Showing papers on "Feature (computer vision) published in 2007"

Computational Methods of Feature Selection

Abstract: PREFACE Introduction and Background Less Is More Huan Liu and Hiroshi Motoda Background and Basics Supervised, Unsupervised, and Semi-Supervised Feature Selection Key Contributions and Organization of the Book Looking Ahead Unsupervised Feature Selection Jennifer G. Dy Introduction Clustering Feature Selection Feature Selection for Unlabeled Data Local Approaches Summary Randomized Feature Selection David J. Stracuzzi Introduction Types of Randomizations Randomized Complexity Classes Applying Randomization to Feature Selection The Role of Heuristics Examples of Randomized Selection Algorithms Issues in Randomization Summary Causal Feature Selection Isabelle Guyon, Constantin Aliferis, and Andre Elisseeff Introduction Classical "Non-Causal" Feature Selection The Concept of Causality Feature Relevance in Bayesian Networks Causal Discovery Algorithms Examples of Applications Summary, Conclusions, and Open Problems Extending Feature Selection Active Learning of Feature Relevance Emanuele Olivetti, Sriharsha Veeramachaneni, and Paolo Avesani Introduction Active Sampling for Feature Relevance Estimation Derivation of the Sampling Benefit Function Implementation of the Active Sampling Algorithm Experiments Conclusions and Future Work A Study of Feature Extraction Techniques Based on Decision Border Estimate Claudia Diamantini and Domenico Potena Introduction Feature Extraction Based on Decision Boundary Generalities about Labeled Vector Quantizers Feature Extraction Based on Vector Quantizers Experiments Conclusions Ensemble-Based Variable Selection Using Independent Probes Eugene Tuv, Alexander Borisov, and Kari Torkkola Introduction Tree Ensemble Methods in Feature Ranking The Algorithm: Ensemble-Based Ranking against Independent Probes Experiments Discussion Efficient Incremental-Ranked Feature Selection in Massive Data Roberto Ruiz, Jesus S. Aguilar-Ruiz, and Jose C. Riquelme Introduction Related Work Preliminary Concepts Incremental Performance over Ranking Experimental Results Conclusions Weighting and Local Methods Non-Myopic Feature Quality Evaluation with (R)ReliefF Igor Kononenko and Marko Robnik Sikonja Introduction From Impurity to Relief ReliefF for Classification and RReliefF for Regression Extensions Interpretation Implementation Issues Applications Conclusion Weighting Method for Feature Selection in k-Means Joshua Zhexue Huang, Jun Xu, Michael Ng, and Yunming Ye Introduction Feature Weighting in k-Means W-k-Means Clustering Algorithm Feature Selection Subspace Clustering with k-Means Text Clustering Related Work Discussions Local Feature Selection for Classification Carlotta Domeniconi and Dimitrios Gunopulos Introduction The Curse of Dimensionality Adaptive Metric Techniques Large Margin nearest Neighbor Classifiers Experimental Comparisons Conclusions Feature Weighting through Local Learning Yijun Sun Introduction Mathematical Interpretation of Relief Iterative Relief Algorithm Extension to Multiclass Problems Online Learning Computational Complexity Experiments Conclusion Text Classification and Clustering Feature Selection for Text Classification George Forman Introduction Text Feature Generators Feature Filtering for Classification Practical and Scalable Computation A Case Study Conclusion and Future Work A Bayesian Feature Selection Score Based on Naive Bayes Models Susana Eyheramendy and David Madigan Introduction Feature Selection Scores Classification Algorithms Experimental Settings and Results Conclusion Pairwise Constraints-Guided Dimensionality Reduction Wei Tang and Shi Zhong Introduction Pairwise Constraints-Guided Feature Projection Pairwise Constraints-Guided Co-Clustering Experimental Studies Conclusion and Future Work Aggressive Feature Selection by Feature Ranking Masoud Makrehchi and Mohamed S. Kamel Introduction Feature Selection by Feature Ranking Proposed Approach to Reducing Term Redundancy Experimental Results Summary Feature Selection in Bioinformatics Feature Selection for Genomic Data Analysis Lei Yu Introduction Redundancy-Based Feature Selection Empirical Study Summary A Feature Generation Algorithm with Applications to Biological Sequence Classification Rezarta Islamaj Dogan, Lise Getoor, and W. John Wilbur Introduction Splice-Site Prediction Feature Generation Algorithm Experiments and Discussion Conclusions An Ensemble Method for Identifying Robust Features for Biomarker Discovery Diana Chan, Susan M. Bridges, and Shane C. Burgess Introduction Biomarker Discovery from Proteome Profiles Challenges of Biomarker Identification Ensemble Method for Feature Selection Feature Selection Ensemble Results and Discussion Conclusion Model Building and Feature Selection with Genomic Data Hui Zou and Trevor Hastie Introduction Ridge Regression, Lasso, and Bridge Drawbacks of the Lasso The Elastic Net The Elastic-Net Penalized SVM Sparse Eigen-Genes Summary INDEX

