Abstract. Image stitching is typically decomposed into three phases: registration, which aligns the source images with a common target image; seam finding,
which determines for each target pixel the source image it should come from;
and blending, which smooths transitions over the seams. As described in [1],
the seam finding phase attempts to place seams between pixels where the transition between source images is not noticeable. Here, we observe that the most
problematic failures of this approach occur when objects are cropped, omitted, or
duplicated. We therefore take an object-centered approach to the problem, leveraging recent advances in object detection [2,3,4]. We penalize candidate solutions
with this class of error by modifying the energy function used in the seam finding
stage. This produces substantially more realistic stitching results on challenging imagery. In addition, these methods can be used to determine when there is
non-recoverable occlusion in the input data, and also suggest a simple evaluation
metric that can be used to evaluate the output of stitching algorithms