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These days, everyone seems to be working on a digital mapping project, and I am no exception. Digital research projects take advantage of technological advances of the past ten years [...]

In this paper, we present incremental smoothing and mapping (iSAM), which is a novel approach to the simultaneous localization and mapping problem that is based on fast incremental matrix factorization, iSAM provides an efficient and exact solution by updating a QR factorization of the naturally sparse smoothing information matrix, thereby recalculating only those matrix entries that actually change, iSAM is efficient even for robot trajectories with many loops as it avoids unnecessary fill-in in the factor matrix by periodic variable reordering. Also, to enable data association in real time, we provide efficient algorithms to access the estimation uncertainties of interest based on the factored information matrix. We systematically evaluate the different components of iSAM as well as the overall algorithm using various simulated and real-world datasets for both landmark and pose-only settings. Index Terms--Data association, localization, mapping, mobile robots, nonlinear estimation, simultaneous localization and mapping (SLAM), smoothing.

This paper presents a novel local submap joining algorithm for building large-scale feature-based maps: sparse local submap joining filter (SLSJF). The input to the filter is a sequence of local submaps. Each local submap is represented in a coordinate frame defined by the robot pose at which the map is initiated. The local submap state vector consists of the positions of all the local features and the final robot pose within the submap. The output of the filter is a global map containing the global positions of all the features as well as all the robot start/end poses of the local submaps. Use of an extended information filter (EIF) for fusing submaps makes the information matrix associated with SLSJF exactly sparse. The sparse structure together with a novel state vector and covariance submatrix recovery technique makes the SLSJF computationally very efficient. The SLSJF is a canonical and efficient submap joining solution for large-scale simultaneous localization and mapping (SLAM) problems that makes use of consistent local submaps generated by any reliable SLAM algorithm. The effectiveness and efficiency of the new algorithm is verified through computer simulations and experiments. Index Terms--Extended information filter (EIF), extended Kalman filter, map joining, simultaneous localization and mapping (SLAM), sparse matrix.

The ability to learn a map of the environment is important for numerous types of robotic vehicles. In this paper, we address the problem of learning a visual map of the ground using flying vehicles. We assume that the vehicles are equipped with one or two low-cost downlooking cameras in combination with an attitude sensor. Our approach is able to construct a visual map that can later on be used for navigation. Key advantages of our approach are that it is comparably easy to implement, can robustly deal with noisy camera images, and can operate either with a monocular camera or a stereo camera system. Our technique uses visual features and estimates the correspondences between features using a variant of the progressive sample consensus (PROSAC) algorithm. This allows our approach to extract spatial constraints between camera poses that can then be used to address the simultaneous localization and mapping (SLAM) problem by applying graph methods. Furthermore, we address the problem of efficiently identifying loop closures. We performed several experiments with flying vehicles that demonstrate that our method is able to construct maps of large outdoor and indoor environments. Index Terms--Attitude sensor, flying vehicles, simultaneous localization and mapping (SLAM), vision.

This paper is centered around landmark detection, tracking, and matching for visual simultaneous localization and mapping using a monocular vision system with active gaze control. We present a system that specializes in creating and maintaining a sparse set of landmarks based on a biologically motivated feature-selection strategy. A visual attention system detects salient features that are highly discriminative and ideal candidates for visual landmarks that are easy to redetect. Features are tracked over several frames to determine stable landmarks and to estimate their 3-D position in the environment. Matching of current landmarks to database entries enables loop closing. Active gaze control allows us to overcome some of the limitations of using a monocular vision system with a relatively small field of view. It supports 1) the tracking of landmarks that enable a better pose estimation, 2) the exploration of regions without landmarks to obtain a better distribution of landmarks in the environment, and 3) the active redetection of landmarks to enable loop closing in situations in which a fixed camera fails to close the loop. Several real-world experiments show that accurate pose estimation is obtained with the presented system and that active camera control outperforms the passive approach. Index Terms--Active camera control, landmark selection, mobile robotics, saliency, visual attention, visual simultaneous localization and mapping (SLAM).

