Single Image Rectification via Object Voting

The patent segments the global rectification problem into multiple local affine transformation estimations, one for each detected object. Each object's local transformation is estimated independently, then aggregated through voting to determine the global projective transformation. This segmentation approach maintains accuracy while reducing computational complexity compared to traditional multi-image methods.

The method uses information from the single input image itself to perform rectification, eliminating the need for external reference images or multiple captures. The objects within the image provide the necessary geometric constraints to determine the transformation parameters, making the system self-sufficient and computationally efficient.

The patent changes the approach from requiring global symmetry constraints to estimating local affine transformations for individual objects. By parameterizing the transformation at the object level and using voting aggregation, the method removes symmetry restrictions while maintaining computational efficiency. The system adapts to various object geometries and image perspectives without requiring symmetric patterns.

The method explicitly handles asymmetric objects and scenes by estimating local transformations for each detected object independently. Rather than requiring symmetric patterns for rectification, the approach accommodates arbitrary object geometries and arrangements, making the system versatile for real-world applications where symmetry cannot be guaranteed.