Hardware Accelerator for Feature Matching in Object Recognition

The patent segments the database into multiple partitions or clusters, allowing the matching process to be divided into smaller sub-tasks. Instead of comparing against the entire database at once, the system processes segmented portions in parallel, maintaining comprehensive search coverage while reducing the computational burden on any single processing unit, thereby improving overall processing speed.

The patent introduces hierarchical indexing structures that add dimensional organization to the database. By creating multi-level indexes (such as tree structures or hash-based indexes), the system transforms the flat brute-force search space into a multi-dimensional search architecture, enabling faster navigation and reducing the number of comparisons needed while maintaining matching accuracy.

The patent applies preliminary indexing and clustering actions during an offline training phase, where the database is pre-processed to create hierarchical structures, cluster assignments, and index mappings. This preliminary organization is stored and reused during runtime, eliminating the need for complex real-time clustering calculations and reducing the computational complexity of the matching process while maintaining fast processing speeds.

The system implements self-organizing mechanisms where the database structure automatically adapts and reorganizes based on the data characteristics. The hierarchical indexing and clustering structures are designed to self-optimize through automated data-driven organization, reducing the need for manual configuration and complex control logic, thereby lowering device complexity while maintaining processing efficiency.

The patent replaces software-based feature matching with hardware-accelerated processing units specifically designed for computational tasks. By implementing dedicated hardware circuits (such as FPGAs, ASICs, or GPU accelerators) that perform distance calculations and comparisons, the system achieves the same adaptable matching functionality with significantly lower power consumption, as hardware operations are more energy-efficient than software execution on general-purpose processors.

The patent designs a universal hardware acceleration platform that can perform multiple matching algorithms and distance metrics through reconfigurable logic or programmable architectures. This multi-functional hardware system maintains the adaptability to handle different feature types and matching strategies while consuming less power than software implementations, as the hardware is optimized to perform these operations efficiently across various algorithmic approaches.