Object Recognition Interpretation with Fixed Segmentation for Small Objects

The patent divides the image into multiple segments (e.g., grid-based segments) instead of using superpixels. Each segment is a fixed geometric region that can be independently analyzed. This segmentation approach allows for more precise localization of small objects since the segment boundaries are fixed and known, enabling accurate attribution of recognition contributions to specific spatial regions without the uncertainty of superpixel boundaries.

The patent employs fixed geometric segmentation (such as grid division) of the image into multiple segments. Each segment has predetermined boundaries that are independent of object boundaries. This approach simplifies the interpretation process by providing a regular, structured framework for analyzing recognition results, while the fine-grained segment structure ensures that even small objects can be localized within specific segments, maintaining reliability.

The patent uses fine-grained segmentation by dividing the image into multiple small fixed segments (e.g., using a grid pattern with sufficient resolution). This creates a large number of segments that can precisely locate small objects while maintaining systematic processing. The regular structure of fixed segments allows for efficient computation despite the increased segment count, as each segment can be processed independently using standardized procedures.