Morphological image compositing

Soille, Pierre

doi:10.1109/tpami.2006.99

Cited by 78 publications

(39 citation statements)

References 38 publications

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“…Seam penalties are widely used in other computer vision applications such as stereo matching (Boykov et al 2001) to give the labeling function its coherence or smoothness. An alternative approach, which places seams along strong consistent edges in overlapping images using a watershed computation has recently been developed by Soille (2006). The sum of the two objective functions is often called the Markov Random Field (MRF) energy, since it arises as the negative log-likelihood of an MRF distribution (Geman and Geman 1984).…”

Section: ))mentioning

confidence: 99%

Image Alignment and Stitching: A Tutorial

Szeliski

2007

FNT in Computer Graphics and Vision

1,153

660

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This tutorial reviews image alignment and image stitching algorithms. Image alignment algorithms can discover the correspondence relationships among images with varying degrees of overlap. They are ideally suited for applications such as video stabilization, summarization, and the creation of panoramic mosaics. Image stitching algorithms take the alignment estimates produced by such registration algorithms and blend the images in a seamless manner, taking care to deal with potential problems such as blurring or ghosting caused by parallax and scene movement as well as varying image exposures. This tutorial reviews the basic motion models underlying alignment and stitching algorithms, describes effective direct (pixel-based) and feature-based alignment algorithms, and describes blending algorithms used to produce seamless mosaics. It closes with a discussion of open research problems in the area.

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Section: ))mentioning

confidence: 99%

Image Alignment and Stitching: A Tutorial

Szeliski

2007

FNT in Computer Graphics and Vision

1,153

660

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“…Given that the difference only reflects changes in a single pixel without neighborhood information, image gradient difference [16,17] and normal cross-correlation [18,19] were selected as measurements in succeeding research. Moreover, in some approaches, image edges [2], salient features [20] and distance to nadir points [20,21] are also considered for seamline optimization. As regards the least-cost path searching algorithms, path optimization methods based on Dijkstra's algorithm are commonly used [10,[22][23][24][25].…”

Section: Related Workmentioning

confidence: 99%

“…In remote sensing, this technique is applied to different datasets, including satellite [1][2][3], aerial [4][5][6][7], low-altitude [8,9] and close-range [10,11] images. In a typical processing pipeline, aerial images are always reprojected onto a digital terrain model (DTM) with various camera locations and orientations, and the obtained digital orthophoto maps (DOMs) are used for mosaicking [12].…”

Section: Introductionmentioning

confidence: 99%

Automatic Seamline Network Generation for Urban Orthophoto Mosaicking with the Use of a Digital Surface Model

Chen

Sun

et al. 2014

Remote Sensing

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Intelligent seamline selection for image mosaicking is an area of active research in the fields of massive data processing, computer vision, photogrammetry and remote sensing. In mosaicking applications for digital orthophoto maps (DOMs), the visual transition in mosaics is mainly caused by differences in positioning accuracy, image tone and relief displacement of high ground objects between overlapping DOMs. Among these three factors, relief displacement, which prevents the seamless mosaicking of images, is relatively more difficult to address. To minimize visual discontinuities, many optimization algorithms have been studied for the automatic selection of seamlines to avoid high ground objects. Thus, a new automatic seamline selection algorithm using a digital surface model (DSM) is proposed. The main idea of this algorithm is to guide a seamline toward a low area on the basis of the elevation information in a DSM. Given that the elevation of a DSM is not completely synchronous with a DOM, a new model, called the orthoimage elevation synchronous model (OESM), is derived and introduced. OESM can accurately reflect the elevation information for each DOM unit. Through the morphological processing of the OESM data in the overlapping area, an initial path network is obtained for seamline selection. Subsequently, a cost function is defined on the basis of several measurements, and Dijkstra's algorithm is adopted to determine the least-cost path from the initial network. Finally, the proposed algorithm is employed for automatic seamline network construction; the effective mosaic polygon of each image is determined, and a seamless mosaic is generated. The experiments with three different datasets indicate that the proposed method OPEN ACCESS Remote Sens. 2014, 6 12335 meets the requirements for seamline network construction. In comparative trials, the generated seamlines pass through fewer ground objects with low time consumption.

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“…A different approach proposed by Reuter et al [10] creates seam lines along landscape features such as waterways or roads to merge nonoverlapping patches of DEMs with minimum geometric discontinuities. Such seam lines can be automatically identified by morphological image compositing [13], frequently used for mosaicking satellite imagery, but rarely used for DEM mosaicking due to the complexity of the method and possible lack of suitable landscape features for the seam line. Achieving seamless transitions is also crucial when filling missing data in DEMs with a DEM from different source.…”

Section: Introductionmentioning

confidence: 99%

Fusion of high-resolution DEMs for water flow modeling

Petrášová

Mitášová

Petras

et al. 2017

Open geospatial data, softw. stand.

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Background: New technologies for terrain reconstruction have increased the availability of topographic data at a broad range of resolutions and spatial extents. The existing digital elevation models (DEMs) can now be updated at a low cost in selected study areas with newer, often higher resolution data using unmanned aerial systems (UAS) or terrestrial sensors. However, differences in spatial coverage and levels of detail often create discontinuities along the newly mapped area boundaries and subsequently lead to artifacts in results of DEM analyses or models of landscape processes. Methods: To generate a seamless updated DEM, we propose a generalized approach to DEM fusion with a smooth transition while preserving important topographic features. The transition is controlled by distance-based weighted averaging along the DEMs' blending overlap with spatially variable width based on elevation differences. Results: We demonstrate the method on two case studies exploring the effects of DEM fusion on water flow modeling in the context of precision agriculture. In the first case study, we update a lidar-based DEM with a fused set of two digital surface models (DSMs) derived from imagery acquired by UAS. In the second application, developed for a tangible geospatial interface, we fuse a georeferenced, physical sand model continuously scanned by a Kinect sensor with a lidar-based DEM of the surrounding watershed in order to computationally simulate and test methods for controlling storm water flow. Conclusions:The results of our experiments demonstrate the importance of seamless, robust fusion for realistic simulation of water flow patterns using multiple high-resolution DEMs.

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Morphological image compositing

Cited by 78 publications

References 38 publications

Image Alignment and Stitching: A Tutorial

Image Alignment and Stitching: A Tutorial

Automatic Seamline Network Generation for Urban Orthophoto Mosaicking with the Use of a Digital Surface Model

Fusion of high-resolution DEMs for water flow modeling

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