The paper proposes a new human-computer interface for telerobotic applications in remote unstructured environments. The interface is based upon Augmented Reality (AR), concepts and integrates three basic aspects: (1) interactive perception, allowing on-demand generation of virtual worksite views for improved examination by the operator; (2) enhanced task representation, involving the 3D embedding of a virtual replica of the equipment into worksite views in order to allow better task planning and rehearsal; (3) haptic feedback to provide the operator with a realistic sense of equipment-worksite mechanical interactions during the planning phase. Experiments with this AR interface in the context of a mining drilling task are also described.

This paper presents a new compression scheme for interlaced stereoscopic sequences which differentiates between a region of fixation and a peripheral area, and thereby compacts the stereoscopic information into the spectral space of a monocular video channel. Spectral compression is achieved by avoiding transmitting high-frequency information over the entire images, but only within and around the region where the observer acuity is the highest. The proposed approach consists of decomposing the left and right fields of the stereoscopic pairs into low-pass and high-pass components. High-frequency components are then limited to a fixation region, thus allowing a reduction of their spectral extent. A composite video signal is then formed by positioning the different components into the available spectral space through filtering and modulation. The approach is compatible with the NTSC standard in the sense that the same color subcarrier and the same spectral region are used for the chrominance components. Strategies are proposed for the estimation of the fixation region, based upon a psychophysical study on visual strategies during depth discrimination tasks.

This paper describes a new spectral compaction method for interlaced stereoscopic sequences which combines the stereo information into the spectral space of a single NTSC video channel. The method makes use of the variable spatial and stereoscopic resolution of the human visual system and relies upon the determination of an area of fixation. Spectral compaction is achieved by retaining only the high-frequency information associated with this area. A composite video signal is formed by combining the lowpass, highpass and chrominance components of both channels into the available spectral space by means of modulation and filtering operations. Compatibility of the composite video signal with the NTSC standard is ensured by using the same colour subcarrier for the chrominance components of one channel and a second subcarrier whose phase is inverted on alternate fields to place the chrominance components of the second channel into the Fukinuki holes. Good quality results are obtained with relatively simple separable FIR filters.

This paper describes a new compression method for interlaced stereoscopic image sequences, which compacts the stereo information into the spectral space of a single NTSC channel. The left and right fields of the stereoscopic pairs are each decomposed into a lowpass and a highpass components. High-frequency components are limited to an area of fixation, thus allowing a reduction of their spectral extent. A composite video signal is then formed by positioning the different components in the available spectral space through filtering and modulation. Compatibility of the produced signal with the NTSC standard is ensured by using the same spectral region for the chrominance information and the same colour subcarrier. Good quality results are obtained with relatively simple 2D separable spatial filters at a reasonable computational cost.

The authors consider the problem of detecting global symmetry in images consisting of dense arrangements of local features, such as dots or oriented segments. Images of this kind are encountered in textured scenes or field-type pictorial representations. The proposed computational model, elaborated on the basis of several psychophysical experiments, assumes a precedence of grouping over symmetry detection and consists of three stages: a grouping stage, in which clusters are formed among local elements presenting a sufficient level of mutual affinity; a symmetry detection stage, in which pairs of symmetrical clusters are discovered and their axes of symmetry determined; and a symmetry subsumption stage, in which an attempt is made to detect more global symmetries. An implementation of the model is described. Results are presented showing a good agreement of the model's performance with human symmetry perception.>

A technique for the detection of depth and orientation discontinuities in range images is described. This method uses basic morphological operators to extract these discontinuities from a dense range map. The authors describe how to extract depth discontinuities using the morphological edge operator of J.S.J. Lee et al. (1987) and how to use a top-hat operator to localize the exact positions of these discontinuities. The detection of orientation discontinuities is then discussed in detail. The calculation of a good estimate of the surface normals using anisotropic filtering inhibited at depth discontinuities is demonstrated. Once these estimates of the normals are calculated, operations similar to those used in depth discontinuity detection are used to locate orientation discontinuities at abrupt variations of the surface normals. Experimental results demonstrate computation of the positions of these discontinuities at pixel precision as well as very efficient use of morphological operators to localize abrupt variations in a noisy image signal in less than 1 s using a Datacube image processor.>

An edge detection and classification scheme for range images which produces a multiscale representation in terms of well-localized depth and orientation edges is presented. The extraction is accomplished by detecting the presence of significant edges at a coarse scale and then determining their precise location by tracking them over decreasing scale. An adaptive multiscale thresholding is applied during this focusing process ro inhibit the attraction of insignificant details. Once focused, the edges are classified into the categories of true edge and diffuse edge by invoking classification rules derived from a mathematical analysis of edge displacement and branching over scale-space. Experimental results illustrate the robustness of the approach in the presence of noise and its performance with synthetic and real images of varying complexity. Comparisons with recently published techniques point out the improved performance of the approach, especially when the images contain substantially overlapping objects.>

Morphological partition filters are introduced in the context of textured-image segmentation. These filters are characterized by a controllable selectivity of action with respect to both directivity and spatial frequency. They are thus able to homogenize regions whose structure corresponds to their characteristics, while leaving others untouched. Automatic adjustment of their structuring elements is obtained through computation of the image covariograms during the various stages of the procedure. The proposed segmentation technique was tested on images made up of natural textures. The procedure appears to perform well in cases of highly structured textures.>