We describe a prototype software system for investigating novel human-computer interaction techniques for 3-D geospatial data. This system, M4-Geo (Multi-Modal Mesh Manipulation of Geospatial data), aims to provide a more intuitive interface for directly manipulating 3-D surface data, such as digital terrain models (DTM). The M4-Geo system takes place within a 3-D environment and uses a Phantom haptic force feedback device to enhance 3-D computer graphics with touch-based interactions. The Phantom uses a 3-D force feedback stylus, which acts as a virtual "finger tip" that allows the user to feel the shape (morphology) of the terrain's surface in great detail. In addition, it acts as a touch sensitive tool for different GIS tasks, such as digitizing (draping) of lines and polygons directly onto a 3-D surface and directly deforming surfaces (by pushing or pulling the stylus in or out). The user may adjust the properties of the surface deformation (e.g., soft or hard) locally by painting it with a special "material color."

We describe M4, the multi-modal mesh manipulation system, which aims to provide a more intuitive desktop interface for freeform manipulation of 3D meshes. The system combines interactive 3D graphics with haptic force feedback and provide several virtual tools for the manipulation of 3D objects represented by irregular triangle meshes. The current functionality includes mesh painting with pressure dependent brush size and paint preview, mesh cutting via drawing a poly-line on the model and two types of mesh deformations. We use two phantoms, either in a co-located haptic/3D-stereo setup or as a fish tank VR setup with a 3D flat panel. In our system, the second hand assists the manipulation of the object, either by ";holding"; the mesh or by affecting the manipulation directly. While the connection of 3D artists and designers to such a direct interaction system may be obvious, we are also investigating its potential benefits for landscape architects and other users of spatial geoscience data. Feedback from an upcoming user study will evaluate the benefits of this system and its tools for these different user groups.

This paper presents a general trainable framework for object detection in static images of cluttered scenes. The detection technique we develop is based on a wavelet representation of an object class derived from a statistical analysis of the class instances. By learning an object class in terms of a subset of an overcomplete dictionary of wavelet basis functions, we derive a compact representation of an object class which is used as an input to a support vector machine classifier. This representation overcomes both the problem of in-class variability and provides a low false detection rate in unconstrained environments. We demonstrate the capabilities of the technique in two domains whose inherent information content differs significantly. The first system is face detection and the second is the domain of people which, in contrast to faces, vary greatly in color, texture, and patterns. Unlike previous approaches, this system learns from examples and does not rely on any a priori (hand-crafted) models or motion-based segmentation. The paper also presents a motion-based extension to enhance the performance of the detection algorithm over video sequences. The results presented here suggest that this architecture may well be quite general.

This paper presents a trainable object detection architecture that is applied to detecting people in static images of cluttered scenes. This problem poses several challenges. People are highly non-rigid objects with a high degree of variability in size, shape, color, and texture. Unlike previous approaches, this system learns from examples and does not rely on any a priori (hand-crafted) models or on motion. The detection technique is based on the novel idea of the wavelet template that defines the shape of an object in terms of a subset of the wavelet coefficients of the image. It is invariant to changes in color and texture and can be used to robustly define a rich and complex class of objects such as people. We show how the invariant properties and computational efficiency of the wavelet template make it an effective tool for object detection.

A theoretical framework is introduced for the perception of specular surface geometry. When an observer moves in three-dimensional space, real scene features, such as surface markings, remain stationary with respect to the surfaces they belong to. In contrast, a virtual feature, which is the specular reflection of a real feature, travels on the surface. Based on the notion of caustics, a novel feature classification algorithm is developed that distinguishes real and virtual features from their image trajectories that result from observer motion. Next, using support functions of curves, a closed-form relation is derived between the image trajectory of a virtual feature and the geometry of the specular surface it travels on. It is shown that in the 2D case where camera motion and the surface profile are coplanar, the profile is uniquely recovered by tracking just two unknown virtual features. Finally, these results are generalized to the case of arbitrary 3D surface profiles that are travelled by virtual features when camera motion is not confined to a plane. An algorithm is developed that uniquely recovers 3D surface profiles using a single virtual feature tracked from the occluding boundary of the object. All theoretical derivations and proposed algorithms are substantiated by experiments.>

Lambert's model for body reflection is widely used in computer graphics. It is used extensively by rendering techniques such as radiosity and ray tracing. For several real-world objects, however, Lambert's model can prove to be a very inaccurate approximation to the body reflectance. While the brightness of a Lambertian surface is independent of viewing direction, that of a rough surface increases as the viewing direction approaches the light source direction. In this paper, a comprehensive model is developed that predicts body reflectance from rough surfaces. The surface is modeled as a collection of Lambertian facets. It is shown that such a surface is inherently non-Lambertian due to the foreshortening of the surface facets. Further, the model accounts for complex geometric and radiometric phenomena such as masking, shadowing, and interreflections between facets. Several experiments have been conducted on samples of rough diffuse surfaces, such as, plaster, sand, clay, and cloth. All these surface demonstrate significant deviation from Lambertian behavior. The reflectance measurements obtained are in strong agreement with the reflectance predicted by the model.

A comprehensive model that predicts reflectance from rough diffuse surfaces is presented. It is shown that diffuse reflectance from rough surfaces increases as the viewing direction approaches the source direction. This is in contrast to Lambertian surfaces, where radiance is independent of the viewing direction. The new model is a generalization of the Lambertian model, and has significant implications for machine vision, graphics, and remote sensing.>