3D minutiae extraction in 3D fingerprint scans.

Traditionally, fingerprint image acquisition was based on contact. However the conventional touch-based fingerprint acquisition introduces some problems such as distortions and deformations to the fingerprint image. The most recent technology for fingerprint acquisition is touchless or 3D live scans introducing higher quality fingerprint scans. However, there is a need to develop new algorithms to match 3D fingerprints. In this dissertation, a novel methodology is proposed to extract minutiae in the 3D fingerprint scans. The output can be used for 3D fingerprint matching. The proposed method is based on curvature analysis of the surface. The method used to extract minutiae includes the following steps: smoothing; computing the principal curvature; ridges and ravines detection and tracing; cleaning and connecting ridges and ravines; and minutiae detection. First, the ridges and ravines are detected using curvature tensors. Then, ridges and ravines are traced. Post-processing is performed to obtain clean and connected ridges and ravines based on fingerprint pattern. Finally, minutiae are detected using a graph theory concept. A quality map is also introduced for 3D fingerprint scans. Since a degraded area may occur during the scanning process, especially at the edge of the fingerprint, it is critical to be able to determine these areas. Spurious minutiae can be filtered out after applying the quality map. The algorithm is applied to the 3D fingerprint database and the result is very encouraging. To the best of our knowledge, this is the first minutiae extraction methodology proposed for 3D fingerprint scans

System of Terrain Analysis, Energy Estimation and Path Planning for Planetary Exploration by Robot Teams

NASA’s long term plans involve a return to manned moon missions, and eventually sending humans to mars. The focus of this project is the use of autonomous mobile robotics to enhance these endeavors. This research details the creation of a system of terrain classification, energy of traversal estimation and low cost path planning for teams of inexpensive and potentially expendable robots. The first stage of this project was the creation of a model which estimates the energy requirements of the traversal of varying terrain types for a six wheel rocker-bogie rover. The wheel/soil interaction model uses Shibly’s modified Bekker equations and incorporates a new simplified rocker-bogie model for estimating wheel loads. In all but a single trial the relative energy requirements for each soil type were correctly predicted by the model. A path planner for complete coverage intended to minimize energy consumption was designed and tested. It accepts as input terrain maps detailing the energy consumption required to move to each adjacent location. Exploration is performed via a cost function which determines the robot’s next move. This system was successfully tested for multiple robots by means of a shared exploration map. At peak efficiency, the energy consumed by our path planner was only 56% that used by the best case back and forth coverage pattern. After performing a sensitivity analysis of Shibly’s equations to determine which soil parameters most affected energy consumption, a neural network terrain classifier was designed and tested. The terrain classifier defines all traversable terrain as one of three soil types and then assigns an assumed set of soil parameters. The classifier performed well over all, but had some difficulty distinguishing large rocks from sand. This work presents a system which successfully classifies terrain imagery into one of three soil types, assesses the energy requirements of terrain traversal for these soil types and plans efficient paths of complete coverage for the imaged area. While there are further efforts that can be made in all areas, the work achieves its stated goals

Tracking Dynamic Features in Image Sequences.

This dissertation deals with detecting and tracking dynamic features in image sequences using digital image analysis algorithms. The tracking problem is complicated in oceanographic images due to the dynamic nature of the features. Specifically, the features of interest move, change size and shape. In the first part of the dissertation, the design and development of a new segmentation algorithm, Histogram-based Morphological Edge Detector (HMED), is presented. Mathematical morphology has been used in the past to develop efficient and robust edge detectors. But these morphological edge detectors do not extract weak gradient edge pixels, and they introduce spurious edge pixels. The primary reason for this is due to the fact that the morphological operations are defined in the domain of a pixel\u27s neighborhood. HMED defines new operations, namely H-dilation and H-erosion, which are defined in the domain of the histogram of the pixel\u27s neighborhood. The motivation for incorporating the histogram into the dilation and erosion is primarily due to the rich information content in the histogram compared to the one available in the pixel\u27s neighborhood. As a result, HMED extracts weak gradient pixels while suppressing the spurious edge pixels. An extensive comparison of all morphological edge detectors in the context of oceanographic digital images is also presented. In the second part of the dissertation, a new augmented region and edge segmentation technique for the interpretation of oceanographic features present in the AVHRR image is presented. The augmented technique uses a topography-based method that extracts topolographical labels such as concave, convex and flat pixels from the image. In this technique, first a bicubic polynomial is fitted to a pixel and its neighborhood, and topolographical label is assigned based on the first and second directional derivatives of the polynomial surface. Second, these labeled pixels are grouped and assembled into edges and regions. The augmented technique blends the edge and region information on a proximity based criterion to detect the features. A number of experimental results are also provided to show the significant improvement in tracking the features using the augmented technique over other previously designed techniques

