Accurate evaluation of the Strouhal number is important for wind-resistant design, but traditional aerodynamic assessments are computationally expensive. To support rapid schematic design, this paper proposes a deep learning framework to predict the...
Although shape similarity is one fundamental element in coastline generalization quality, its related research is still inadequate. Consistent with the hierarchical pattern of shape recognition, the Dual-side Bend Forest Shape Representation Model is...
The melting time of scrap is a factor that affects the Electrical Energy (EE) consumption of the Electric Arc Furnace (EAF) process. The EE consumption itself stands for most of the total energy consumption during the process. Three distinct represen...
The representation of 3D shapes from point clouds remains a fundamental challenge in computer vision. A common approach decomposes 3D objects into interpretable geometric primitives, enabling compact, structured, and efficient representations. Buildi...
It is challenging for semantic segmentation of buildings based on high-resolution remote sensing images, given high variability of appearance and complicated backgrounds of the buildings and their images. In this communication, we proposed an ensembl...
3D shape clustering is developing into an important research subject with the wide applications of 3D shapes in computer vision and multimedia fields. Since 3D shapes generally take on various modalities, how to comprehensively exploit the multi-moda...
Accurate agricultural object extraction from high-resolution remote sensing imagery requires not only high regional segmentation accuracy but also reliable object geometry. Yet most semantic segmentation frameworks are optimized primarily for pixel-...
Robust and accurate face alignment remains a challenging task, especially when local noises, illumination variations and partial occlusions exist in images. The existing local search and global match methods often misalign due to local optima without...
The classification and recognition of the shapes of buildings in map space play an important role in spatial cognition, cartographic generalization, and map updating. As buildings in map space are often represented as the vector data, research was co...
12 September 2017
The adjoint method is considered as the most efficient approach to compute gradients with respect to an arbitrary number of design parameters. However, one major challenge of adjoint-based shape optimization methods is the integration into a computer...
Biological cognition depends on learning-structured representations in ambiguous environments. Computational models of structure learning typically frame this as an inference problem, but often overlook the temporally extended dynamics that shape lea...
In this paper, a novel surrogate model for shape-parametrized vehicle drag force prediction is proposed. It is assumed that only a limited dataset of high-fidelity CFD results is available, typically less than ten high-fidelity CFD solutions for diff...
Effective shape representation of building footprints is essential for many geospatial analysis and applications, such as building retrieval, cartographic generalization, and urban morphology analysis. Constrained by scarce and coarse annotations of...
The raindrop size distribution (DSD) is vital for applications such as quantitative precipitation estimation, understanding microphysical processes, and validation/improvement of two-moment bulk microphysical schemes. We trace the history of the DSD...
Rapid pose classification and pose retrieval in 3D human datasets are important problems in shape analysis. In this paper, we extend the Multi-View Convolutional Neural Network (MVCNN) with ordered view feature fusion for orientation-aware 3D human p...
In this paper, we propose a 3 dimensional (3D) model identification method based on weighted implicit shape representation (WISR) and panoramic view. The WISR is used for 3D shape normalization. The 3D shape normalization method normalizes a 3D model...
Particle shape is a critical factor governing the macroscopic behavior of granular materials. While the Discrete Element Method (DEM) has proven to be a powerful tool for modeling these materials, the accurate representation of particles with realist...
This study addresses a key limitation of conventional clay constitutive models, which often assume linear stress paths at low stress ratios and lack a systematic link between plasticity and yield surface shape. A symmetry-consistent bounding surface...
Space and shape is one of the geometry topics that should be mastered by students and require proper teachers’ Mathematics Content Knowledge (MCK) for teaching to avoid misconception. This study aimed at developing a constructive conceptual fra...
The review of historical archives that allow us to know the observations and experiences of those who recorded scarcely explored territories in the past, especially in the context of European colonization of vast areas of the world in the seventeenth...
Background/Objectives: Parental mental representations play a crucial role in shaping early parent–child relationships, particularly during the perinatal period. These internal models influence caregiving behaviors, emotional attunement, and th...
While studies consistently show that the popular media often provide medicalized images of disabled people as “other” or inferior, dynamic societal changes, such as the diffusion of human rights laws, increasing public awareness, and the...
Shape analysis of curves in
Early-stage ship design demands efficient exploration of diverse hull forms under tight geometric and performance constraints. Existing data-driven approaches often rely on a single geometric representation, limiting their ability to capture both glo...
Stereo-digital image correlation (DIC) is promising in structural vibration testing due to its advantages of non-contact, full-field, and high-spatial resolution. However, thousands of full-field displacement maps generated by stereo-DIC hamper its p...
