nils-herrmann/clean_scirepeval huggingface dataset:
Geometric context from a single image

Many computer vision algorithms limit their performance by ignoring the underlying 3D geometric structure in the image. We show that we can estimate the coarse geometric properties of a scene by learning appearance-based models of geometric classes, even in cluttered natural scenes. Geometric classes describe the 3D orientation of an image region with respect to the camera. We provide a multiple-hypothesis framework for robustly estimating scene structure from a single image and obtaining confidences for each geometric label. These confidences can then be used to improve the performance of many other applications. We provide a thorough quantitative evaluation of our algorithm on a set of outdoor images and demonstrate its usefulness in two applications: object detection and automatic single-view reconstruction.
A Comparison of Image Segmentation Algorithms

Unsupervised image segmentation algorithms have matured to the point where they generate reasonable segmentations, and thus can begin to be incorporated into larger systems. A system designer now has an array of available algorithm choices, however, few objective numerical evaluations exist of these segmentation algorithms. As a first step towards filling this gap, this paper presents an evaluation of two popular segmentation algorithms, the mean shift-based segmentation algorithm and a graph-based segmentation scheme. We also consider a hybrid method which combines the other two methods. This quantitative evaluation is made possible by the recently proposed measure of segmentation correctness, the Normalized Probabilistic Rand (NPR) index, which allows a principled comparison between segmentations created by different algorithms, as well as segmentations on different images. For each algorithm, we consider its correctness as measured by the NPR index, as well as its stability with respect to changes in parameter settings and with respect to different images. An algorithm which produces correct segmentation results with a wide array of parameters on any one image, as well as correct segmentation results on multiple images with the same parameters, will be a useful, predictable and easily adjustable preprocessing step in a larger system. Our results are presented on the Berkeley image segmentation database, which contains 300 natural images along with several ground truth hand segmentations for each image. As opposed to previous results presented on this database, the algorithms we compare all use the same image features (position and colour) for segmentation, thereby making their outputs directly comparable.
Active Learning For Outdoor Obstacle Detection

Real-world applications of mobile robotics call for increased autonomy, requiring reliable perception systems. Since manually tuned perception algorithms are difficult to adapt to new operating environments, systems based on supervised learning are necessary for future progress in autonomous navigation. Data labeling is a major concern when supervised learning is applied to the large-scale problems occuring in realistic robotics applications. We believe that algorithms for automatically selecting important data for labeling are necessary, and propose to employ active learning techniques to reduce the amount of labeling required to learn from a data set. In this paper we show that several standard active learning algorithms can be adapted to meet specific constraints characteristic to our domain, such as the need to learn from data with severely unbalanced class priors. We validate the solutions we propose by extensive experimentation on multiple realistic data sets captured with a robotic vehicle. Based on our results for the task of obstacle detection, we conclude that active learning techniques are applicable to our domain, and they can lead to significant reductions in the labeling effort required to use supervised learning in outdoor perception.
Background Estimation under Rapid Gain Change in Thermal Imagery

We consider detection of moving ground vehicles in airborne sequences recorded by a thermal sensor with automatic gain control, using an approach that integrates dense optic flow over time to maintain a model of background appearance and a foreground occlusion layer mask. However, the automatic gain control of the thermal sensor introduces rapid changes in intensity that makes this difficult. In this paper we show that an intensity-clipped affine model of sensor gain is suffi- cient to describe the behavior of our thermal sensor. We develop a method for gain estimation and compensation that uses sparse flow of corner features to compute the affine background scene motion that brings pairs of frames into alignment prior to estimating change in pixel brightness. Dense optic flow and background appearance modeling is then performed on these motioncompensated and brightness-compensated frames. Experimental results demonstrate that the resulting algorithm can segment ground vehicles from thermal airborne video while building a mosaic of the background layer, despite the presence of rapid gain changes.
Finding organized structures in 3-D ladar data

In this paper, we address the problem of finding organized thin structures in three-dimensional (3-D) data. Linear and planar structures segmentation received much attention but thin structures organized in complex patterns remain a challenge for segmentation algorithms. We are interested especially in the problems posed by repetitive and symmetric structures acquired with a laser range finder. The method relies on 3-D data projections along specific directions and 2-D histograms comparison. The sensitivity of the classification algorithm to the parameter settings is evaluated and a segmentation method proposed. We illustrate our approach with data from a concertina wire in terrain with vegetation.
Approximate Parameter Learning in Discriminative Fields

