and the further introduced gradient-based SPC can provide robust solution to zero-mean and non-zero-mean noise, and the latter is also used to model the interference term of non-overlapped regions. Comprehensive results from synthetic data and MRI images have fully validated our methodology. Due to its substantially lower computational complexity, the proposed method offers additional advantages in terms of efficiency and can lend itself to very fast implementations for a wide range of applications where speed is at a premium

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Dynamic image mosaic via SIFT and dynamic programming

Cited by 41 publications

References 24 publications

Robust 2D Otsu’s Algorithm for Uneven Illumination Image Segmentation

Robust 2D Otsu’s Algorithm for Uneven Illumination Image Segmentation

Optimal Seamline Detection for Orthoimage Mosaicking by Combining Deep Convolutional Neural Network and Graph Cuts

Gradient-based subspace phase correlation for fast and effective image alignment

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