Beyond the Black Box in Computer Vision: A Traceable Architecture for Reproducible Canny Contour Identification
DOI:
https://doi.org/10.69916/comtechno.v4i1.412Keywords:
Canny Framework, Gradient Isotropy, Experimental Reproducibility, Configuration Management, Inferential StatisticsAbstract
While modern edge detection algorithms are pivotal in advanced computer vision pipelines, conventional implementations typically operate as static, transient black-boxes, severely hindering experimental replication and parameter traceability. This study addresses this methodological bottleneck by developing a structured, web-based contour identification system designed to enforce configuration management within the Canny framework. The decoupled architecture fuses a PHP-driven interface with a high-performance Python backend and a persistent relational database layer, allowing for the deterministic tracking of localized hysteresis thresholds, aperture scales, and spatial gradient vectors. Empirical evaluations were executed on a curated dataset comprising high-contrast object morphologies, characterized by distinct structural boundaries and varied illumination backgrounds to rigorously test edge degradation. The transition from linear Manhattan approximations to an isotropic Euclidean space ( gradient norm) yields single-pixel edge localization sharpness and unbroken contour continuity. Quantitatively, this mathematical refinement achieves a peak F-measure boundary accuracy of 0.91 and a Pratt’s Figure of Merit of 0.895, albeit introducing a 16.8% latency overhead. Furthermore, a multi-factor Analysis of Variance (ANOVA) robustly rejects the null hypothesis, confirming that parameter interactions significantly dictate contour fidelity (). The primary contribution of this research is the transformation of a heuristic vision task into a deterministic, database-backed ecosystem. By embedding an explicit audit trail for every processing trace, this framework provides computer vision practitioners with a rigorous instrument for quasi-quantitative comparative analysis, establishing a transparent benchmark for reproducible boundary extraction in downstream visual recognition tasks.
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