In this paper, we present an approach for approximate maximu m likelihood parameter learning in discriminative field models, whi ch is based on approximating true expectations with simple piecewise c onstant functions constructed using inference techniques. Gradient as cent with these updates shows interesting weak-convergence behavio r which is tied closely to the number of errors made during inference. The pe rformance of various approximations was evaluated with different inf erence techniques showing that the learned parameters lead to good clas sific tion performance so long as the method used for approximating the radient is consistent with the inference mechanism. The proposed ap pro ch is general enough to be used for conditional training of conven tional MRFs.
Linear model hashing and batch RANSAC for rapid and accurate object recognition

This paper proposes a joint feature-based model indexing and geometric constraint based alignment pipeline for efficient and accurate recognition of 3D objects from a large model database. Traditional approaches either first prune the model database using indexing without geometric alignment or directly perform recognition based alignment. The indexing based pruning methods without geometric constraints can miss the correct models under imperfections such as noise, clutter and obscurations. Alignment based verification methods have to linearly verify each model in the database and hence do not scale up. The proposed techniques use spin images as semi-local shape descriptors and locality-sensitive hashing (LSH) to index into a joint spin image database for all the models. The indexed models represented in the pruned set are further pruned using progressively complex geometric constraints. A simple geometric configuration of multiple spin images, for instance a doublet, is first used to check for geometric consistency. Subsequently, full Euclidean geometric constraints are applied using RANSAC-based techniques on the pruned spin images and the models to verify specific object identity. As a result, the combined indexing and geometric alignment based pipeline is able to focus on matching the most promising models, and generate far less pose hypotheses while maintaining the same level of performance as the sequential alignment based recognition. Furthermore, compared to geometric indexing techniques like geometric hashing, the construction time and storage complexity for the proposed technique remains linear in the number of features rather than higher order polynomial. Experiments on a 56 3D model database show promising results.
Classifier fusion for outdoor obstacle detection

This work 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 infrared (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 tractor.
Natural terrain classification using 3-d ladar data

Because of the difficulty of interpreting laser data in a meaningful way, safe navigation in vegetated terrain is still a daunting challenge. In this paper, we focus on the segmentation of ladar data using local 3-D point statistics into three classes: clutter to capture grass and tree canopy, linear to capture thin objects like wires or tree branches, and finally surface to capture solid objects like ground terrain surface, rocks or tree trunks. We present the details of the method proposed, the modifications we made to implement it on-board an autonomous ground vehicle. Finally, we present results from field tests using this rover and results produced from different stationary laser sensors.
Parts-based 3D object classification

This paper presents a parts-based method for classifying scenes of 3D objects into a set of pre-determined object classes. Working at the part level, as opposed to the whole object level, enables a more flexible class representation and allows scenes in which the query object is significantly occluded to be classified. In our approach, parts are extracted from training objects and grouped into part classes using a hierarchical clustering algorithm. Each part class is represented as a collection of semi-local shape features and can be used to perform pan class recognition. A mapping from part classes to object classes is derived from the learned part classes and known object classes. At run-time, a 3D query scene is sampled, local shape features are computed, and the object class is determined using the learned pan classes and the pan-to-object mapping. Classifying novel 3D scenes of vehicles into eight classes demonstrate the approach.
Shape-based recognition of wiry objects

We present an approach to the recognition of complex-shaped objects in cluttered environments based on edge information. We first use example images of a target object in typical environments to train a classifier cascade that determines whether edge pixels in an image belong to an instance of the desired object or the clutter. Presented with a novel image, we use the cascade to discard clutter edge pixels and group the object edge pixels into overall detections of the object. The features used for the edge pixel classification are localized, sparse edge density operations. Experiments validate the effectiveness of the technique for recognition of a set of complex objects in a variety of cluttered indoor scenes under arbitrary out-of-image-plane rotation. Furthermore, our experiments suggest that the technique is robust to variations between training and testing environments and is efficient at runtime.
Word: Wiry object recognition database

