Dynamic Field Vision (DFV): A Hilbert Space Flow Approach to Emergent Feature Extraction

Current computer vision architectures, such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), rely on exhaustive spatial scanning (convolutions) or static global attention (tokenization) to infer object geometry. This paper introduces **Dynamic Field Vision (DFV)**, a novel paradigm rooted in the **Model of Dynamic Internal States (MDEI)** [1]. DFV maps pixels into a high-dimensional Hilbert space (H) and models the image as a continuous vector field that evolves towards a stable, coherent state. This approach replaces the rigid, sequential processes of detection, classification, and stabilization with a single, coupled dynamical flow. The core innovation is the principle of **Detection by Convergence**: object geometry and semantic structure emerge dynamically as the system stabilizes, eliminating the need for pre-defined spatial grids, anchor boxes, or exhaustive hypothesis testing. This results in a framework that is inherently faster, more robust to noise, and aligns naturally with the physics of self-organizing systems.