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Figure 1: Symmetry-driven structural analysis of an irregular facade (a) results in a hierarchical decomposition (b) into regular grids. Our analysis introduces layering (b), going beyond flat segmentation via splits (c) and allowing more compact and natural structural representations. The resulting hierarchical model of facades enables applications such as structural editing (d) and retargeting (e). AbstractWe present an algorithm for hierarchical and layered analysis of irregular facades, seeking a high-level understanding of facade structures. By introducing layering into the analysis, we no longer view a facade as a flat structure, but allow it to be structurally separated into depth layers, enabling more compact and natural interpretations of building facades. Computationally, we perform a symmetry-driven search for an optimal hierarchical decomposition defined by split and layering operations applied to an input facade. The objective is symmetry maximization, i.e., to maximize the sum of symmetry of the substructures resulting from recursive decomposition. To this end, we propose a novel integral symmetry measure, which behaves well at both ends of the symmetry spectrum by accounting for all partial symmetries in a discrete structure. Our analysis results in a structural representation, which can be utilized for structural editing and exploration of building facades. Links:DL PDF WEB VIDEO DATA CODE

Figure 1: Multi-scale partial intrinsic symmetry detection: five symmetry scales (large to small) are detected. Each symmetric region is shown in uniform color. Note the detection of inter-and intra-object symmetries, as well as cylindrical symmetry of the limbs. AbstractWe present an algorithm for multi-scale partial intrinsic symmetry detection over 2D and 3D shapes, where the scale of a symmetric region is defined by intrinsic distances between symmetric points over the region. To identify prominent symmetric regions which overlap and vary in form and scale, we decouple scale extraction and symmetry extraction by performing two levels of clustering. First, significant symmetry scales are identified by clustering sample point pairs from an input shape. Since different point pairs can share a common point, shape regions covered by points in different scale clusters can overlap. We introduce the symmetry scale matrix (SSM), where each entry estimates the likelihood two point pairs belong to symmetries at the same scale. The pair-to-pair symmetry affinity is computed based on a pair signature which encodes scales. We perform spectral clustering using the SSM to obtain the scale clusters. Then for all points belonging to the same scale cluster, we perform the second-level spectral clustering, based on a novel point-to-point symmetry affinity measure, to extract partial symmetries at that scale. We demonstrate our algorithm on complex shapes possessing rich symmetries at multiple scales. Links:DL PDF WEB DATA

To select a better kind of feature for radar specific emitter identification, the unintentional frequency and phase modulation features are compared through theoretic analysis and experimental verification with 104 real radar instances. Results show that the unintentional phase modulation feature outperforms the unintentional frequency modulation feature.