A curtain rod assembly manufactured primarily from extruded aluminum components, the curtain rod defining an elongated cruciform groove in its upper portion to receive therein a like shaped support part, a horizontal flange being provided at the rod's lower portion to receive wheeled curtain carriers, the rod being symmetrical about a vertical plane. In a modification of the rod, instead of a vertically elongated cruciform groove, a vertical plate-like portion is provided wherein such upper vertical portion is fastened through horizontal bolts to an overhead support, a flange on the lower aspect with a horizontal cross piece defining tracks for the wheeled curtain carriers as in the first modification, this second modification also being symmetrical about a vertical plane. A spacer support is provided for the first curtain rod having a pair of spaced parallel cruciform-shaped portions to be received in the like shaped slots of two overlapping parallel rods, the spacer including a stop member for carriers on one of the rods and a rope guide thereunder slideably to receive the curtain rope for supporting same. Live-end and deadend pulleys journalled in housings are provided which are adapted to support or be supported by a rod in a fixed relationship thereto so as to be aligned with the curtain carriers. The curtain rod is adapted to receive chain links in its cruciform slot for supporting the rod from the overhead structure at selected locations. The curtain rod also is adapted to be inverted whereby the flange, through a slot provided therein, is supported by overhead screw connections and a curtain carrier is received in the cruciform slot.
A Hybrid Object-Level/Pixel-Level Framework For Shape-based Recognition

This paper presents a technique for shape-based recognition that fuses pixellevel and object-level approaches into a unified framework. A pixel-level algorithm classifies individual pixels as belonging to a target object or clutter based on automatically-selected shape features computed in a spatial arrangement around them; an object-level algorithm classifies object-sized rectangular image regions as objects or clutter by aggregating pixel classifier scores in the regions. We train a cascade of interleaved pixel-level and objectlevel modules to quickly localize complex-shaped objects in highly cluttered scenes under arbitrary out-of-image-plane rotation. Experimental results on a large set of real, highly-cluttered images of a common object under arbitrary out of image plane rotation demonstrate improvements over cascades of strictly pixel-level modules.
Enabling learning from large datasets: applying active learning to mobile robotics

Autonomous navigation in outdoor, off-road environments requires solving complex classification problems. Obstacle detection, road following and terrain classification are examples of tasks which have been successfully approached using supervised machine learning techniques for classification. Large amounts of training data are usually necessary in order to achieve satisfactory generalization. In such cases, manually labeling data becomes an expensive and tedious process. This work describes a method for reducing the amount of data that needs to be presented to a human trainer. The algorithm relies on kernel density estimation in order to identify "interesting" scenes in a dataset. Our method does not require any interaction with a human expert for selecting the images, and only minimal amounts of tuning are necessary. We demonstrate its effectiveness in several experiments using data collected with two different vehicles. We first show that our method automatically selects those scenes from a large dataset that a person would consider "important" for classification tasks. Secondly, we show that by labeling only few of the images selected by our method, we obtain classification performance that is comparable to the one reached after labeling hundreds of images from the same dataset.
Path planning with hallucinated worlds

We describe an approach that integrates midrange sensing into a dynamic path planning algorithm. The algorithm is based on measuring the reduction in path cost that would be caused by taking a sensor reading from candidate locations. The planner uses this measure in order to decide where to take the next sensor reading. Ideally, one would like to evaluate a path based on a map that is as close as possible to the true underlying world. In practice, however, the map is only sparsely populated by data derived from sensor readings. A key component of the approach described in this paper is a mechanism to infer (or "hallucinate") more complete maps from sparse sensor readings. We show how this hallucination mechanism is integrated with the planner to produce better estimates of the gain in path cost occurred when taking sensor readings. We show results on a real robot as well as a statistical analysis on a large set of randomly generated path planning problems on elevation maps from real terrain.
Discriminative Fields for Modeling Spatial Dependencies in Natural Images

In this paper we present Discriminative Random Fields (DRF), a discriminative framework for the classification of natural image regions by incorporating neighborhood spatial dependencies in the labels as well as the observed data. The proposed model exploits local discriminative models and allows to relax the assumption of conditional independence of the observed data given the labels, commonly used in the Markov Random Field (MRF) framework. The parameters of the DRF model are learned using penalized maximum pseudo-likelihood method. Furthermore, the form of the DRF model allows the MAP inference for binary classification problems using the graph min-cut algorithms. The performance of the model was verified on the synthetic as well as the real-world images. The DRF model outperforms the MRF model in the experiments.
3D modeling using a statistical sensor model and stochastic search

