Robert C. Bolles

Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

A new paradigm, Random Sample Consensus (RANSAC), for fitting a model to experimental data is introduced. RANSAC is capable of interpreting/smoothing data containing a significant percentage of gross errors, and is thus ideally suited for applications in automated image analysis where interpretation is based on the data provided by error-prone feature detectors. A major portion of this paper describes the application of RANSAC to the Location Determination Problem (LDP): Given an image depicting a set of landmarks with known locations, determine that point in space from which the image was obtained. In response to a RANSAC requirement, new results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form. These results provide the basis for an automatic system that can solve the LDP under difficult viewing

Recognising and Locating Partially Visible Objects: The Local-Feature-Focus Method

A new method of locating partially visible two-dimensional objects has been designed. The method is applicable to complex industrial parts that may contain several occurrences of local features such as holes and corners. The matching process is robust, because it bases its decisions on groups of mutually consistent features, and it is relatively fast, because it concentrates on key features that are automatically selected on the basis of a detailed analysis of CAD type of models of the objects.

Background modeling for segmentation of video-rate stereo sequences

Stereo sequences promise to be a powerful method for segmenting images for applications such as tracking human figures. We present a method of statistical background modeling for stereo sequences that improves the reliability and sensitivity of segmentation in the presence of object clutter. The dynamic version of the method, called gated background adaptation, can reliably learn background statistics in the presence of corrupting foreground motion. The method has been used with a simple head discriminator to detect and track people using a stereo head mounted on a pan/tilt platform. It runs at video rates using standard PC hardware.

Generalizing epipolar-plane image analysis on the spatiotemporal surface

The previous implementations of our Epipolar-Plane Image Analysis mapping technique demonstrated the feasibility and benefits of the approach, but were carried out for restricted camera geometries. The question of more general geometries made the techniques utility for autonomous navigation uncertain. We have developed a generalization of our analysis that (a) enables varying view direction, including variation over time (b) provides three-dimensional connectivity information for building coherent spatial descriptions of observed objects; and (c) operates sequentially, allowing initiation and refinement of scene feature estimates while the sensor is in motion. To implement this generalization it was necessary to develop an explicit description of the evolution of images over time. We have achieved this by building a process that creates a set of two-dimensional manifolds defined at the zeros of a three-dimensional spatiotemporal Laplacian. These manifolds represent explicitly both the spatial and temporal structure of the temporally evolving imagery, and we term them spatiotemporal surfaces. The surfaces are constructed incrementally, as the images are acquired. We describe a tracking mechanism that operates locally on these evolving surfaces in carrying out three-dimensional scene reconstruction.

Perceptual Organization and Curve Partitioning

In this paper we offer a critical evaluation of the partitioning (perceptual organization) problem, noting the extent to which it has distinct formulations and parameterizations. We show that most partitioning techniques can be characterized as variations of four distinct paradigms, and argue that any effective technique must satisfy two general principles. We give concrete substance to our general discussion by introducing new partitioning techniques for planar geometric curves, and present experimental results demonstrating their effectiveness.

Epipolar-plane image analysis: a technique for analyzing motion sequences

A technique for unifying spatial and temporal analysis of an image sequence taken by a camera moving in a straight line is presented. The technique is based on a “dense” sequence of images-images taken close enough together to form a solid block of data. Slices of this solid directly encode changes due to motion of the camera. These slices, which have one spatial dimension and one temporal dimension, are more structured than conventional images. This additional structure makes them easier to analyze. We present the theory behind this technique, describe an initial implementation, and discuss our preliminary results.

Robust Feature Matching Through Maximal Cliques

A crucial step in the recognition or location of an object in an image is the proper identification of object features. If the features are not uniquely characterized by their local appearances, as is often the case in programmable assembly, the matching technique must base its decisions on the relative structure of the features. In this paper we describe a technique that uses the relative positions and orientations of the features to determine the correspondence between features of an object model and features observed in a picture. A graph is constructed in which maximal cliques (i.e., completely connected subgraphs) represent mutually consistent assignments of model features to observed features. The technique is a robust, general-purpose way to match structures. However, in practical applications its use is restricted to moderately sized graphs because the algorithm that locates maximal cliques is apparently exponential. For tasks that require the analysis of large graphs a few techniques are presented to reformulate them so that smaller graphs are sufficient.

Rectification and recognition of text in 3-D scenes

Abstract.Real-world text on street signs, nameplates, etc. often lies in an oblique plane and hence cannot be recognized by traditional OCR systems due to perspective distortion. Furthermore, such text often comprises only one or two lines, preventing the use of existing perspective rectification methods that were primarily designed for images of document pages. We propose an approach that reliably rectifies and subsequently recognizes individual lines of text. Our system, which includes novel algorithms for extraction of text from real-world scenery, perspective rectification, and binarization, has been rigorously tested on still imagery as well as on MPEG-2 video clips in real time.

3DPO's strategy for matching three-dimensional objects in range data

A strategy for recognizing and locating three-dimensional objects in range data is presented. The strategy combines information derived from models of the objects and edges and surfaces detected in the data to efficiently match objects. Given a set of objects to be found, the set of object features are partitioned into subsets having similar intrinsic properties. An ordered tree of features to be considered is set up for each subset. These search trees are designed to maximize the use of the information as it is obtained and minimize the time required to recognize objects. A detailed example of this approach being used to recognize moderately complex castings in a jumble is presented.

Localization and Mapping for Autonomous Navigation in Outdoor Terrains : A Stereo Vision Approach

We consider the problem of autonomous navigation in unstructured outdoor terrains using vision sensors. The goal is for a robot to come into a new environment, map it and move to a given goal at modest speeds (1 m/sec). The biggest challenges are in building good maps and keeping the robot well localized as it advances towards the goal. In this paper, we concentrate on showing how it is possible to build a consistent, globally correct map in real time, using efficient precise stereo algorithms for map making and visual odometry for localization. While we have made advances in both localization and mapping using stereo vision, it is the integration of the techniques that is the biggest contribution of the research. The validity of our approach is tested in blind experiments, where we submit our code to an independent testing group that runs and validates it on an outdoor robot