The central fixation bias in scene viewing: selecting an optimal viewing position independently of motor biases and image feature distributions.

Abstract: Observers show a marked tendency to fixate the center of the screen when viewing scenes on computer monitors. This is often assumed to arise because image features tend to be biased toward the center of natural images and fixations are correlated with image features. A common alternative explanation is that experiments typically use a central pre-trial fixation marker, and observers tend to make small amplitude saccades. In the present study, the central bias was explored by dividing images post hoc according to biases in their image feature distributions. Central biases could not be explained by motor biases for making small saccades and were found irrespective of the distribution of image features. When the scene appeared, the initial response was to orient to the center of the screen. Following this, fixation distributions did not vary with image feature distributions when freely viewing scenes. When searching the scenes, fixation distributions shifted slightly toward the distribution of features in the image, primarily during the first few fixations after the initial orienting response. The endurance of the central fixation bias irrespective of the distribution of image features, or the observer's task, implies one of three possible explanations: First, the center of the screen may be an optimal location for early information processing of the scene. Second, it may simply be that the center of the screen is a convenient location from which to start oculomotor exploration of the scene. Third, it may be that the central bias reflects a tendency to re-center the eye in its orbit.

Sharing Visual Features for Multiclass and Multiview Object Detection

Abstract: We consider the problem of detecting a large number of different classes of objects in cluttered scenes. Traditional approaches require applying a battery of different classifiers to the image, at multiple locations and scales. This can be slow and can require a lot of training data since each classifier requires the computation of many different image features. In particular, for independently trained detectors, the (runtime) computational complexity and the (training-time) sample complexity scale linearly with the number of classes to be detected. We present a multitask learning procedure, based on boosted decision stumps, that reduces the computational and sample complexity by finding common features that can be shared across the classes (and/or views). The detectors for each class are trained jointly, rather than independently. For a given performance level, the total number of features required and, therefore, the runtime cost of the classifier, is observed to scale approximately logarithmically with the number of classes. The features selected by joint training are generic edge-like features, whereas the features chosen by training each class separately tend to be more object-specific. The generic features generalize better and considerably reduce the computational cost of multiclass object detection

Stability of feature selection algorithms: a study on high-dimensional spaces

Abstract: With the proliferation of extremely high-dimensional data, feature selection algorithms have become indispensable components of the learning process Strangely, despite extensive work on the stability of learning algorithms, the stability of feature selection algorithms has been relatively neglected This study is an attempt to fill that gap by quantifying the sensitivity of feature selection algorithms to variations in the training set We assess the stability of feature selection algorithms based on the stability of the feature preferences that they express in the form of weights-scores, ranks, or a selected feature subset We examine a number of measures to quantify the stability of feature preferences and propose an empirical way to estimate them We perform a series of experiments with several feature selection algorithms on a set of proteomics datasets The experiments allow us to explore the merits of each stability measure and create stability profiles of the feature selection algorithms Finally, we show how stability profiles can support the choice of a feature selection algorithm

Evaluation of Features Detectors and Descriptors based on 3D Objects

Abstract: We explore the performance of a number of popular feature detectors and descriptors in matching 3D object features across viewpoints and lighting conditions. To this end we design a method, based on intersecting epipolar constraints, for providing ground truth correspondence automatically. These correspondences are based purely on geometric information, and do not rely on the choice of a specific feature appearance descriptor. We test detector-descriptor combinations on a database of 100 objects viewed from 144 calibrated viewpoints under three different lighting conditions. We find that the combination of Hessian-affine feature finder and SIFT features is most robust to viewpoint change. Harris-affine combined with SIFT and Hessian-affine combined with shape context descriptors were best respectively for lighting change and change in camera focal length. We also find that no detector-descriptor combination performs well with viewpoint changes of more than 25---30?.

