An Efficient and Deterministic Object Recognition System

This paper presents an efficient deterministic framework for small-scale object recognition based on geometric normalization and invariant-driven elimination. The proposed method avoids training procedures and iterative optimization, operating instead through structured preprocessing, scalar invariant filtering, and deterministic vector comparison within a compact single-table vector database. Each object is represented as a feature vector derived from a fixed 7×7 grid and associated geometric descriptors. Recognition proceeds through a three-stage pipeline: (i) centroid-based translation and scale normalization, (ii) invariant-based candidate reduction using mass and second-order spatial moments, and (iii) localized orientation refinement followed by cosine similarity matching. Invariant-driven preselection significantly reduces the candidate set prior to fine comparison, improving computational efficiency while avoiding exhaustive rotational search. The method is lightweight, reproducible, and free of stochastic components. Experimental validation on a 100-object prototype library demonstrates stable and accurate identification under scale variation and controlled rotational perturbations. The results indicate that for structured, low-dimensional pattern libraries, deterministic geometric reasoning combined with finite database ordering provides a practical and computationally controlled recognition mechanism. The proposed framework is designed for structured and finite pattern libraries where objects can be represented through compact grid-based descriptors. In such environments, deterministic geometric preprocessing and invariant filtering significantly reduce the search space before final similarity evaluation. This allows recognition to be completed in a single deterministic evaluation cycle without iterative optimization or training procedures. The approach therefore provides a computationally lightweight alternative for embedded systems, symbolic recognition tasks, and controlled pattern libraries where structural compatibility can be exploited effectively.