Feature Tracking with Automatic Selection of Spatial Scales

Feature Tracking with Automatic Selection of Spatial Scales

When observing a dynamic world, the size of image structures may vary over time. This article emphasizes the need for including explicit mechanisms for automatic scale selection in feature tracking algorithms in order to: (i) adapt the local scale of processing to the local image structure, and (ii) adapt to the size variations that may occur over time. The problems of corner detection and blob...

On the Handling of Spatial and Temporal Scales in Feature Tracking

Junction Detection with Automatic Selection of Detection Scales and Localization Scales

The subject of scale selection is essential to many aspects of multi-scale and multi-resolution processing of image data. This article shows how a general heuristic principle for scale selection can be applied to the problem of detecting and localizing junctions. In a rst uncommitted processing step initial hypotheses about interesting scale levels (and regions of interest) are generated from s...

Cartographic Data Entry through Automatic Feature Tracking

Many cartographic systems currently rely on raster-scan digitizing to convert analog source material to digital form. Raster technology is very effective at rapid and accurate digitization of large volumes of cartographic data. At the same time, existing raster-to-vector (R-V) conversion processes rely on an inordinately large amount of human post-scan editing to coherently sort and combine the...

Visual Feature Tracking with Automatic Motion Model Switching

In this paper we present a motion determination and tracking technique based on the combination of Bayesian multiple hypothesis and a Multiple Model Filtering (MMF) algorithm. Corner features appearing in the initial frame of an image sequence were predicted in the subsequent frames using an extension of the multiple hypothesis algorithm (MHT [I]) based on different motion models. The collectio...