nils-herrmann/clean_scirepeval huggingface dataset:
Minimum risk distance measure for object recognition

The optimal distance measure for a given discrimination task under the nearest neighbor framework has been shown to be the likelihood that a pair of measurements have different class labels [S. Mahamud et al., (2002)]. For implementation and efficiency considerations, the optimal distance measure was approximated by combining more elementary distance measures defined on simple feature spaces. We address two important issues that arise in practice for such an approach: (a) What form should the elementary distance measure in each feature space take? We motivate the need to use the optimal distance measure in simple feature spaces as the elementary distance measures; such distance measures have the desirable property that they are invariant to distance-respecting transformations, (b) How do we combine the elementary distance measures ? We present the precise statistical assumptions under which a linear logistic model holds exactly. We benchmark our model with three other methods on a challenging face discrimination task and show that our approach is competitive with the state of the art.
Experimental Results in Using Aerial LADAR Data for Mobile Robot Navigation

In this paper, we investigate the use of high resolution aerial LADAR data for autonomous mobile robot navigation in natural environments. The use of prior maps from aerial LADAR (LAser Detection And Ranging) survey is considered for enhancing system performance in two areas. First, the prior maps are used for registration with the data from the robot in order to compute accurate localization in the map. Second, the prior maps are used for computing detailed traversability maps that are used for planning over long distances. Our objective is to assess the key issues in using such data and to report on a first batch of experiments in combining high-resolution aerial data and on-board sensing
The optimal distance measure for object detection

We develop a multi-class object detection framework whose core component is a nearest neighbor search over object part classes. The performance of the overall system is critically dependent on the distance measure used in the nearest neighbor search. A distance measure that minimizes the misclassification risk for the 1-nearest neighbor search can be shown to be the probability that a pair of input image measurements belong to different classes. In practice, we model the optimal distance measure using a linear logistic model that combines the discriminative powers of more elementary distance measures associated with a collection of simple to construct feature spaces like color, texture and local shape properties. Furthermore, in order to perform search over large training sets efficiently, the same framework was extended to find hamming distance measures associated with simple discriminators. By combining this discrete distance model with the continuous model, we obtain a hierarchical distance model that is both fast and accurate. Finally, the nearest neighbor search over object part classes was integrated into a whole object detection system and evaluated against an indoor detection task yielding good results.
Sensor and classifier fusion for outdoor obstacle detection: an application of data fusion to autonomous off-road navigation

This paper describes an approach for using several levels of data fusion in the domain of autonomous off-road navigation. We are focusing on outdoor obstacle detection, and we present techniques that leverage on data fusion and machine learning for increasing the reliability of obstacle detection systems. We are combining color and IR imagery with range information from a laser range finder. We show that in addition to fusing data at the pixel level, performing high level classifier fusion is beneficial in our domain. Our general approach is to use machine learning techniques for automatically deriving effective models of the classes of interest (obstacle and non-obstacle for example). We train classifiers on different subsets of the features we extract from our sensor suite and show how different classifier fusion schemes can be applied for obtaining a multiple classifier system that is more robust than any of the classifiers presented as input. We present experimental results we obtained on data collected with both the Experimental Unmanned Vehicle (XUV) and a CMU developed robotic vehicle.
Quality assessment of traversability maps from aerial LIDAR data for an unmanned ground vehicle

In this paper we address the problem of assessing quantitatively the quality of traversability maps computed from data collected by an airborne laser range finder. Such data is used to plan paths for an unmanned ground vehicle (UGV) prior to the execution of long range traverses. Little attention has been devoted to the problem we address in this paper. We use a unique data set of geodetic control points, real robot navigation data, ground LIDAR (light detection and ranging) data and aerial imagery, collected during a week long demonstration to support our work.
Discriminative random fields: a discriminative framework for contextual interaction in classification

In this work we present discriminative random fields (DRFs), a discriminative framework for the classification of image regions by incorporating neighborhood interactions in the labels as well as the observed data. The discriminative random fields offer several advantages over the conventional Markov random field (MRF) framework. First, the DRFs allow to relax the strong assumption of conditional independence of the observed data generally used in the MRF framework for tractability. This assumption is too restrictive for a large number of applications in vision. Second, the DRFs derive their classification power by exploiting the probabilistic discriminative models instead of the generative models used in the MRF framework. Finally, all the parameters in the DRF model are estimated simultaneously from the training data unlike the MRF framework where likelihood parameters are usually learned separately from the field parameters. We illustrate the advantages of the DRFs over the MRF framework in an application of man-made structure detection in natural images taken from the Corel database.
Toward generating labeled maps from color and range data for robot navigation