The adaptive-subspace self-organizing map (ASSOM) is useful for invariant feature generation and visualization. However, the learning procedure of the ASSOM is slow. In this paper, two fast implementations of the ASSOM are proposed to boost ASSOM learning based on insightful discussions of the basis rotation operator of ASSOM. We investigate the objective function approximately maximized by the classical rotation operator. We then explore a sequence of two schemes to apply the proposed ASSOM implementations to saliency-based invariant feature construction for image classification. In the first scheme, a cumulative activity map computed from a single ASSOM is used as descriptor of the input image. In the second scheme, we use one ASSOM for each image category and a joint cumulative activity map is calculated as the descriptor. Both schemes are evaluated on a subset of the Corel photo database with ten classes. The multi-ASSOM scheme is favored. It is also applied to adult image filtering and shows promising results. Index Terms--Adaptive-subspace self-organizing map (ASSOM), adult image filtering, feature construction, image classification.

Byline: S. S. Fedin (1), A. S. Kulikov (1) Abstract: The splitting method is one of the most powerful and well-studied approaches to solving various NP-hard problems. The main idea of this method is to split the input instance of a problem into several simpler instances (further simplified by certain simplification rules) such that when the solution for each of them is found, one can construct the solution for the initial instance in polynomial time. There exists a huge number of papers describing algorithms of this type, and usually a considerable part of such a paper is devoted to case analysis. In this paper, we present a program that, given a set of simplification rules, automatically generates a proof of an upper bound on the running time of a splitting algorithm using these rules. As an example, we report the results of experiments with such a program for the SAT, MAXSAT, and (n, 3)-MAXSAT (the MAXSAT problem for the case where every variable in the formula appears at most three times) problems. Bibliography: 13 titles. Author Affiliation: (1) St.Petersburg State University, St.Petersburg, Russia Article History: Registration Date: 10/03/2006 Received Date: 11/10/2004 Article note: Translated from Zapiski Nauchnykh Seminarov POMI, Vol. 316, 2004, pp. 111--128.

The growth of the Internet and the digital revolution have meant increased reliance on electronic representations of information. Geospatial information has been readily adapted to the world of cyberspace, and most Web pages incorporate graphics, images, or maps to represent spatial and spatialized data. But flat computer screens do not facilitate a map or graph experience by those who are visually impaired. The traditional method for compensating for nonvisual access to maps and graphics has been to construct hard-copy tactile maps. In this article, we examine an electronic accommodation for nonvisual users--the haptic map. Using new and off-the-shelf hardware--force feedback and vibrotactile mice--we explore how touch can be combined with virtual representations of shapes and patterns to enable nonvisual access to onscreen map or graphic material. Key Words: digital representation, haptic maps, visual impairment.

In this study, independent classifications of Landsat Thematic Mapper imagery and Jet Propulsion Laboratory AirSAR were combined to create an integrated classification of pasture and other vegetation types for a study area in the agricultural zone of Western Australia. The resulting classification combines greenness and brightness information from optical data with structure and water content information from synthetic aperture radar (SAR). Field observations of vegetation type, botanical composition, ground cover percentage, wet and dry biomass, canopy height, and soil water content were collected at 34 sites representing a range of pastures, browse shrubs, and crops. An unsupervised version of the Complex Wishart classification procedure, based on preserving scattering characteristics from the Freeman and Durden backscatter decomposition, was applied to the C-, L-, and P-band polarimetric SAR data. The optical classification was carried out using a principle component analysis on the green, red, and near-infrared bands and clustering on the basis of a class centroid distance measure and knowledge of ground targets. These two classification results were then fused together. Assessment of a confusion matrix using the individual sites showed that identification of more uniform, dense, and structurally distinct canopies was better than that of more diverse, sparse, and structurally ambiguous canopies, as the former were better represented by the canopy height attribute used in the SAR classification component. The optical classification enabled correction of SAR misclassification of vegetation due to surface roughness and soil moisture effects, or similar backscatter responses from herbaceous or arboreal canopies. The results show that simplification of vegetation into groups based upon properties with sensitive responses in both the optical and SAR domains, and combination of separate SAR and optical classifications, has potential for improving classification of diverse and heterogeneous herbaceous and browse cover in grazing lands. However, collection of ground calibration data must be at an appropriate spatial scale and include canopy and surface measurements directly related to backscatter mechanisms and spectral sensitivity. Index Terms--Botanical composition, grazing lands, herbage height, land-use classification, radar polarimetry, synthetic aperture radar (SAR).

