Edge‐Enhanced Matching for Gradient‐Based Computer Vision Displacement Measurement

Graph Matching Techniques for Computer Vision

Many computer vision applications require a comparison between two objects, or between an object and a reference model. When the objects or the scenes are represented by graphs, this comparison can be performed using some form of graph matching. The aim of this chapter is to introduce the main graph matching techniques that have been used for computer vision, and to relate each application with...

Computer Vision-Based Structural Displacement Measurement Robust to Light-Induced Image Degradation for In-Service Bridges

The displacement responses of a civil engineering structure can provide important information regarding structural behaviors that help in assessing safety and serviceability. A displacement measurement using conventional devices, such as the linear variable differential transformer (LVDT), is challenging owing to issues related to inconvenient sensor installation that often requires additional ...

A Vision-Based Sensor for Noncontact Structural Displacement Measurement

Conventional displacement sensors have limitations in practical applications. This paper develops a vision sensor system for remote measurement of structural displacements. An advanced template matching algorithm, referred to as the upsampled cross correlation, is adopted and further developed into a software package for real-time displacement extraction from video images. By simply adjusting t...

Template Matching Techniques in Computer Vision

Similarity Matching in Computer Vision and Multimedia

Comparing two images, or an image and a model, is the fundamental operation for any retrieval systems. The similarity matching of two images can reside in the hierarchical levels from pixel-by-pixel level, feature space level, object level , and semantic level. In most systems of interest, a simple pixel-by-pixel comparison will not suffice: the difference that we determine must bear some corre...