Abstract
Seamline generation is a crucial last step in the ortho-image mosaicking process. In particular, it is required to convolute residual geometric and radiometric imperfections that stem from various sources. In particular, temporal differences in the acquired data will cause the scene content and illumination conditions to vary. These variations can be modelled successfully. However, one is left with micro-differences that do need to be considered in seamline generation. Another cause of discrepancies originates from the rectification surface as it will not model the actual terrain and especially human-made objects perfectly. Quality of the image orientation will also contribute to the overall differences between adjacent ortho-rectified images. <br><br> Our approach takes into consideration the aforementioned differences in designing a seamline engine. We have identified the following essential behaviours of the seamline in our engine: 1) Seamlines must pass through the path of least resistance, i.e., overlap areas with low radiometric differences. 2) Seamlines must not intersect with breaklines as that will lead to visible geometric artefacts. And finally, 3), shorter seamlines are generally favourable; they also result in faster operator review and, where necessary, interactive editing cycles. The engine design also permits alteration of the above rules for special cases. <br><br> Although our preliminary experiments are geared towards line imaging systems (i.e., the Leica ADS family), our seamline engine remains sensor agnostic. Hence, our design is capable of mosaicking images from various sources with minimal effort. The main idea behind this engine is using graph cuts which, in spirit, is based of the max-flow min-cut theory. The main advantage of using graph cuts theory is that the generated solution is global in the energy minimization sense. In addition, graph cuts allows for a highly scalable design where a set of rules contribute towards a cost function which, in turn, influences the path of minimum resistance for the seamlines. In this paper, the authors present an approach for achieving quality seamlines relatively quickly and with emphasis on generating truly seamless ortho-mosaics.
Highlights
Watershed SegmentationThe watershed by flooding (Beucher S., and Lantuejoul C., 1979) algorithm is – conceptually – based on how water flows into areas of local minima and form separated pools
2) Seamlines must not intersect with breaklines as that will lead to visible geometric artefacts
Our preliminary experiments are geared towards line imaging systems, our seamline engine remains sensor agnostic
Summary
The watershed by flooding (Beucher S., and Lantuejoul C., 1979) algorithm is – conceptually – based on how water flows into areas of local minima and form separated pools. The boundary of each pool is the local maxima, and as presented in this paper, we can utilize the watershed segmentation to identify a network of possible seamlines from a cost image. Identify a set of markers within an image that represent local low points. These local minimas serve as the seeds for the watershed algorithm. 2. Starting with the lowest unmarked minima next to a segment, a pixel flooding operator marks pixels with the same value as their neighbouring seed. The authors offer an automatic approach to pick the markers that works well in the context of seamline generation as a presented in the text later on
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