We consider here the implementation of efficient search and retrieval of digital elevation maps (DEMs), in particular the ability to conduct elevation searches without decompressing the entire map. We utilize set partitioning in hierarchical trees (SPIHT) for the compression, and compare two wavelet-based search and retrieval systems: one utilizing nonlinear max- and min-lifted integer wavelets and the other using the standard 5/3 integer wavelet. While the coarse-scale maxima/minima preservation inherent in the max- and min-lifted wavelets may seem ideal for the implementation of efficient elevation searches, the interscale propagation of maxima/minima and the bit-plane layering of the SPIHT coder adversely affect the efficiency of such searches. The use of the standard 5/3 integer wavelet requires the addition of separate maxima and minima information but does not have the complication of coefficient propagation, providing more satisfactory results. Elevation search bitrates are presented for both systems and compared to results obtained using the Kakadu implementation of the JPEG2000 standard. Index Terms--Digital elevation maps (DEMs), image browsing, max-lifted, nonlinear wavelets, search and retrieval lossless compression.

Visualization can be of significant value in improving the verification, validation, and communication of discrete-event simulation models of construction operations, which in turn can make the models more credible and thus useful in operations planning. The presented work extends the state-of-the-art of visualizing simulation models of construction operations in three-dimensions. The writers present efficient methods and a tool, ViTerra, that can be used to automatically generate photorealistic, digital, three-dimensional (3D) terrain computer-aided design databases to represent construction job sites in animations of simulated construction processes. The work capitalizes on the availability of detailed topographical and aerial imagery data that are readily available in digital format from several government and private organizations. In addition to automated terrain generation techniques, the presented work implements animation methods to describe the evolution of virtual jobsites by depicting deformations to the visualized terrains in response to common construction operations such as earthmoving and trenching. The writers' first contribution alleviates the need to manually create 3D terrain models for use in animations as has been the case hitherto. The writers' second contribution (1) enhances realism in animations of several common construction operations; and (2) allows the visualization of several other construction processes that were hitherto impossible to animate accurately in 3D. DOI: 10.1061/(ASCE)0887-3801(2005)19:2(160) CE Database subject headings: Computer graphics; Construction sites; Digital mapping; Simulation; Terrain models; Imaging techniques.

The spatial prediction and simulation of point values from areal data are addressed within the general geostatistical framework of change of support (the term support referring to the domain informed [...]

Surface networks capture the topological relations between passes of a continuous surface, the paths of steepest descent and ascent starting at the passes, and the pits and peaks where the [...]

Airborne laser scanning (ALS) is becoming an increasingly popular data capture technique for a variety of applications in urban surface modeling. Raw ALS data are captured and supplied as a [...]

This article introduces a procedure for progressively increasing the density of an initial point set that can be used as a basis for interpolating surfaces of variable resolution from sparse [...]

The mathematics and the metaphysics, Fall to them as you find your stomach serves you; No profit grows where is no pleasure ta'en: In brief, sir, study what you most [...]