This paper addresses the problem of extracting information from range and color data acquired by a mobile robot in urban environments. Our approach extracts geometric structures from clouds of 3-D points and regions from the corresponding color images, labels them based on prior models of the objects expected in the environment - buildings in the current experiments - and combines the two sources of information into a composite labeled map. Ultimately, our goal is to generate maps that are segmented into objects of interest, each of which is labeled by its type, e.g., building, vegetation, etc. Such a map provides a higher-level representation of the environment than the geometric maps normally used for mobile robot navigation. The techniques presented here are a step toward the automatic construction of such labeled maps.
Robust tracking and structure from motion with sample based uncertainty representation

Geometric reconstruction of the environment from images is critical in autonomous mapping and robot navigation. Geometric reconstruction involves feature tracking, i.e., locating corresponding image features in consecutive images, and structure from motion (SFM), i.e., recovering the 3D structure of the environment from a set of correspondences between images. Although algorithms for feature tracking and structure from motion are well-established, their use in practical mobile robot applications is still difficult because of occluded features, non-smooth motion between frames, and ambiguous patterns in images. We show how a sampling-based representation can be used in place of the traditional Gaussian representation of uncertainty. We show how sampling can be used for both feature tracking and SFM and we show how they are combined in this framework. The approach is exercised in the context of a mobile robot navigating through an outdoor environment with an omnidirectional camera.
Automatic three-dimensional modeling from reality

In this dissertation, we develop techniques to fully automate the process of constructing a digital. three-dimensional (3D) model from a set of 3D views of a static scene obtained from unknown viewpoints. Since 3D sensors only capture scene structure from a single viewpoint, multiple views must be combined to create a complete model. Existing methods require measurement of the sensor viewpoints or manual view registration. Given a set of 3D views, the challenge is to align the views in a common coordinate system without knowing the original sensor viewpoints or even which views overlap one another. This problem is analogous to assembling a jigsaw puzzle in 3D. The views are the puzzle pieces, and the problem is to correctly assemble the pieces without even knowing what the puzzle is supposed to look like. Our approach uses pair-wise surface matching to align pairs of views. These pair-wise matches, some of which will be incorrect, are stored as edges in a graph that contains a node for each view. A sub-graph of this graph is a model hypothesis. Our goal is to find a model hypothesis that connects all the views and contains only correct matches. First, we develop a framework for evaluating the quality of model hypotheses, using maximum likelihood estimation to learn a probabilistic model of pair-wise registration success. This method provides a principled way to combine multiple measures of registration accuracy. We then extend this local quality measure to form a global measure of model quality. Next, we describe two classes of algorithms for robustly searching the large space of possible models for the best model hypothesis. Our approach can detect situations in which no solution exists, outputting a set of model parts if a single model using all the views cannot be found. We show results for a large collection of automatically modeled scenes and demonstrate that our algorithm works independently of scene size and the type of range sensor used.
Discriminative distance measures for object detection

The reliable detection of an object of interest in an input image with arbitrary background clutter and occlusion has to a large extent remained an elusive goal in computer vision. Traditional model-based approaches are inappropriate for a multi-class object detection task primarily due to difficulties in modeling arbitrary object classes. Instead, we develop a detection framework whose core component is a nearest neighbor search over object parts. The performance of the overall system is critically dependent on the distance measure used in the nearest neighbor search. A distance measure that minimizes the mis-classification risk for the 1-nearest neighbor search can be shown to be the probability that a pair of input measurements belong to different classes. This pair-wise probability is not in general a metric distance measure. Furthermore, it can out-perform any metric distance, approaching even the Bayes optimal performance. In practice, we seek a model for the optimal distance measure that combines the discriminative powers of more elementary distance measures associated with a collection of simple feature spaces that are easy and efficient to implement; in our work, we use histograms of various feature types like color, texture and local shape properties. For performing efficient nearest neighbor search over large training sets, the linear model was extended to discretized distance measures that combines distance measures associated with discriminators organized in a tree-like structure. Finally, the nearest neighbor search over object parts was integrated into a whole object detection system and evaluated against both an indoor detection task as well as a face recognition task yielding promising results.
3D Rover Localization in Airborne Ladar Data

