Topological Gradient Connection Analysis for Feature Detection

Edges and corners are two major image features in the modelling of man‐made objects; an edge provides strong geometric orientation and corners possess good localisation. Feature detection is the basis of image processing for numerous applications such as image registration and object modelling. Completeness and localisation are the two major considerations for these applications; however, illumination, reflectance and shadows may interfere with image grey values to produce various gradients along an edge. Thus, threshold selection is an important step in obtaining suitable features in target‐dependent methods as improper selection might cause information loss and broken edges. Instead of threshold selection, this study therefore proposes a feature extraction method using topological gradient connection (TGC) analysis involving three steps: grey value refinement, gradient computation and topological connection analysis. The first step uses a Gaussian filter to smooth the grey value image. The second step computes directional gradients to identify ridge pixels and collect feature candidates. The third step analyses adjacent candidates based on the criterion of topological connection. This three‐step tracing procedure combines these connected candidates into a single object. The proposed scheme employs different images derived from various sensors and compares them with the Canny operator (using manually selected thresholds) and manually plotted corners for detection ability assessment. Experimental results indicate that the automatic results are more complete for subtle feature lines than the Canny edges. In addition, the proposed method provides higher flexibility in selecting suitable feature layers for different applications.