Now to ensure the quality of automatic extraction of buildings from aerial and space imagery is not comprehensively understood. Dedicated efforts are needed in examining both the theoretical and practical aspects of the problem. This paper first presents a review of building extraction research activities, with the focus on quality-related issues. Also addressed is recent progress in the use of high-resolution satellite images. As a theoretical study, a set of five quality measures of the extracted buildings is proposed that considers all possible types of errors and describes the accuracy of building extraction relative to the ground truth. To achieve a reliable and precision result, a step-by-step quality control strategy is implemented in a classification-guided building extraction procedure. Unreliable results are identified based on the proposed quality index and excluded from further processing. Empirical results using IKONOS images are presented to demonstrate the proposed quality measures and quality control strategy. CE Database subject headings: Imaging techniques: Satellites; Photogrammetry; Digital mapping; Quality control.

Coordinate transformations refer to mathematical processing that enables overlay of digital maps that use different coordinate reference systems, that is, map projections. The transformation from geographical to map (plane) coordinates is the conventional practice in cartography, which is called forward transformation. The inverse transformation, which yields geographical coordinates from map coordinates, is a more recent development, due to the need for transformation between different map projections, especially in geographic information systems (GIS). The combination of the inverse and forward transformation from one projection to another, which may be called grid-on-grid or map-to-map transformation, can be necessary for some custom applications in GIS and in automated cartography. Many different approximation algorithms can be used for this problem on desktop computers. In this paper a new local transformation method called a non-Sibsonian transformation, which uses non-Sibson local coordinates, is suggested for map-to-map transformation or for improving geometrical accuracy of scanned maps. A case study is performed using the Lambert conformal conic projection and is presented with results. CE Database subject headings: Transformation; Digital mapping; Geographic information systems; Accuracy; Surveys.

A multidimensional, mapping based complex wavelet is introduced that consists of a mapping onto a complex function space followed by a discrete wavelet transform (DWT) of the complex mapping. Mapping-based complex wavelet transforms in seismic signal-processing applications the state of art results is demonstrated by exploiting the advantages of the multidimensional.

ABSTRACT: Most line processing algorithms developed so far in cartographic generalization focus on polygonal curves (or polylines). This representation model is sometimes not sufficient for certain processes due to its [...]

A lossless, context-based procedure is proposed for content progressive coding of images such as digital maps.

In this paper, we propose a novel approach to generating topology preserving mapping of structural shapes using the self-organizing maps (SOM). The structural information of the geometrical shapes is captured by the relational vectors. These relational attribute vectors are quantised using an SOM. Using this quantization SOM, a histogram is generated for every shape. These histograms are treated as inputs to train another SOM which yields a topology preserving mapping of the geometric shapes. By appropriately choosing the relational vectors, it is possible to generate the mapping invariant to some chosen transformations such as rotation, translation, scale, affine, or perspective. Experimental results using trademark objects are presented to demonstrate the performance of the proposed methodology. Index Terms--Attributed relational graphs, pairwise geometric histograms, relational attribute vectors, self-organizing maps, shape indexing, shape recognition, shape retrieval, structural databases, topology conserving mapping.

A model for spatial navigation that includes an action system capable of guiding the search for specific goals with the assistance of a cognitive map is applied. The system efficiently and completely explores the environment and once the environment is mapped, the action system can plan the best route to navigate between places.

Research is presented concerning the development of a multi-resolution image representation method which is wavelet-based. This method is capable of matching the spacial acuity of human visual systems within many regions of interest.