Feature Location Using Probabilistic Ranking of Methods Based on Execution Scenarios and Information Retrieval

Abstract: This paper recasts the problem of feature location in source code as a decision-making problem in the presence of uncertainty. The solution to the problem is formulated as a combination of the opinions of different experts. The experts in this work are two existing techniques for feature location: a scenario-based probabilistic ranking of events and an information-retrieval-based technique that uses latent semantic indexing. The combination of these two experts is empirically evaluated through several case studies, which use the source code of the Mozilla Web browser and the Eclipse integrated development environment. The results show that the combination of experts significantly improves the effectiveness of feature location as compared to each of the experts used independently

Merging Markov and DCT Features for Multi-Class JPEG Steganalysis

Abstract: Blind steganalysis based on classifying feature vectors derived from images is becoming increasingly more powerful. For steganalysis of JPEG images, features derived directly in the embedding domain from DCT coefficients appear to achieve the best performance (e.g., the DCT features10 and Markov features21). The goal of this paper is to construct a new multi-class JPEG steganalyzer with markedly improved performance. We do so first by extending the 23 DCT feature set,10 then applying calibration to the Markov features described in21 and reducing their dimension. The resulting feature sets are merged, producing a 274-dimensional feature vector. The new feature set is then used to construct a Support Vector Machine multi-classifier capable of assigning stego images to six popular steganographic algorithms-F5,22 OutGuess,18 Model Based Steganography without ,19 and with20 deblocking, JP Hide&Seek,1 and Steghide.14 Comparing to our previous work on multi-classification,11, 12 the new feature set provides significantly more reliable results.

Wrapper–Filter Feature Selection Algorithm Using a Memetic Framework

Abstract: This correspondence presents a novel hybrid wrapper and filter feature selection algorithm for a classification problem using a memetic framework. It incorporates a filter ranking method in the traditional genetic algorithm to improve classification performance and accelerate the search in identifying the core feature subsets. Particularly, the method adds or deletes a feature from a candidate feature subset based on the univariate feature ranking information. This empirical study on commonly used data sets from the University of California, Irvine repository and microarray data sets shows that the proposed method outperforms existing methods in terms of classification accuracy, number of selected features, and computational efficiency. Furthermore, we investigate several major issues of memetic algorithm (MA) to identify a good balance between local search and genetic search so as to maximize search quality and efficiency in the hybrid filter and wrapper MA

Linear feature-based models for information retrieval

Abstract: There have been a number of linear, feature-based models proposed by the information retrieval community recently. Although each model is presented differently, they all share a common underlying framework. In this paper, we explore and discuss the theoretical issues of this framework, including a novel look at the parameter space. We then detail supervised training algorithms that directly maximize the evaluation metric under consideration, such as mean average precision. We present results that show training models in this way can lead to significantly better test set performance compared to other training methods that do not directly maximize the metric. Finally, we show that linear feature-based models can consistently and significantly outperform current state of the art retrieval models with the correct choice of features.

A stability index for feature selection

Abstract: Sequential forward selection (SFS) is one of the most widely used feature selection procedures. It starts with an empty set and adds one feature at each step. The estimate of the quality of the candidate subsets usually depends on the training/testing split of the data. Therefore different sequences of features may be returned from repeated runs of SFS. A substantial discrepancy between such sequences will signal a problem with the selection. A stability index is proposed here based on cardinality of the intersection and a correction for chance. The experimental results with 10 real data sets indicate that the index can be useful for selecting the final feature subset. If stability is high, then we should return a subset of features based on their total rank across the SFS runs. If stability is low, then it is better to return the feature subset which gave the minimum error across all SFS runs.

Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry

Abstract: High-resolution photomicrographs of phytoplankton cells and chains can now be acquired with imaging-in-flow systems at rates that make manual identification impractical for many applications. To address the challenge for automated taxonomic identification of images generated by our custom-built submersible Imaging FlowCytobot, we developed an approach that relies on extraction of image features, which are then presented to a machine learning algorithm for classification. Our approach uses a combination of image feature types including size, shape, symmetry, and texture characteristics, plus orientation invariant moments, diffraction pattern sampling, and co-occurrence matrix statistics. Some of these features required preprocessing with image analysis techniques including edge detection after phase congruency calculations, morphological operations, boundary representation and simplification, and rotation. For the machine learning strategy, we developed an approach that combines a feature selection algorithm and use of a support vector machine specified with a rigorous parameter selection and training approach. After training, a 22-category classifier provides 88% overall accuracy for an independent test set, with individual category accuracies ranging from 68% to 99%. We demonstrate application of this classifier to a nearly uninterrupted 2-month time series of images acquired in Woods Hole Harbor, including use of statistical error correction to derive quantitative concentration estimates, which are shown to be unbiased with respect to manual estimates for random subsamples. Our approach, which provides taxonomically resolved estimates of phytoplankton abundance with fine temporal resolution (hours for many species), permits access to scales of variability from tidal to seasonal and longer.

Human Ear Recognition in 3D

Abstract: Human ear is a new class of relatively stable biometrics that has drawn researchers' attention recently. In this paper, we propose a complete human recognition system using 3D ear biometrics. The system consists of 3D ear detection, 3D ear identification, and 3D ear verification. For ear detection, we propose a new approach which uses a single reference 3D ear shape model and locates the ear helix and the antihelix parts in registered 2D color and 3D range images. For ear identification and verification using range images, two new representations are proposed. These include the ear helix/antihelix representation obtained from the detection algorithm and the local surface patch (LSP) representation computed at feature points. A local surface descriptor is characterized by a centroid, a local surface type, and a 2D histogram. The 2D histogram shows the frequency of occurrence of shape index values versus the angles between the normal of reference feature point and that of its neighbors. Both shape representations are used to estimate the initial rigid transformation between a gallery-probe pair. This transformation is applied to selected locations of ears in the gallery set and a modified iterative closest point (ICP) algorithm is used to iteratively refine the transformation to bring the gallery ear and probe ear into the best alignment in the sense of the least root mean square error. The experimental results on the UCR data set of 155 subjects with 902 images under pose variations and the University of Notre Dame data set of 302 subjects with time-lapse gallery-probe pairs are presented to compare and demonstrate the effectiveness of the proposed algorithms and the system

Feature Diagrams and Logics: There and Back Again

Abstract: Feature modeling is a notation and an approach for modeling commonality and variability in product families. In their basic form, feature models contain mandatory/optional features, feature groups, and implies and excludes relationships. It is known that such feature models can be translated into propositional formulas, which enables the analysis and configuration using existing logic- based tools. In this paper, we consider the opposite translation problem, that is, the extraction of feature models from propositional formulas. We give an automatic and efficient procedure for computing a feature model from a formula. As a side effect we characterize a class of logical formulas equivalent to feature models and identify logical structures corresponding to their syntactic elements. While many different feature models can be extracted from a single formula, the computed model strives to expose graphically the maximum of the original logical structure while minimizing redundancies in the representation. The presented work furthers our understanding of the semantics of feature modeling and its relation to logics, opening avenues for new applications in reverse engineering and refactoring of feature models.

Adaptive boosting for SAR automatic target recognition

Abstract: The paper proposed a novel automatic target recognition (ATR) system for classification of three types of ground vehicles in the moving and stationary target acquisition and recognition (MSTAR) public release database. First MSTAR image chips are represented as fine and raw feature vectors, where raw features compensate for the target pose estimation error that corrupts fine image features. Then, the chips are classified by using the adaptive boosting (AdaBoost) algorithm with the radial basis function (RBF) network as the base learner. Since the RBF network is a binary classifier, the multiclass problem was decomposed into a set of binary ones through the error-correcting output codes (ECOC) method, specifying a dictionary of code words for the set of three possible classes. AdaBoost combines the classification results of the RBF network for each binary problem into a code word, which is then "decoded" as one of the code words (i.e., ground-vehicle classes) in the specified dictionary. Along with classification, within the AdaBoost framework, we also conduct efficient fusion of the fine and raw image-feature vectors. The results of large-scale experiments demonstrate that our ATR scheme outperforms the state-of-the-art systems reported in the literature

