Explore the vast range of titles available.

In order to improve the accuracy of semantic model intrinsic detection, a skeleton-based high-level semantic model intrinsic self-symmetry detection method is proposed. The semantic analysis of the model set is realized by the uniform segmentation of the model within the same style, the component correspondence of the model between different styles, and the shape content clustering. Based on the results of clustering analysis, for a given three-dimensional (3D) point cloud model, according to the curve skeleton, the skeleton point pairs reflecting the symmetry between the model surface points are obtained by the election method, and the symmetry is extended to the model surface vertices according to these skeleton point pairs. With the help of skeleton, the symmetry of the point cloud model is obtained, and then the symmetry region of point cloud model is obtained by the symmetric correspondence matrix and spectrum method, so as to realize the intrinsic symmetry detection of the model. The experimental results show that the proposed method has the advantages of less time, high accuracy, and high reliability.

Radial symmetry detection is a hot topic in computer vision owing to its importance in understanding the image structure and content. A major generalisation to the problem is presented and single image spiral symmetric centroid detection is studied. Since radial symmetry is just a particular case in the model of spiral symmetry, spiral symmetry detection is more challenging and general. For this new problem, a fast and effective voting-based solution is proposed, which can reliably detect both the classical radial symmetric centre and the more general spiral symmetric centroid. Extensive experiments validate the generality and practicability of the proposed approach.

In this paper we present a novel moment-based skeleton detection for representing human objects in RGB-D videos with animated 3D skeletons. An object often consists of several parts, where each of them can be concisely represented with a skeleton. However, it remains as a challenge to detect the skeletons of individual objects in an image since it requires an effective part detector and a part merging algorithm to group parts into objects. In this paper, we present a novel fully unsupervised learning framework to detect the skeletons of human objects in a RGB-D video. The skeleton modeling algorithm uses a pipeline architecture which consists of a series of cascaded operations, i.e., symmetry patch detection, linear time search of symmetry patch pairs, part and symmetry detection, symmetry graph partitioning, and object segmentation. The properties of geometric moment-based functions for embedding symmetry features into centers of symmetry patches are also investigated in detail. As compared with the state-of-the-art deep learning approaches for skeleton detection, the proposed approach does not require tedious human labeling work on training images to locate the skeleton pixels and their associated scale information. Although our algorithm can detect parts and objects simultaneously, a pre-learned convolution neural network (CNN) can be used to locate the human object from each frame of the input video RGB-D video in order to achieve the goal of constructing real-time applications. This much reduces the complexity to detect the skeleton structure of individual human objects with our proposed method. Using the segmented human object skeleton model, a video surveillance application is constructed to verify the effectiveness of the approach. Experimental results show that the proposed method gives good performance in terms of detection and recognition using publicly available datasets.

Regular and symmetry shapes occurred in natural and manufactured objects. Detecting these shapes are essential and still tricky task in computer vision. This paper proposes a novel hierarchical shape detection (HiSD) method, which consists of circularity and roundness detection, and regularity and symmetry detection phases. The first phase recognizes the circular and elliptical shapes using aspect ratio and roundness measurements. The second phase, the main phase in the HiSD, recognizes the regular and symmetry shapes using density distribution measurement (DDM) and the proposed sampling point-line distance distribution (SPLDD) algorithm. The proposed method presets effective with low computation cost shape detection approach which is not sensitive to specific category of objects. It enables to detect different types of objects involving the arbitrary, regular, and symmetry shapes. Experimental results show that the proposed method performs well compared to the existing state-of-the-art algorithms.

A bstract A rather general method for determining the spin density matrix of a multi-particle system from angular decay data is presented. The method is based on a Bloch parameterisation of the d -dimensional generalised Gell-Mann representation of ρ and exploits the associated Wigner- and Weyl-transforms on the sphere. Each parameter of a (possibly multipartite) spin density matrix can be measured from a simple average over an appropriate set of experimental angular decay distributions. The general procedures for both projective and non-projective decays are described, and the Wigner P and Q symbols calculated for the cases of spin-half, spin-one, and spin-3/2 systems. The methods are used to examine Monte Carlo simulations of pp collisions for bipartite systems: pp → W + W − , pp → ZZ , pp → ZW + , pp → W + t ¯ , t t ¯ , and those from the Higgs boson decays H → WW * and H → ZZ * . Measurements are proposed for entanglement detection, exchange symmetry detection and Bell inequality violation in bipartite systems.

