Purpose Structural component detection is a prerequisite for various applications, including construction progress measurement and quality inspection. However, it is still a challenge to detect structural components reliably in construction site images taken from a complex and unstructured construction environment. Because construction site images contain numerous unexpected objects, structural...

High, sustained loads in masonry cause time-dependent deformations, which can lead to failure of the construction. This phenomenon mostly occurs in high constructions, such as historical towers, where the dead load imposes significant stresses on the base of the tower [4]. As knowledge and experimental data are lacking to describe this time-dependent behavior in historical masonry, a research p...

This paper proposes a tree-based convolutional neural network (TBCNN) for discriminative sentence modeling. Our model leverages either constituency trees or dependency trees of sentences. The tree-based convolution process extracts sentences structural features, which are then aggregated by max pooling. Such architecture allows short propagation paths between the output layer and underlying fea...

In this work we propose to embed a fused biometric feature vector as a watermark into the sample image in spatial domain based on the fusion parameter (for embedding the watermark) which is chosen adaptively by using the structural similarity measure. Further the watermark is extracted from the subject image and verified with a considerably good accuracy. The results are demonstrated with subst...

objective: diabetes is one of the most common metabolic diseases. earlier diagnosis of diabetes and treatment of hyperglycemia and related metabolic abnormalities is of vital importance. diagnosis of diabetes via proper interpretation of the diabetes data is an important classification problem. classification systems help the clinicians to predict the risk factors that cause the diabetes or pre...

The motivation of this work is customizing 3D models for fabrication. It is desirable to update the visualization of the model’s problematic areas in real time during editing, but often these analysis algorithms are very time consuming. We investigate creating a fast update algorithm for per-vertex model fragility using machine learning. We propose and analyze a new feature vector formulation a...

• The fifth objective of the AASHO Road Test was as follows: "To develop instrumentation, test procedures, data, graphs and formulas which will reflect the capabilities of the various test sections; and which will be helpful in future highway design in the evaluation of the loadcarrying capabilities of existing highways and determining the most promising areas of further highway research." It w...

Structural pattern recognition approaches offer the most expressive, convenient, powerful but computational expensive representations of underlying relational information. To benefit from mature, less expensive and efficient state-of-the-art machine learning models of statistical pattern recognition they must be mapped to a low-dimensional vector space. Our method of explicit graph embedding br...

With the emergence of graph databases, the task of frequent subgraph discovery has been extensively addressed. Although the proposed approaches in the literature have made this task feasible, the number of discovered frequent subgraphs is still very high to be efficiently used in any further exploration. Feature selection based on exact or approximate structural similarity is a way to reduce th...

In this paper, we propose a novel local feature descriptor, Local Gaussian Structural Pattern (LGSP), for face recognition that encodes the directional information of the face’s textures (i.e., the texture’s structure), producing a more discriminant code than other state-of-the-art methods. Each micro-pattern’s structure is computed by using a derivative-Gaussian compass mask, which is more rob...