A dynamic mosaicking method based on histogram equalization for an improved seamline

Laaroussi, Saadeddine; Baataoui, Aziz; Halli, Akram; Satori, Khalid

doi:10.1016/j.procs.2018.01.131

Cited by 9 publications

(3 citation statements)

References 26 publications

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“…Furthermore, ref. [39] employed a histogram equalization strategy to prevent optimal seamlines from crossing over moving objects like cars. Another example [22] applied their mosaic generation approach to two pairs of UAV orthophotos.…”

Section: Viewing Angle Disparitiesmentioning

confidence: 99%

Automated Two-Step Seamline Detection for Generating Large-Scale Orthophoto Mosaics from Drone Images

Varshosaz,

Sajadian,

Pirasteh

et al. 2024

Remote Sensing

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To generate an orthophoto mosaic from a collection of aerial images, the original images are first orthorectified individually using a Digital Surface Model (DSM). Then, they are stitched together along some determined seamlines to form the orthophoto mosaic. Determining appropriate seamlines is a critical process, as it affects the visual and geometric quality of the results. The stitching process can usually be done in frame-to-frame or multi-frame modes. Although the latter is more efficient, both still involve a lot of pre-processing, such as creating individual orthophotos, image registration, and overlap extraction. This paper presents a novel coarse-to-fine approach that directly determines the seamline network without such pre-processing. Our method has been specifically applied for UAV photogrammetry projects where, due to the large number of images and the corresponding overlaps, the orthophoto mosaic generation can be very challenging and time-consuming. We established the seamlines simultaneously for all the images through a two-step process. First, a DSM was generated, and a low-resolution grid was overlayed. Then, for each grid point, an optimal image was selected. Then, the grid cells are grouped into polygons based on their corresponding optimal image. Boundaries of these polygons established our seamline network. Thereafter, to generate the orthophoto mosaic, we overlayed a higher/full resolution grid on the top of the DSM, the optimal image of each point of which was quickly identified via our low-resolution polygons. In this approach, not only seamlines were automatically generated, but also were the need for the creation, registration, and overlap extraction of individual orthophotos. Our method was systematically compared with a conventional frame-to-frame (CF) technique from different aspects, including the number of double-mapped areas, discontinuities across the seamlines network, and the amount of processing time. The outcomes revealed a 46% decrease in orthophoto generation time and a notable reduction in the number of double-mapped areas, sawtooth effects, and object discontinuities within the constructed orthophoto mosaic.

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Section: Viewing Angle Disparitiesmentioning

confidence: 99%

Automated Two-Step Seamline Detection for Generating Large-Scale Orthophoto Mosaics from Drone Images

Varshosaz,

Sajadian,

Pirasteh

et al. 2024

Remote Sensing

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show abstract

“…The process of creating a mosaic usually starts with image registration, however, in some cases, a pretreatment step [29] is included to adjust the colour intensities between the images, to remove unneeded exposure, and to avoid errors in the detection of objects in motion.…”

Section: Fbm Energy Map Creationmentioning

confidence: 99%

Dynamic mosaicking: combining A* algorithm with fractional Brownian motion for an optimal seamline detection

et al. 2020

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“…Although duplication is not considered an error, most of the time it is an unwanted effect that often appears in some methods and is usually not dealt with, unless the goal is to create a mosaic with object tracking in mind. Furthermore, in some cases, if the images have different intensities values such as the images were taken during different times, these errors appear more frequently so to make the method more robust, a pretreatment [12] step is often added in some algorithms to avoid these errors appearing.…”

Section: Introductionmentioning

confidence: 99%

Item‐to‐item recommender system with simultaneous use of multiple images for image mosaicking creation in dynamic scenes

Laaroussi

Baataoui

Halli³

et al. 2020

IET image process

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Image mosaicking is a series of successive algorithms that use a sequence of images or a video of a scene to create a single image with a wider field of view of the whole scene. In most cases, when dynamic objects exist in the input data, issues, such as ghosting, parallax effects or object duplication, are visible in the resulting mosaic. These technical errors appear when the objects in motion aren't properly treated. In order to create good result mosaics, a new method is presented in this paper. The proposed approach uses all the images at the same time to divide the images into different areas by using k‐means clustering to create categories, then each category is recommended from the original images with a recommender system. In fact, by considering each image as a user, each pixel as an item, and each item belonging to a category, it is possible to use a recommender system by computing scores with the item profiles. The resulting mosaic will then be a new user to the system. Furthermore, by clustering the images, projection errors are avoided and a better quality mosaic can be created as is seen in the obtained results.

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A dynamic mosaicking method based on histogram equalization for an improved seamline

Cited by 9 publications

References 26 publications

Automated Two-Step Seamline Detection for Generating Large-Scale Orthophoto Mosaics from Drone Images

Automated Two-Step Seamline Detection for Generating Large-Scale Orthophoto Mosaics from Drone Images

Dynamic mosaicking: combining A* algorithm with fractional Brownian motion for an optimal seamline detection

Item‐to‐item recommender system with simultaneous use of multiple images for image mosaicking creation in dynamic scenes

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