From Template to Image: Reconstructing Fingerprints from Minutiae Points

Abstract: Most fingerprint-based biometric systems store the minutiae template of a user in the database. It has been traditionally assumed that the minutiae template of a user does not reveal any information about the original fingerprint. In this paper, we challenge this notion and show that three levels of information about the parent fingerprint can be elicited from the minutiae template alone, viz., 1) the orientation field information, 2) the class or type information, and 3) the friction ridge structure. The orientation estimation algorithm determines the direction of local ridges using the evidence of minutiae triplets. The estimated orientation field, along with the given minutiae distribution, is then used to predict the class of the fingerprint. Finally, the ridge structure of the parent fingerprint is generated using streamlines that are based on the estimated orientation field. Line integral convolution is used to impart texture to the ensuing ridges, resulting in a ridge map resembling the parent fingerprint. The salient feature of this noniterative method to generate ridges is its ability to preserve the minutiae at specified locations in the reconstructed ridge map. Experiments using a commercial fingerprint matcher suggest that the reconstructed ridge structure bears close resemblance to the parent fingerprint

Maximally Stable Colour Regions for Recognition and Matching

Abstract: This paper introduces a novel colour-based affine co-variant region detector. Our algorithm is an extension of the maximally stable extremal region (MSER) to colour. The extension to colour is done by looking at successive time-steps of an agglomerative clustering of image pixels. The selection of time-steps is stabilised against intensity scalings and image blur by modelling the distribution of edge magnitudes. The algorithm contains a novel edge significance measure based on a Poisson image noise model, which we show performs better than the commonly used Euclidean distance. We compare our algorithm to the original MSER detector and a competing colour-based blob feature detector, and show through a repeatability test that our detector performs better. We also extend the state of the art in feature repeatability tests, by using scenes consisting of two planes where one is piecewise transparent. This new test is able to evaluate how stable a feature is against changing backgrounds.

Feature selection for ranking

Abstract: Ranking is a very important topic in information retrieval. While algorithms for learning ranking models have been intensively studied, this is not the case for feature selection, despite of its importance. The reality is that many feature selection methods used in classification are directly applied to ranking. We argue that because of the striking differences between ranking and classification, it is better to develop different feature selection methods for ranking. To this end, we propose a new feature selection method in this paper. Specifically, for each feature we use its value to rank the training instances, and define the ranking accuracy in terms of a performance measure or a loss function as the importance of the feature. We also define the correlation between the ranking results of two features as the similarity between them. Based on the definitions, we formulate the feature selection issue as an optimization problem, for which it is to find the features with maximum total importance scores and minimum total similarity scores. We also demonstrate how to solve the optimization problem in an efficient way. We have tested the effectiveness of our feature selection method on two information retrieval datasets and with two ranking models. Experimental results show that our method can outperform traditional feature selection methods for the ranking task.

3d geometric modeling and 3d video content creation

Abstract: A method and apparatus for obtaining an image to determine a three dimensional shape of a stationary or moving object using a bi dimensional coded light pattern having a plurality of distinct identifiable feature types. The coded light pattern is projected on the object such that each of the identifiable feature types appears at most once on predefined sections of distinguishable epipolar lines. An image of the object is captured and the reflected feature types are extracted along with their location on known epipolar lines in the captured image. The locations of identified feature types in the 2D image are corresponded to 3D coordinates in space in accordance to triangulation mappings. Thus a 3D mapping or model of imaged objects at an point in time is obtained.

Design and application of industrial machine vision systems

Abstract: In this paper, the role and importance of the machine vision systems in the industrial applications are described. First understanding of the vision in terms of a universal concept is explained. System design methodology is discussed and a generic machine vision model is reported. Such a machine includes systems and sub-systems, which of course depend on the type of applications and required tasks. In general, expected functions from a vision machine are the exploitation and imposition of the environmental constraint of a scene, the capturing of the images, analysis of those captured images, recognition of certain objects and features within each image, and the initiation of subsequent actions in order to accept or reject the corresponding objects. After a vision system performs all these stages, the task in hand is almost completed. Here, the sequence and proper functioning of each system and sub-systems in terms of high-quality images is explained. In operation, there is a scene with some constraint, first step for the machine is the image acquisition, pre-processing of image, segmentation, feature extraction, classification, inspection, and finally actuation, which is an interaction with the scene under study. At the end of this report, industrial image vision applications are explained in detail. Such applications include the area of automated visual inspection (AVI), process control, parts identification, and important role in the robotic guidance and control. Vision developments in manufacturing that can result in improvements in the reliability, in the product quality, and enabling technology for a new production process are presented. The key points in design and applications of a machine vision system are also presented. Such considerations can be generally classified into the six different categories such as the scene constraints, image acquisition, image pre-processing, image processing, machine vision justification, and finally the systematic considerations. Each aspect of such processes is described here and the proper condition for an optimal design is reported.