Ancient Chinese architecture, with its typical symmetrical structures, curved roofs, and upturned eaves presenting a unique architectural aesthetic, is a treasure of Chinese culture. Recently, unmanned aerial vehicle oblique photogrammetry and laser scanning technology have greatly facilitated the realistic replication of ancient buildings and have become crucial data sources for the HBIM of ancient buildings. However, parameter extraction and geometric model representation are more difficult because of the curved surfaces and upturned eaves of traditional Chinese roofs. As symmetrical features are typical of ancient Chinese architecture, the parameter quantity and modelling difficulty of the model representation can be effectively reduced by recognizing the symmetrical structure of traditional Chinese roofs and using “mirror replication” to quickly generate the other half of the model. Accurate symmetry detection and highly efficient parameter extraction are crucial for the HBIM of traditional Chinese roofs. Therefore, in this study, a deep learning network, namely, TCRSym-Net, is proposed to identify the symmetry from point clouds of traditional Chinese roofs. Each roof point cloud is then relocated and reoriented to obtain longitudinal and cross sections, and parametric modelling scripts are coded in Dynamo to model traditional Chinese roofs via curve lofting and solid Boolean operations. The experimental results reveal that the symmetry detection network is effective for symmetry detection, and five different types of traditional Chinese roofs are successfully recreated, which confirms the dependability of the method.

This study investigated whether symmetry perception is vulnerable to metacontrast masking and whether such masking selectively disrupts feedback-dependent visual processes. Across four experiments, we employed a metacontrast paradigm with briefly presented targets (20 ms) followed by masks at varying stimulus onset asynchronies (SOAs), manipulating both target–mask configuration and task demands. All experiments produced the classic U-shaped accuracy-by-SOA curve associated with Type B masking, where performance is lowest at intermediate SOAs. Critically, performance at 0 ms SOA varied depending on the perceptual compatibility of the stimuli. In Experiments 1 and 2, the target and mask were spatially complementary and could be perceptually grouped into a unified figure. Under these conditions, performance at 0 ms SOA exceeded the no-mask baseline, reflecting facilitation due to perceptual integration. In contrast, in Experiments 3 and 4—where the stimuli and mask had no complementary shape and could not be integrated into a coherent object—performance at 0 ms SOA was slightly suppressed, indicating that integration failed to occur. These findings suggest that facilitation at short SOAs depends on the rapid formation of a coherent perceptual object, whereas symmetry detection—requiring temporally extended, feedback-supported integration—is more susceptible to early interruption by masking. Together, these results support both dual-channel and recurrent models of visual masking. Type B suppression reflects interactions between fast feedforward and slower feedback signals, while the presence or absence of early facilitation serves as an index of perceptual organization. These findings underscore how stimulus structure and task context affect the temporal dynamics of shape perception.

This study proposes a method for detecting curved reflection symmetry in binary and grayscale images. The crucial step is to construct a curvilinear symmetry axis generating a nonlinear transformation of the image coordinates that projects the curve on the Y axis and makes the image maximally symmetric about this axis in terms of the Jaccard index. We proposed analytical estimations for the symmetry axis curvature to make the transform bijective. We applied dynamic programming to construct the curvilinear symmetry axis. The axis points are generated one by one with a local direction change at each point. To improve the computational efficiency of the method for images of a given size, we construct a graph of possible transitions in advance. To estimate the symmetry in grayscale images, we proposed two analogs to the Jaccard index. The experiments with image libraries demonstrated that the method correctly handles images containing a single object on a homogeneous background.

Understanding symmetries within data is crucial for explainability and enhancing model efficiency in artificial intelligence. This work investigates an approach to neural symmetry detection, specifically leveraging the mathematical framework of Lie theory. Our approach projects data into a low-dimensional latent space, where symmetry transformations can be efficiently applied. By leveraging the matrix exponential, we accurately capture both affine and non-affine transformations, allowing for improved data augmentation and model selection as potential applications. Our method also estimates transformation magnitude distributions, providing deeper insights into the geometric structure of data. Experiments conducted on augmented MNIST demonstrate the effectiveness of our approach in detecting complex symmetries with multiple transformations. This work paves the way for more interpretable and parameter efficient AI models by identifying structural priors that align with the inherent symmetries in data.

Little is known about the detection of and preference for multiple simultaneous parallel axes of symmetry. Neuroscientists have suggested that the detection of symmetry occurs in extrastriate brain areas with large receptive fields. Such large receptive fields may potentially hinder the simultaneous detection of more than one axis of symmetry. In contrast, psychophysicists have found that symmetry detection occurs within small spatial windows, allowing for the concurrent detection of multiple axes of symmetry. Using psychophysical and computational methods, we aim to test whether multiple axes of symmetry can be detected in parallel and to understand the role of multiple axes of symmetry on aesthetic valence. Experiment 1 provides evidence that multiple axes of symmetry cannot be detected simultaneously. However, with relatively long temporal integration, people can detect them. Experiment 2 suggests that multiple axes of symmetry tend to increase preference. However, the preference for symmetry is not universal because, although most people prefer symmetry, others prefer complex images without axes of symmetry. We present and test a computational model that explains the results of these experiments.