3D Range Images Research Articles

Temporal Range Registration for Unmanned Ground and Aerial Vehicles

An iterative temporal registration algorithm is presented in this article for registering 3D range images obtained from unmanned ground and aerial vehicles traversing unstructured environments. We are primarily motivated by the development of 3D registration algorithms to overcome both the unavailability and unreliability of Global Positioning System (GPS) within required accuracy bounds for Unmanned Ground Vehicle (UGV) navigation. After suitable modifications to the well-known Iterative Closest Point (ICP) algorithm, the modified algorithm is shown to be robust to outliers and false matches during the registration of successive range images obtained from a scanning LAser Detection And Ranging (LADAR) rangefinder on the UGV. Towards registering LADAR images from the UGV with those from an Unmanned Aerial Vehicle (UAV) that flies over the terrain being traversed, we then propose a hybrid registration approach. In this approach to air to ground registration to estimate and update the position of the UGV, we register range data from two LADARs by combining a feature-based method with the aforementioned modified ICP algorithm. Registration of range data guarantees an estimate of the vehicle's position even when only one of the vehicles has GPS information. Temporal range registration enables position information to be continually maintained even when both vehicles can no longer maintain GPS contact. We present results of the registration algorithm in rugged terrain and urban environments using real field data acquired from two different LADARs on the UGV.

Finger surface as a biometric identifier

We present a novel approach for personal identification and identity verification which utilizes 3D finger surface features as a biometric identifier. Using 3D range images of the hand, a surface representation for the index, middle, and ring finger is calculated and used for comparison to determine subject similarity. We use the curvature based shape index to represent the fingers’ surface. Gallery and probe shape index signatures are compared using the normalized correlation coefficient to compute a match score. A large unique database of hand images supports the research. We use data sets obtained over time to examine the performance of each individual finger surface as a biometric identifier as well as the performance obtained when combining them. Both identification and verification experiments are conducted. In addition, probe and gallery sets sizes are increased to further improve recognition performance in our experiments. Our approach yields good results for a first-of-its-kind biometric technique, indicating that this approach warrants further research.

Line Extraction in 2D Range Images for Mobile Robotics

This paper presents a geometrical feature detection framework for use with conventional 2D laser rangefinders. This framework is composed of three main procedures: data pre-processing, breakpoint d...

Shape difference visualization for ancient bronze mirrors through 3D range images

AbstractJapanese archaeologists have paid special attention to ancient Chinese bronze mirrors because the mirrors may provide a key for the exact location of Yamatai State, which is one of the major archaeological controversies. Currently, archaeologists visually analyse ancient Chinese bronze mirrors for their shape difference. The practice requires a huge amount of time and effort. In this paper, we propose an automatic method for detecting the shape difference between a pair of ancient mirrors. The 3D data of the mirrors are obtained using a laser range scanner. Our algorithm then aligns them into the same coordinate and visualizes their shape differences. Our proposed algorithm provides fast and non‐damaging analysis for shape difference. Further analysis can be evaluated on our data instead of the actual mirror, so it can be performed by more than one group of archaeologists. Copyright © 2003 John Wiley & Sons, Ltd.

The Nonlinear Statistics of High-Contrast Patches in Natural Images

Recently, there has been a great deal of interest in modeling the non-Gaussian structures of natural images. However, despite the many advances in the direction of sparse coding and multi-resolution analysis, the full probability distribution of pixel values in a neighborhood has not yet been described. In this study, we explore the space of data points representing the values of 3 × 3 high-contrast patches from optical and 3D range images. We find that the distribution of data is extremely “sparse” with the majority of the data points concentrated in clusters and non-linear low-dimensional manifolds. Furthermore, a detailed study of probability densities allows us to systematically distinguish between images of different modalities (optical versus range), which otherwise display similar marginal distributions. Our work indicates the importance of studying the full probability distribution of natural images, not just marginals, and the need to understand the intrinsic dimensionality and nature of the data. We believe that object-like structures in the world and the sensor properties of the probing device generate observations that are concentrated along predictable shapes in state space. Our study of natural image statistics accounts for local geometries (such as edges) in natural scenes, but does not impose such strong assumptions on the data as independent components or sparse coding by linear change of bases.