Automatic road network extraction from high resolution satellite imagery using spectral classification methods

Road networks play an important role in a number of geospatial applications, such as cartographic, infrastructure planning and traffic routing software. Automatic and semi-automatic road network extraction techniques have significantly increased the extraction rate of road networks. Automated processes still yield some erroneous and incomplete results and costly human intervention is still required to evaluate results and correct errors. With the aim of improving the accuracy of road extraction systems, three objectives are defined in this thesis: Firstly, the study seeks to develop a flexible semi-automated road extraction system, capable of extracting roads from QuickBird satellite imagery. The second objective is to integrate a variety of algorithms within the road network extraction system. The benefits of using each of these algorithms within the proposed road extraction system, is illustrated. Finally, a fully automated system is proposed by incorporating a number of the algorithms investigated throughout the thesis. CopyrightDissertation (MSc)--University of Pretoria, 2010.Computer Scienceunrestricte

Vegetation classification and mapping project report: Great Sand Dunes National Park and Preserve

NPS 140/101150.February 2010.Natural Resource Report NPS/ROMN/NRR--2010/179.Includes bibliographical references

Genetic terrain programming

Dissertação apresentada à Universidad de Extremadura para obtenção do Diploma de Estudios Avanzados, orientada por Francisco Fernandéz de Vega e Carlos Cotta.Nowadays there are a wide range of techniques for terrain generation, but all of them are focused on providing realistic terrains, often neglecting other aspects (e.g., aesthetic appeal or presence of desired features). This thesis presents a new technique, GTP (Genetic Terrain Programming), based on evolutionary design with Genetic Programming. The GTP technique consists of a guided evolution, by means of Interactive Evolution, accordingly to a speci c desired terrain feature or aesthetic appeal. This technique can yield both aesthetic and real TPs (Terrain Programmes) which are capable of gen- erating di erent terrains, but consistently with the same features. TPs are also scale invariant, meaning that terrain features will be preserved across di erent LODs (Levels Of Details), which allows the use of low LODs dur- ing the evolutionary phase without compromising results. Additionally, the resulting TPs can be incorporated in video games, like any other procedural technique, to generate terrains. Furthermore, by way of resorting to several TPs to compose the full landscape, it is possible to control some localised terrain features, thus eliminating the main drawback of traditional procedu- ral techniques. The combination of GP with evolutionary art systems also diminish the e ort and time required to create complex terrains when com- pared to modeling techniques. Moreover, the results are not dependent on the designer's skills

The Effects of Impervious Surfaces and Forests on Water Quality in a Southern Appalachian Headwater Catchment: A Geospatial Modeling Approach

The water quality of streams is impacted by the land cover types that occur within their watersheds and stream corridors. Research has indicated that impervious surfaces (roads, roofs, and parking lots) exert significant stress on stream system health by increasing storm runoff and transporting pollutants into streams. Forests, on the other hand, serve to protect water quality by slowing runoff, which allows rainfall to percolate into the ground, and absorbing pollutants. This thesis research examined the effects of impervious surfaces and forests on water quality in the headwaters of the New River in Watauga County. Results demonstrated that these effects are clearly identifiable and statistically significant. Limiting the amount of impervious surfaces that occur within 100 meters of streams and establishing 50 meter forested stream buffer zones could improve water quality and help preserve stream system health

Shape classification: towards a mathematical description of the face

Recent advances in biostereometric techniques have led to the quick and easy acquisition of 3D data for facial and other biological surfaces. This has led facial surgeons to express dissatisfaction with landmark-based methods for analysing the shape of the face which use only a small part of the data available, and to seek a method for analysing the face which maximizes the use of this extensive data set. Scientists working in the field of computer vision have developed a variety of methods for the analysis and description of 2D and 3D shape. These methods are reviewed and an approach, based on differential geometry, is selected for the description of facial shape. For each data point, the Gaussian and mean curvatures of the surface are calculated. The performance of three algorithms for computing these curvatures are evaluated for mathematically generated standard 3D objects and for 3D data obtained from an optical surface scanner. Using the signs of these curvatures, the face is classified into eight 'fundamental surface types' - each of which has an intuitive perceptual meaning. The robustness of the resulting surface type description to errors in the data is determined together with its repeatability. Three methods for comparing two surface type descriptions are presented and illustrated for average male and average female faces. Thus a quantitative description of facial change, or differences between individual's faces, is achieved. The possible application of artificial intelligence techniques to automate this comparison is discussed. The sensitivity of the description to global and local changes to the data, made by mathematical functions, is investigated. Examples are given of the application of this method for describing facial changes made by facial reconstructive surgery and implications for defining a basis for facial aesthetics using shape are discussed. It is also applied to investigate the role played by the shape of the surface in facial recognition