Satellite imagery has traditionally been used to collect crop statistics, but its low resolution and registration accuracy limit agricultural analytics to plant stand levels and large areas. Precision agriculture seeks analytic tools at near single plant level, and this work explores how to improve aerial photogrammetry to enable inter-day precision agriculture analytics for intervals of up to a month.Our work starts by presenting an accurately registered image time series, captured up to twice a week, by an unmanned aerial vehicle over a wheat crop field. The dataset is registered using photogrammetry aided by fiducial ground control points (GCPs). Unfortunately, GCPs severely disrupt crop management activities. To address this, we propose a novel inter-day registration approach that only relies once on GCPs, at the beginning of the season.The method utilises LoFTR [1], a state-of-the-art image-matching transformer. The original LoFTR network was trained using imagery of outdoor urban areas. One of our contributions is to extend LoFTR’s training method, which uses matching images of a static scene, to a dynamic scene of plants undergoing growth. Another contribution is a thorough evaluation of our registration method that integrates intraday crop reconstruction with earlier-day scans in a seven degree-of-freedom alignment. Experimental results show the advantage of our approach over other matching algorithms and demonstrate the importance of retraining using crop scenes, and a training method customised for growing crops, with an average registration error of 27 cm across a season.

ROUGE is the most commonly used measure for evaluating summarization algorithms in practice. However, it is questionable whether ROUGE scores adequately reflect the quality of summaries in terms of content. We introduce a metric to measure (legal) content of summaries based on exhaustiveness and concreteness and compare ROUGE scores with values generated by legal experts according to our metric on two tasks. Our results show that ROUGE does not reliably gauge legal content and thus should not be used as a single indicator for the quality of summarization algorithms. For our particular use case we furthermore show one way to increase the reliability of ROUGE by pre-selecting sentences.

In this paper, a novel algorithm for the automatic online improvement of the extrinsic camera parameters of a stereo image pair is introduced. To this end, the well-known dense stereo matching method PatchMatch stereo (PM) is extended for the pixelwise estimation of a discrepancy between the expected epipolar line and the actual correspondence. The availability of an initial guess of the camera parameters is assumed. Next, the estimated disparity map is filtered for highly stable and accurate correspondences that cover preferably the complete image. For this reason, we extend a quality estimation method adapted to Semi-Global Matching (SGM) derived disparity maps for general disparity maps. Finally, the set of stable and accurate correspondences from the disparity map is used for the estimation of the extrinsic camera parameters by means of the five-point algorithm in a RANSAC (random sample consensus) framework. Our algorithm can estimate optimized disparity maps and is able to adjust for errors in the relative camera pose. It can even correct epipolar errors of tens of pixels in highresolution images. We demonstrate that the proposed algorithm allows for robust and accurate estimation of the extrinsic camera parameters on datasets that provide weaklycalibrated image pairs.