In this paper we investigate the use of airborne laser altimetry and 3D ground measurements to estimate the position and the attitude of a mobile robot. Our approach is derived from earlier work on matching terrain maps by using local surface signatures. In this paper, we show how point selection strategies improve significantly both the speed and the accuracy of the surface matching algorithm. We also introduce strategies for dealing with missing data - “range shadows” - due to occlusions. We describe in detail the performance of the system in terms of accuracy, speed, and robustness with respect to parameter selection and terrain type as evaluated in five different environments with different ground and airborne sensors. We also describe the performance of the system in real environments with large-scale maps.
Probabilistic Classification of Image Regions using an Observation-Constrained Generative Approach

In generic image understanding applications, one of the goals is to interpret the semantic context of the scene (e.g., beach, office etc.). In this paper, we propose a probabilistic region classification scheme for natural scene images as a priming step for the problem of context interpretation. In conventional generative methods, a generative model is learnt for each class using all the available training data belonging to that class. However, if a set of newly observed data has been generated because of the subset of the model support, using the full model to assign generative probabilities can produce serious artifacts in the probability assignments. This problem arises mainly when the different classes have multimodal distributions with considerable overlap in the feature space. We propose an approach to constrain the class generative probability of a set of newly observed data by exploiting the distribution of the new data itself and using linear weighted mixing. A KL-Divergence-based fast model selection procedure is also proposed for learning mixture models in a sparse feature space. The preliminary results on the natural scene images support the effectiveness of the proposed approach. Image region classification, generative model, semantic interpretation, image segmentation .
Terrain Classification Techniques From Ladar Data For Autonomous Navigation

Autonomous navigation remains a considerable challenge, primarily because of the difficulty in describing the environment of the robot in a way that captures the variability of natural environments. In this paper, we focus on the problem of extracting the ground terrain surface from sparse 3-D data from LADAR mobility sensors, including the segmentation of the terrain from obscuring vegetation. In this paper, we briefly review possible approaches to LADAR processing, discuss their limitations, and describe our current approach. Results obtained with the GDRS CTA LADAR are presented
Object Recognition by a Cascade of Edge Probes

We frame the problem of object recognition from edge cues in terms of determining whether individual edge pixels belong to the target object or to clutter, based on the configuration of edges in their vicinity. A classifier solves this problem by computing sparse, localized edge features at image locations determined at training time. In order to save computation and solve the aperture problem, we apply a cascade of these classifiers to the image, each of which computes edge features over larger image regions than its predecessors. Experiments apply this approach to the recognition of real objects with holes and wiry components in cluttered scenes under arbitrary out-of-image-plane rotation.
On Pencils of Tangent Planes and the Recognition of Smooth 3D Shapes from Silhouettes

This paper presents a geometric approach to recognizing smooth objects from their outlines. We define a signature function that associates feature vectors with objects and baselines connecting pairs of possible viewpoints. Feature vectors, which can be projective, affine, or Euclidean, are computed using the planes that pass through a fixed baseline and are also tangent to the object's surface. In the proposed framework, matching a test outline to a set of training outlines is equivalent to finding intersections in feature space between the images of the training and the test signature functions. The paper presents experimental results for the case of internally calibrated perspective cameras, where the feature vectors are angles between epipolar tangent planes.
3D Photography from Photographs and Video Clips

This paper addresses the problem of acquiring realistic visual models of the shape and appearance of complex three-dimensional (3D) scenes from collec-tions of images, a process dubbed 3D photography. We focus on three instances of this problem: (1) the image-based construction of projective visual hulls of complex surfaces from weakly-calibrated photographs; (2) the automated matching and registration of photographs of textured surfaces using affine-invariant patches and their geometric relationships; and (3) an approach to projective motion analysis and self-calibration explicitly accounting for natural camera constraints such as zero skew and capable of handling large numbers of images in an efficient and uniform manner. We also briefly discuss some relat-ed applications of oriented differential projective geometry to computer vision problems, including the determination of the ordering of rim segments in pro-jective visual hull computation, and a purely projective proof of Koenderink's famous characterization of the local shape of visual contours.
Building Elevation Maps From Underwater Sonar Data

