Traceable Symmetry-Aware Image Processing for Two-Dimensional Morphological Diagnostics in Product Concept Design: A Four-Alternative Smart-Speaker Study
Year: 2026
Type: article
Abstract: Product concept images combine symmetry, closure, balance, and repeated components. Existing shape analysis and computational aesthetic methods can quantify these properties; however, when evidence is reduced to global descriptors or aggregate scores, image-layer provenance and sensitivity to rasterization or heuristic settings may be obscured. This paper presents a traceable image-processing pipeline based on scenario framing, alternative specification, geometry-informed computation, evidence synthesis, and design embodiment (SAGE-D), evaluated on four controlled smart-speaker alternatives using separate body, light-band, and aperture masks. Seven dimensionless descriptors measure silhouette reflection, centroid balance, light-band closure, aperture regularity and gradient, component-scale retention, and contour compactness. Resolution resampling, one-pixel morphology, parameter perturbation, and synthetic controls assess sensitivity. At 512×512 pixels, A, B, and R showed exact bilateral silhouette consistency; B and R showed complete light-band occupancy; and C and R showed strong downward aperture-radius gradients. Conventional same-mask measures gave concordant geometric readings, while leave-one-gate-out analysis showed that screening depended mainly on predefined closed-ring and linear-gradient requirements. Only R passed all six case gates. SAGE-D is used here as an auditable organization of layer-specific measurements and bounded screening rules, not as a superior descriptor set. The conclusions are limited to the supplied two-dimensional (2D) representations and do not establish population-level generalizability, preference, or engineering performance.
Source: Symmetry
DOI: 10.3390/sym18081402
Authors: Xinman Wang, Wenjie Liu, Lingwan Huang
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7203824994