  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.