It is well known that high-dimensional image data allows for the separation of classes that are spectrally very similar, i.e., possess nearly equal first-order statistics, provided that their second-order statistics differ significantly. The aim of this study is to contribute to a better understanding, from a more geometrically oriented point of view, of the role played by the second-order statistics in remote sensing digital image classification of natural scenes when the classes of interest are spectrally very similar and high dimensional multispectral image data is available. A number of the investigations that have been developed in this area deal with the fact that as the data dimensionality increases, so does the difficulty in obtaining a reasonably accurate estimate of the within-class covariance matrices from the number of available labeled samples, which is usually limited. Several approaches have been proposed to deal with this problem. This study aims toward a complementary goal. Assuming that reasonably accurate estimates for the within-class covariance matrices have been obtained, we seek to better understand what kind of geometrically-oriented interpretation can be given to them as the data dimensionality increases and also to understand how this knowledge can help the design of a classifier. In order to achieve this goal, the covariance matrix is decomposed into a number of parameters that are then analyzed separately with respect to their ability to separate the classes. Methods for image classification based on these parameters are investigated. Results of tests using data provided by the sensor system AVIRIS are presented and discussed. Index Terms: AVIRIS sensor, digital image classification, high-dimensional data, remote sensing, second-order statistics.

This paper shows how to segment large data sets of multitemporal and interferometric SAR images using an unsupervised, fuzzy clustering method. An adaptive feature extraction (principal component transformation) is employed which may drastically reduce the number of images and improves the final results. This also speeds up the fuzzy clustering iteration part considerably. The method is applied to data over two areas in Sweden: one typical urban area with forest and farmland surroundings and a forested area. The best classification accuracy is obtained when classifying the data into two classes, agreeing with the predictions of the cluster validity parameters used in this study. The method always finds the dominating land-covers in the images first. These are then subdivided as more clusters (classes) are identified, indicating that the segmentation is moderately hierarchical. The final classification results, between 65% and 75%, are comparable to those obtained in other studies. Analyzing the final cluster signatures reveals that the current unsupervised method has several similarities with rule-based methods. Index Terms: Classification, fuzzy clustering, multitemporal, SAR, SAR interferometry, unsupervised image segmentation.

In this paper, we propose a new method for unsupervised classification of terrain types and man-made objects using polarimetric synthetic aperture radar (SAR) data. This technique is a combination of the unsupervised classification based on polarimetric target decomposition [9] and the maximum likelihood classifier based on the complex Wishart distribution for the polarimetric covariance matrix [15]. We use Cloude and Pottier's method to initially classify the polarimetric SAR image. The initial classification map defines training sets for classification based on the Wishart distribution. The classified results are then used to define training sets for the next iteration. Significant improvement has been observed in iteration. The iteration ends when the number of pixels switching classes becomes smaller than a predetermined number or when other criteria are met. We observed that the class centers in the entropy-alpha plane are shifted by each iteration. The final class centers in the entropy-alpha plane are useful for class identification by the scattering mechanism associated with each zone. The advantages of this method are the automated classification, and the interpretation of each class based on scattering mechanism. The effectiveness of this algorithm is demonstrated using a JPL/AIRSAR polarimetric SAR image. Index Terms: Radar polarimetry, synthetic aperture radar, terrain classification

In the 1960's, stereoscopic methods were first applied to radar images to derive ground elevation. Unfortunately, research uncovered contradictions between error propagation theory and practical results. These contradictions combined with the lack of stereo radar pairs led to the decline of radargrammetry. The launch in 1995 of Canada's first earth observation satellite, RADARSAT, with its various operating modes and specific geometric characteristics, has turned the tide. The error propagation of the radargrammetric DEM generated from different RADARSAT stereo configurations is then quantitatively evaluated along the full processing chain (stereo model set-up with ground control points (GCP's), image matching and threedimensional (3-D) intersection). Two matching algorithms are used: 1) automatic and 2) computer-assisted visual matching. The GCP collection method using stereoscopic plotting is a requisite to achieve the best results for the stereo model and DEM. The automatic matching gives slightly better results than the computer-assisted visual matching, except when the radiometric disparities in the stereo images are too large. Since the geometric advantage (not involved in the automatic matching) can compensate for the radiometric disadvantage, visual matching, which combines both aspects, is a better method in these conditions. Consequently, these two algorithms can be used in a two-step method to generate the best DEM whatever the stereo configuration. Since the relief is an important parameter in the final accuracy, geometric versus radiometric disparity tradeoffs and general guidelines are suggested for selecting RADARSAT stereo pairs for DEM generation as a function of terrain relief. Index Terms: DEM, error analysis, error tracking, radar, radargrammetry, RADARSAT, stereo, terrain mapping.

