The first complete description of SweepLSD is given, a line segment detector that reads the image exactly once and emits each segment within a few rows of its last pixel passing the scan line, with the tightest frame-time distribution and the best per-segment direction accuracy of the four detectors.
Abstract
We present SweepLSD, a line segment detector that reads the image exactly once and emits each segment within a few rows of its last pixel passing the scan line. Every stage, including connected-component labeling and the final line test, processes the image as a row stream: intermediate memory is O(width) rather than O(pixels), and the per-pixel core is integer-only. We give the first complete description of the algorithm, designed in the author's 2014 master's thesis but never published, together with an open-source C++17 implementation and an FPGA realization -- held bit-exact against the software in its hardware configuration -- detecting segments in live 1080p30 video on 2009-era silicon without frame buffer or external memory. On structure-rich public 4K photographs downscaled to Full-HD, one CPU thread detects segments in ~11 ms -- 4.6x/5.2x/25x faster than the original authors'implementations of ELSED, EDLines, and LSD -- with the tightest frame-time distribution and the best per-segment direction accuracy of the four detectors, and curve rejection by design, while trailing ELSED in F-score on synthetic ground truth. A Manhattan-frame vanishing-point study on York Urban and NYU-VP scores every detector under a selection/evaluation-separated best-estimator-per-detector protocol, under which SweepLSD leads on NYU-VP by ~0.3 degrees and trails by 0.1 degrees on York Urban, with the fastest end-to-end pipeline of the four detectors on both. A single-frame camera-attitude application, evaluated on synthetic scenes with exact ground truth and on EuRoC and TUM-VI, matches the baselines'accuracy at a fraction of their memory, and drives a 4K horizon lock to 0.06 degrees median attitude error at 32 ms median per frame.
Omni-macos is presented, which runs its encoder, index and store on the Mac that already holds the files, so no indexed file, no typed query and no vector ever leaves the machine.
Across three decoder architectures on an H100 the authors measure that parse, not copy, holds 64-72% of device-resident decode time; that bounding back-reference chain depth - provable, and costing 0.006% in ratio - moves latency by at most 2.8% and, for the file's own latency spike, provably by nothing at all.
We report operational experience full-fine-tuning a 32.76B-parameter dense model (Qwen3-32B) on 16 x NVIDIA B300 (two nodes, FSDP / ZeRO-3) -- among the first published field accounts on this accelerator. We claim no new algorithm. The individual mechanisms we use are established practice; our contribution is the integ...
Seon Ho Kim, U-Jeong Jeon, Su Hyeon Kim et al.· 1 citation
The edge detection is a crucial procedure in real-time image processing since it allows you to extract features correctly and comprehend the scene. This paper describes a hardware based Sobel edge detector algorithm based on a 16-directional 5 $\times$ 5 kernel implemented on a Zynq-7000 (xc7z020clg400-3) FPGA with var...
M. Nikhil, Suranjan K H, Rathod Shriram K et al.· 2026 5th International Confe...· 0 citations
The architecture thereby combines significance-aware time encoding, channel-scalable readout, and hardware-aware adaptation, and combines significance-aware time encoding, channel-scalable readout, and hardware-aware adaptation.
Ao-Ming Zhan, Ye Zhao, Yu-Mei Zhou et al.· Applied Sciences· 0 citations
Through controlled ablations on an NVIDIA H100 it is shown that decode throughput is governed not by occupancy, compute, address scatter, or launch parallelism, but by work granularity: throughput is a function of the average match length, because a short match leaves most lanes of a cooperating warp idle.
Yakiv Shavidze· arXiv.org· 1 citation
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