Searching for interacting features

Abstract: Feature interaction presents a challenge to feature selection for classification. A feature by itself may have little correlation with the target concept, but when it is combined with some other features, they can be strongly correlated with the target concept. Unintentional removal of these features can result in poor classification performance. Handling feature interaction can be computationally intractable. Recognizing the presence of feature interaction, we propose to efficiently handle feature interaction to achieve efficient feature selection and present extensive experimental results of evaluation.

Red Opal: product-feature scoring from reviews

Abstract: Online shoppers are generally highly task-driven: they have a certain goal in mind, and they are looking for a product with features that are consistent with that goal. Unfortunately, finding a product with specific features is extremely time-consuming using the search functionality provided by existing web sites.In this paper, we present a new search system called Red Opal that enables users to locate products rapidly based on features. Our fully automatic system examines prior customer reviews, identifies product features, and scores each product on each feature. Red Opal uses these scores to determine which products to show when a user specifies a desired product feature. We evaluate our system on four dimensions: precision of feature extraction, efficiency of feature extraction, precision of product scores, and estimated time savings to customers. On each dimension, Red Opal performs better than a comparison system.

Scalable near identical image and shot detection

Abstract: This paper proposes and compares two novel schemes for near duplicate image and video-shot detection. The first approach is based on global hierarchical colour histograms, using Locality Sensitive Hashing for fast retrieval. The second approach uses local feature descriptors (SIFT) and for retrieval exploits techniques used in the information retrieval community to compute approximate set intersections between documents using a min-Hash algorithm.The requirements for near-duplicate images vary according to the application, and we address two types of near duplicate definition: (i) being perceptually identical (e.g. up to noise, discretization effects, small photometric distortions etc); and (ii) being images of the same 3D scene (so allowing for viewpoint changes and partial occlusion). We define two shots to be near-duplicates if they share a large percentage of near-duplicate frames.We focus primarily on scalability to very large image and video databases, where fast query processing is necessary. Both methods are designed so that only a small amount of data need be stored for each image. In the case of near-duplicate shot detection it is shown that a weak approximation to histogram matching, consuming substantially less storage, is sufficient for good results. We demonstrate our methods on the TRECVID 2006 data set which contains approximately 165 hours of video (about 17.8M frames with 146K key frames), and also on feature films and pop videos.

Feature location via information retrieval based filtering of a single scenario execution trace

Abstract: The paper presents a semi-automated technique for feature location in source code. The technique is based on combining information from two different sources: an execution trace, on one hand and the comments and identifiers from the source code, on the other hand. Users execute a single partial scenario, which exercises the desired feature and all executed methods are identified based on the collected trace. The source code is indexed using Latent Semantic Indexing, an Information Retrieval method, which allows users to write queries relevant to the desired feature and rank all the executed methods based on their textual similarity to the query. Two case studies on open source software (JEdit and Eclipse) indicate that the new technique has high accuracy, comparable with previously published approaches and it is easy to use as it considerably simplifies the dynamic analysis.

Terrain Synthesis from Digital Elevation Models

Abstract: In this paper, we present an example-based system for terrain synthesis. In our approach, patches from a sample terrain (represented by a height field) are used to generate a new terrain. The synthesis is guided by a user-sketched feature map that specifies where terrain features occur in the resulting synthetic terrain. Our system emphasizes large-scale curvilinear features (ridges and valleys) because such features are the dominant visual elements in most terrains. Both the example height field and user's sketch map are analyzed using a technique from the field of geomorphology. The system finds patches from the example data that match the features found in the user's sketch. Patches are joined together using graph cuts and Poisson editing. The order in which patches are placed in the synthesized terrain is determined by breadth-first traversal of a feature tree and this generates improved results over standard raster-scan placement orders. Our technique supports user-controlled terrain synthesis in a wide variety of styles, based upon the visual richness of real-world terrain data.