Deriving a terrain model from sensor data is an important task for the autonomous navigation of a mobile robot. This paper describes an approach for autonomous underwater vehicles using a side scan sonar system. First. some general aspects of the type of data and filtering techniques to improve it are discussed. We then proceed to derive an estimcltcd bottom contour, using a geomemc reflection model and information about shadows and highlights. Severat tccfiniqucs of surface reconsrruction and their limitations are presented.
Autonomous Driving with Concurrent Goals and Multiple Vehicles: Experiments and Mobility Components

In this paper, we describe a complete system for mission planning and execution for multiple robots in natural terrain. We report on experiments with a system for autonomously driving two vehicles based on complex mission specifications. We show that the system is able to plan local paths in obstacle fields based on sensor data, to plan and update global paths to goals based on frequent obstacle map updates, and to modify mission execution, e.g., the assignment and ordering of the goals, based on the updated paths to the goals.Two recently developed sensors are used for obstacle detection: a high-speed laser range finder, and a video-rate stereo system. An updated version of a dynamic path planner, D*, is used for on-line computation of routes. A new mission planning and execution-monitoring tool, GRAMMPS, is used for managing the allocation and ordering of goals between vehicles.We report on experiments conducted in an outdoor test site with two HMMWVs. Implementation details and performance analysis, including failure modes, are described based on a series of twelve experiments, each over 1/2 km distance with up to nine goals.The work reported here includes a number of results not previously published, including the use of a real-time stereo machine and a high-performance laser range finder, and the use of the GRAMMPS planning system.
Training Object Detection Models with Weakly Labeled Data

Appearance based object detection systems utilizing statistical models to capture real world variations in appearance have been shown to exhibit good detection performance. The parameters of these statistical models are typically learned automatically from labeled training images. This process can be difficult in that a large number of labeled training examples may be needed to accurately model appearance variation. In this work we describe a method whereby a training set consisting of a small number of fully labeled training examples augmented with a set of weakly labeled examples can be used to train a detector which exhibits performance better than that which can be obtained with a reduced set of fully labeled training examples alone.
Discriminant Filters For Object Recognition

This paper presents a technique for using training data to design image filters for appearance-based object recognition. Rather than scanning the image with a single set of filters and using the results to test for the existence of objects, we use many sets of filters and take linear combinations of their outputs. The combining coefficients are optimized in a training phase to encourage discriminability between the filter responses for distinct parts of the object and clutter. Our experiments on three popular filter types show that by using this approach to combine sets of filters whose design parameters vary over a wide range, we can achieve detection performance competitive with that of any individual filter set. This in turn can ease the task of fine-tuning the settings for both the filters and the mechanisms that analyze their
Distributed robotic mapping of extreme environments

In the extreme environments posed by war fighting, fire fighting, and nuclear accident response, the cost of direct human exposure is levied in terms of injury and death. Robotic alternatives must address effective operations while removing humans from danger. This is profoundly challenging, as extreme environments inflict cumulative performance damage on exposed robotic agents. Sensing and perception are among the most vulnerable components. We present a distributed robotic system that enables autonomous reconnaissance and mapping in urban structures using teams of robots. Robot teams scout remote sites, maintain operational tempos, and successfully execute tasks, principally the construction of 3-D Maps, despite multiple agent failures. Using an economic model of agent interaction based on a free market architecture, a virtual platform (a robot colony) is synthesized where task execution does not directly depend on individual agents within the colony.
Progress in 3-D Mapping and Localization

Thispaperis a summaryof resultsobtainedin thepastfew years in theareaof 3-D mappingandrobot localization. Theemphasisof this work is thereconstructionof three-dimensional representationsof theenvironmentfromsensor information,assuminginaccurateor absentrobotposeinformation,andassuminggeneral 3-D configurations,e.g., not limited to 2-1/2Delevationmaps.Theapproachesaredividedinto twobroadclasses:Surfacematching, in which large piecesof 3-D surfacesare matchedacrossobservationsin order to recover thetransformationsbetweenobservations,andfeaturematching in which individual featuresextractedfromtheinputdataarematched.Surfacematching is mostapplicableto robotsequippedwith range sensor s such asstereoor ladar, while feature matching is most applicableto video-basedsystems. We reporton experimentsandapplicationsin theareasof terrain mapbuilding, modelingof interior environments,modelingof individual objectsfrom manyviews,cooperative stereovision using teams of r obots, and simultaneous r ecovery of structur e and motion fr om bearing-only sensor s.