The coding of dynamic texture for mapping on 3-D scenes is studied. Dynamic texture can be coded taking into consideration the viewpoint of the user and the viewing conditions which involves coding and transmitting each texture's part using the required resolution. The use of dynamic texture coding in three-dimensional games (3-D), 3-D simulations of dynamic systems and walkthroughs of dynamic constructions that is based on an adapted optimal level allocation video codec is presented.

Mutants of Chlorella sorokiniana lacking the chloroplast synthase CF0CF1 ATP synthase were isolated using a new method of delayed luminiscence. Characterization of these strains using biochemical methods reveals a lack of detectable synthesis and accumulation of the ATP synthase subunits alpha-CF1 and beta-CF1. Functional characterization also shows the presence of an electrochemical gradient across the thylakoid membrane in the dark-adapted state. This electrochemical gradient, however, is not suppressed under anaerobic conditions.

Buried archaeological features can be mapped with ground-penetrating radar (GPR) and three-dimensional computer mapping. The new methods have been tested at sites in the Southwestern region of the US with good results. Sometimes buried features were not found by the GPR surveys due to factors such as soil conditions, disturbance of the surface, and calibration of equipment, which future GPR surveys could take into account.

This paper proposes a two-stage global motion parameter estimation method using block-based motion vectors and a model failure (MF) object compensation algorithm by object-oriented fractal mapping of the residual image for an object-oriented coder. In the first stage of the two-stage motion parameter estimation algorithm, coarse motion parameters are estimated by fitting block-based motion vectors computed hierarchically to a six-parameter model, and in the second stage, the estimated motion parameters are refined by the gradient method using an image reconstructed by motion parameters detected in the first stage. The local prediction error by the six-parameter method is locally reduced by blockwise motion parameter correction using the residual image. Finally, the MF object is compensated by object-oriented fractal mapping of the previous residual image into the current one, in which geometric affine mapping is followed by the massic transformation. For MF object compensation, selection between motion parameter compensation and fractal mapping is achieved by the validity test. Computer simulation results show that the proposed method gives better performance than the conventional ones in terms of the peak signal-to-noise ratio (PSNR) and compression ratio (CR). Index Terms - Affine mapping, fractal mapping, global motion parameter estimation, model failure (MF) objects, motion vectors, object-oriented coding, six-parameter model.

Maps of spin lattice relaxation time ([T.sub.1]) can be reconstructed directly from magnetic resonance imaging (MRI) k-space data measured with very short data acquisition times, e.g., a data set for a 128 x 128[T.sub.1] map can be acquired in less than 3 s using gradients with 10-T/m/s slew rate. In principle, this approach could be extended to quantitate other MRI parameters but current use is limited by the lack of precise, accurate and fast reconstruction. Using theoretical calculations, computer simulations, and experiments we have optimized a parametric reconstruction method using a Leverberg-Marquardt (L-M) algorithm and compared it to the quasi-Newton method originally used. We have found significant improvement using the L-M method provided [T.sub.1] is solved for directly without linearization. Reconstruction time was reduced by a factor of 60. Computer simulations show that the method has acceptable accuracy even in signals with 5% noise. Optimization included the investigation of the signal-to-noise (S/N) of each k-space data point and its impact on relative error of the reconstruction. This result indicates that rectangular k-space data could be collected for further reduction of data acquisition times. Determination of [T.sub.1] maps by direct parametric reconstruction of k-space data appears feasible and may stimulate further application of quantitative MRI. Index Terms - MRI, non-Fourier k-space reconstruction, parametric reconstruction, ([T.sub.1]) map.