Image mosaicing: A deeper insight

Pandey, Achala; Pati, Umesh Chandra

doi:10.1016/j.imavis.2019.07.002

Cited by 44 publications

(14 citation statements)

References 86 publications

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“…Due to the limitations of the imaging swath or acquisition mechanism, it is common that the region of interest (ROI) cannot be contained in only one remote sensing image scene. Therefore, it is necessary to generate a large-scale composite image with a sequence of overlapped satellite scenes or tiles, which is usually referred to by a technique called image mosaicking [1]. Image mosaicking consists of five aspects which include image registration, extraction of overlapping areas, radiometric normalization, seam-line detection, and finally, image blending [2].…”

Section: Introductionmentioning

confidence: 99%

Automatic Generation of Seamless Mosaics Using Invariant Features

et al. 2021

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The acquisition of satellite images over a wide area is often carried out across seasons because of satellite orbits and atmospheric conditions (e.g., cloud cover, dust, etc.). This results in spectral mismatch between adjacent scenes as the sun angle and the atmospheric conditions will be different for different acquisitions. In this work, we developed an approach to generate seamless mosaics using Scale-Invariant Features Transformation (SIFT). In this process, we make use of the overlapping areas between two adjacent scenes and then map spectral values of one imagery scene to another based on the filtered points detected by SIFT features to create a seamless mosaic. We make use of the Random Sample Consensus (RANSAC) method successively to filter out obtained SIFT points across adjacent tiles and to remove spectral outliers across each band of an image. Several high resolution satellite images acquired with WorldView-2 and Dubaisat-2 satellites, and medium resolution Sentinel-2 satellite imagery are used for experimentation. The experimental results show that the proposed approach can generate good seamless mosaics. Furthermore, Sentinel-2’s level 2A (L2A) product surface reflectance data is used to adjust the spectral values for color consistency.

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Section: Introductionmentioning

confidence: 99%

Automatic Generation of Seamless Mosaics Using Invariant Features

et al. 2021

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“…But the disadvantage is that the calculation speed is slow. Pandey et al [12] extended the image mosaic to a higher stage and proposed an image mosaic algorithm that can adaptively select the motion model, which greatly improves the matching efficiency. Cooper et al [13] proposed a fully automatic image mosaic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Progressive Image Mosaicing Algorithm in Fine Art Image Fusion for Virtual Reality

Song

2021

IEEE Access

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In virtual reality applications, the fade-in and fade-out algorithm based on Bernstein polynomials is a classic method of image fusion. However, this algorithm has certain limitations in image fusion. In this paper, through the analysis and research of the algorithm, the SIFT algorithm is first used to register the image. Secondly, for the disjoint situation of overlapping areas, a progressive image mosaic fusion algorithm in the form of sine function is proposed, which makes it practical and a wider range of applications. It can reflect its characteristics in applications such as virtual reality and art image fusion. Finally, a segmentation technology is added to make the progressive image mosaic fusion algorithm suitable for a variety of overlapping regions, making this algorithm a convenient and flexible image mosaic fusion algorithm. The simulation experiment results show that the progressive mosaic image fusion algorithm proposed in this paper is in good agreement with the spatial details and texture details of highresolution panchromatic images, and the time is shorter, which meets the real-time requirements. INDEX TERMSFine art image fusion; progressive; image mosaic algorithm; virtual reality

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“…Image fusion is a key step of image stitching to eliminate stitching gaps, ghosting and other bad visual phenomenon [6]. This paper studies the basic theories and key technologies involved in image stitching technology, and analyzes and compares related algorithms [7]. In this paper, an image stitching technique based on the gradated fusion algorithm of the optimal seam is proposed, including the use of feature-based methods to extract and register the feature points of the collected images, and then use the distance algorithm: compare the nearest neighbor distance and the second nearest neighbor distance method to perform rough matching of feature points, then use the sampling consistency algorithm to refine the matching point pairs and calculate the transformation matrix to complete the image registration.…”

Section: Introductionmentioning

confidence: 99%

Optimal Region Gradated Fusion Based Automatic Image Stitching for Industrial Systems

Huang

Zheng

Song

et al. 2021

Preprint

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In the process of equipment production in large manufacturing, the continuity of production is becoming more significant. Timely detection of equipment operation faults can ensure production continuity and greatly reduce loss. In this study, the purpose is to use multi-equipment and image stitching algorithm to obtain the complete image of a large-scale production line. An improved image stitching method based on image fusion is proposed in this paper, which mainly solves the technical problems of stitching seams, unnatural effects, and distortion after image transformation in the existing stitching technique. In the image stitching algorithm, the improved fusion algorithm based on the optimal seam and gradated in and out fusion algorithm is used to realize image fusion, including the use of dynamic programming to find the optimal seam and limit the range of fusion based on the optimal seam found. Finally, the gradated in and out fusion algorithm is used to perform fusion calculation within the limited fusion range to complete image stitching. In the end, through the comparison of different dimensional image fusion indicators with the effect of the existing fusion algorithm, the experimental results show that the method in this paper solves the problem of unnatural image stitching effect, enhances the image stitching result, and has the great fusion effect. Therefore, the panorama processed by the image stitching algorithm proposed in this paper can be efficiently processed through the industrial detection module.

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Image mosaicing: A deeper insight

Cited by 44 publications

References 86 publications

Automatic Generation of Seamless Mosaics Using Invariant Features

Automatic Generation of Seamless Mosaics Using Invariant Features

Optimization of the Progressive Image Mosaicing Algorithm in Fine Art Image Fusion for Virtual Reality

Optimal Region Gradated Fusion Based Automatic Image Stitching for Industrial Systems

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