Accurate and robust registration of multiple three-dimensional (3D) views is crucial for creating digital 3D models of real-world scenes. In this paper, we present a framework for evaluating the quality of model hypotheses during the registration phase. We use maximum likelihood estimation to learn a probabilistic model of registration success. This method provides a principled way to combine multiple measures of registration accuracy. Also, we describe a stochastic algorithm for robustly searching the large space of possible models for the best model hypothesis. This new 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. This work is part of a system we have developed to automate the 3D modeling process for a set of 3D views obtained from unknown sensor viewpoints.
Robust extraction of multiple structures from non-uniformly sampled data

The extraction of multiple coherent structures from point clouds is crucial to the problem of scene modeling. While many statistical methods exist for robust estimation from noisy data, they are inadequate for addressing issues of scale, semi-structured clutter, and large point density variation together with the computational restriction of autonomous navigation. This paper extends an approach of nonparametric projection-pursuit based regression to compensate for the non-uniform and directional nature of data sampled in outdoor environments. The proposed algorithm is employed for extraction of planar structures and clutter grouping. Results are shown for scene abstraction of 3D range data in large urban scenes.
Shape-based recognition of wiry objects

We present an approach to the recognition of complex-shaped objects in cluttered environments based on edge cues. We first use example images of the desired object in typical backgrounds to train a classifier cascade which determines whether edge pixels in an image belong to an instance of the object or the clutter. Presented with a novel image, we use the cascade to discard clutter edge pixels. The features used for this classification are localized, sparse edge density operations. Experiments validate the effectiveness of the technique for recognition of complex objects in cluttered indoor scenes under arbitrary out-of-image-plane rotation.
Discriminative techniques for the recognition of complex-shaped objects

This thesis presents new techniques which enable the automatic recognition of everyday objects like chairs and ladders in images of highly cluttered scenes. Given an image, we extract information about the shape and texture properties present in small patches of the image and use that information to identify parts of the objects we are interested in. We then assemble those parts into overall hypotheses about what objects are present in the image, and where they are. Solving this problem in a general setting is one of the central problems in computer vision. 
The central theme of this work is that formulating object recognition as a discrimination problem can ease the burden of system design. In particular, we show that thinking of recognition in terms of discriminating between objects and clutter, rather than separately modeling the appearances of objects and clutter, can simplify the processes of extracting information from the image and identifying which parts of the image correspond with parts of objects. 
The bulk of this thesis is concerned with recognizing “wiry” objects in highly-cluttered images; an example problem is finding ladders in images of a messy warehouse space. Wiry objects are distinguished by a prevalence of very thin, elongated, stick-like components; examples include tables, chairs, bicycles, and desk lamps. They are difficult to recognize because they tend to lack distinctive color or texture characteristics and their appearance is not easy to describe succinctly in terms of rectangular patches of image pixels. Here, we present a set of algorithms which extends current capabilities to find wiry objects in highly cluttered images across changes in the clutter and object pose. 
The second part of the thesis presents a technique for extracting texture features from images in such a way that features from objects of interest are both well-clustered with each other and well-separated from the features from clutter. We present an optimization framework for automatically combining existing texture features into features that discriminate well, thus simplifying the process of tuning the parameters of the feature extraction process.
Man-made structure detection in natural images using a causal multiscale random field

This paper presents a generative model based approach to man-made structure detection in 2D (two-dimensional) natural images. The proposed approach uses a causal multiscale random field suggested by Bouman and Shapiro (1994) as a prior model on the class labels on the image sites. However, instead of assuming the conditional independence of the observed data, we propose to capture the local dependencies in the data using a multiscale feature vector. The distribution of the multiscale feature vectors is modeled as mixture of Gaussians. A set of robust multi-scale features is presented that captures the general statistical properties of man-made structures at multiple scales without relying on explicit edge detection. The proposed approach was validated on real-world images from the Corel data set, and a performance comparison with other techniques is presented.