In object detection systems for autonomous driving, LIDAR sensors provide very useful information. However, problems occur because the object representation is greatly distorted by changes in distance. To solve this problem, we propose a LIDAR shape...
Robotic manipulation policies have made significant progress in recent years, yet their target-cognitive generalization capability remains insufficient when facing unseen targets and scenarios with similar distractors. Existing methods mostly rely on...
Most fruit classification studies rely on color-based features, but shape-based analysis provides a promising alternative for distinguishing subtle variations within the same variety. Tomato shape classification is challenging due to irregular contou...
This paper introduces a hierarchical object-centric descriptor framework called TOSD (Triplet Object-Centric Semantic Descriptor). The goal of this method is to overcome the limitations of existing pixel-based and global feature embedding approaches....
This study investigates how digital representation practices shape spatial meaning-making, aesthetic coherence, and narrative construction in interior design education. Responding to a notable gap in the literature regarding the limited examination o...
Regularization can improve generalization by constraining how a model uses its internal representation. In this paper, we study whether algebraic fixed-point constraints applied to the final LSTM hidden state during training can reorganize the recurr...
The Weibull is a popular distribution that models monotonous failure rate data. In this work, we introduce the four-parameter Weibull extended Weibull distribution that presents greater flexibility, thus modeling data with bathtub-shaped and unimodal...
Visual information shapes spatial perception and body representation in human augmentation. However, the perceptual consequences of viewpoint-height changes produced by sensor–display geometry are not well understood. To address this gap, we de...
A number of different approaches are currently available to digitally restore the symmetry of a specimen deformed by taphonomic processes. These tools include mirroring and retrodeformation to approximate the original shape of an object by symmetrisa...
In both commercial and scientific photography, achieving accurate colour reproduction is essential. This study investigates the influence of light-shaping attachments on colour rendering indices (CRIs) under various lighting conditions. Through 180 m...
Generative Adversarial Networks (GANs) perform well on natural images but often fail in domains governed by strict geometric or symbolic constraints. This work focuses on convolutional GANs and studies how their inductive biases interact with two con...
The Loess Plateau is highly susceptible to landslides due to its fragile geological structure and frequent human activities, particularly old landslides with historical structural damage. The features of these landslides in remote sensing images beco...
Accurate battery state-of-charge (SOC) estimation under dynamic operating conditions remains challenging because battery responses are nonlinear, history-dependent, temperature-sensitive, and prone to transient disturbances. To address this problem,...
Evaluating whether an intellectual property (IP) prototype faithfully preserves the visual identity and semantic intent of its original concept design is an important yet challenging task in product design and creative prototyping. Existing evaluatio...
In civil engineering, different machine learning algorithms have been adopted to process the huge amount of data continuously acquired through sensor networks and solve inverse problems. Challenging issues linked to structural health monitoring or lo...
The robotic mapping problem, which consists in providing a spatial model of the environment to a robot, is a research topic with a wide range of applications. One important challenge of this problem is to obtain a map that is information-rich (i.e.,...
Artificial intelligence (AI) is increasingly used in medical imaging, with research spanning task-specific classification, detection, and segmentation, representation learning, image reconstruction, generative models, foundation models, and vision&nd...
Background/Objectives: Emotion processing has increasingly been conceptualized as a transdiagnostic mechanism underlying psychological adaptation and psychopathology. From an attachment perspective, individual differences in emotion perception may be...
Mesh-agnostic models have advantages in processing flow field data with various topologies and densities, and they can easily incorporate partial differential equations. Beyond physics-informed neural networks, mesh-agnostic models have been studied...
The scale features of rock art exhibit significant diversity and graduality. Among the existing semantic segmentation methods for rock art, although some models have taken note of the scale differences in rock art patterns and the complexity of direc...
Accumulating evidence over the past decades has established that people conceptualize elapsing time along a sagittal mental timeline (MTL). A recent study discovered that representations of autobiographical memories (AMs) also proceed along a sagitta...
Background: Our representation of the body is multidimensional, involving both the processing of internal bodily signals and representations of the body’s external features. Although interoception (inner) and outer body representations show age...
In precision livestock farming, 3D point clouds provide important data support for analyzing pig behavior and monitoring their health. However, due to environmental occlusions, limited sensor viewpoints, and mutual shielding between pigs, the acquire...
Nailfold capillaroscopy is a reliable way to detect and analyze microvascular abnormalities. It is safe, simple, noninvasive, and inexpensive. Among all the capillaroscopic abnormalities, nailfold microhemorrhages are closely associated with early va...
Geometrical structures and the internal local region relationship, such as symmetry, regular array, junction, etc., are essential for understanding a 3D shape. This paper proposes a point cloud feature extraction network named PointSCNet, to capture...
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