We propose a method for detecting precursors, such as small rock and/or soil fall, which occur prior to massive slope failure. The key feature of our method is directly recognizing the trajectory of a small collapse using spatiotemporal Gabor filtering. Simulation analysis, where the conditions of the simulation are quantitatively defined, reveals the effectiveness of the proposed method in detecting a tiny moving object with low contrast in the background under low frame-rate video monitoring. Experiments using actual monitoring videos of a hazardous slope confirmed the effectiveness of our method. The effects of error factors in an outdoor environment, which may inhibit recognition, are also evaluated.

A new type of geometric model called Interval volume for volumetric data exploration is presented. An interval volume represents a three dimensional subvolume for which the associate scalar values lie within a user specified interval, and provides one of the promising approaches to solid fitting, which is an extended concept of traditional surface fitting. A well known isosurfacing algorithm called Marching Cubes is extended to obtain a solid fitting algorithm, which extracts from a given volumetric data set a high resolution, polyhedral solid data structure of an interval volume. Branch-on-Need Octree is used as an auxiliary data structure to accelerate the extraction process. A variety of interval volume rendering methods and principal related operations, including measurements and focusing, are also presented. The effectiveness of measurement coupled visualization capabilities of the presented approach is demonstrated by application to visualizing a four dimensional simulated data from atomic collision research.

Proposes as a generalization of isosurfaces, the 'interval volume', which is a new type of geometric model representing 3D subvolumes with field values belonging to a closed interval. A dominant surface fitting algorithm called 'marching cubes' is extended to obtain a solid fitting algorithm, which extracts from a given volumetric dataset a high-resolution polyhedral solid data structure of the interval volume. Rendering methods for the interval volume and principal related operations are also presented. The effectiveness of this approach is illustrated with 4D simulated data from atomic collision research.