Perception fundamentally relies on spatial segmentation and boundary discovery, a process traditionally hindered by the computational bottlenecks of sequential hysteresis. While modern CNNs and diffusion models excel at learning data-driven mappings for boundary prediction, they operate at a different pipeline layer, leaving a critical gap for deterministic contour continuation on compute-constrained CPU architectures. We introduce SLUVN, an evolution of Canny Edge Detection that substitutes standard hysteresis with a submillisecond, branchless SIMD state machine. While the foundational algorithm, LUVN, exploits the mathematical orthogonality of gradients to reduce the traditional Moore neighborhood into a deterministic 4-neighbor sequence, SLUVN represents an overhaul of the entire Canny pipeline. By serializing spatial awareness into 8-bit (32-byte aligned) "smart-pixels" during the gradient calculation, this novel Continuity Quadrant leverages SIMD (AVX2/NEON) instructions to evaluate 16 pixels simultaneously within a strictly allocation-free memory pool. Extensive benchmarking on Cityscapes and BSDS500 validates its structural utility against modern baselines, including PiDiNet, HED, and RCF, establishing a new accuracy frontier with a +13.26% ODS increase over sequential baselines. Microarchitectural profiling on compute-constrained edge hardware (4-thread) demonstrates that SLUVN distills raw 2-megapixel Cityscapes frames into a sparse, topologically verified structural representation with an average pipeline latency of just 2.400 ms (416.75 FPS). More importantly for autonomous safety systems, SLUVN's allocation-free design tightens worst-case execution jitter by 38.5% compared to standard baseline implementations. By slashing branch mispredictions by over 80% and achieving an instruction-per-cycle (IPC) of 3.437, SLUVN translates scalar adjacency into SIMD-level parallelism, providing a highly efficient, deterministically safe geometric layer for real-time spatial segmentation on ubiquitous hardware.
Howard et al. (Fri,) studied this question.