SOFTWARE TOOLKIT FOR TEACHING IMAGE PROCESSING

We introduce a software framework called the Java Vision Toolkit (JVT) for teaching image processing and computer vision. The toolkit provides over 50 image operations and presents them to the user in a GUI that can render grayscale, color and 3D range images. The software is written in Java, enabling it to be integrated into HTML documents and interactive course materials. The framework is designed for extensibility using a source code template that supports the implementation of any new operation with a minimal amount of supporting code. For students, this framework encapsulates the GUI, file I/O and other trivial programming details and allows them the maximum amount of time to spend on understanding computer vision. We compare the JVT with other computer vision software frameworks that are used for teaching and research. We also discuss the use of the JVT in an undergraduate image processing course at the University of South Florida.

Extracting objects from range and radiance images

In this paper, we present a pipeline and several key techniques necessary for editing a real scene captured with both cameras and laser range scanners. We develop automatic algorithms to segment the geometry from range images into distinct surfaces, register texture from radiance images with the geometry, and synthesize compact high-quality texture maps. The result is an object-level representation of the scene which can be rendered with modifications to structure via traditional rendering methods. The segmentation algorithm for geometry operates directly on the point cloud from multiple registered 3D range images instead of a reconstructed mesh. It is a top-down algorithm which recursively partitions a point set into two subsets using a pairwise similarity measure. The result is a binary tree with individual surfaces as leaves. Our image registration technique performs a very efficient search to automatically find the camera poses for arbitrary position and orientation relative to the geometry. Thus, we can take photographs from any location without precalibration between the scanner and the camera. The algorithms have been applied to large-scale real data. We demonstrate our ability to edit a captured scene by moving, inserting, and deleting objects.

Range image acquisition for machine vision

An optical 3-D imaging system for autonomous vehicle and mobile robot navigation is presented. The system is based on a phase comparison range finder using a 30-mW laser diode modulated at a 5-MHz frequency, and a scanning mechanism, consisting of a nodding mirror and an eight-faced polygon and providing 30 X 30-deg total view with only 0.5-deg instantaneous fields of view for the receiver optics. A Si-avalanche photodiode (APD) optoelectric detector and low-noise preamplifier can detect very low levels of returned energy. With an integrated MC4044 circuit for phase comparison, pixel range information can be represented by gray level on the high-resolution video monitor. A 3-D range image is obtained in the experiment, and a range accuracy of 0.08 m at 10 m outdoors in sunlight is achieved.

Exploiting neural trees in range image understanding

In this paper, a new neural tree architecture whose nodes are generalized perceptrons without hidden layers is applied to segment range images into surface patches, according to the six models of differential geometry, e.g., peak, ridge, valley, saddle, pit and flat. A new learning scheme which improves upon the standard neural tree algorithms in terms of convergence is proposed. Splitting nodes are introduced into the neural tree architecture to divide the training set when the current perceptron node repeats the same classification of the parent node: such a strategy is able to assure in any case the convergence of the tree building process and to reduce misclassifications. Significant results on synthetic and real 3D range images are presented and compared with conventional approaches.

Segmentation of 3D Range Images Using Pyramidal Data Structures

Given a 3D range image of a scene containing multiple arbitrarily shaped objects, we segment the scene into homogeneous surface patches. A new modular framework for the segmentation task has been proposed. In the first module, oversegmentation is achieved using zeroth and first-order local surface properties. The segmentation is then refined in the second module using high order surface representations dictated by the upper level vision tasks. The procedure has been applied successfully to many range images obtained from various institutions, 11 of which are presented here.

Context-based vision: recognizing objects using information from both 2D and 3D imagery

Results from an ongoing project concerned with recognizing objects in complex scene domains, especially in the domain that includes the natural outdoor world, are described. Traditional machine recognition paradigms assume either that all objects of interest are definable by a relatively small number of explicit shape models or that all objects of interest have characteristic, locally measurable features. The failure of both assumptions has a dramatic impact on the form of an acceptable architecture for an object recognition system. In this work, the use of the contextual information is a central issue, and a system is explicitly designed to identify and use context as an integral part of recognition that eliminates the traditional dependence on stored geometric models and universal image partitioning algorithms. This paradigm combines the results of many simple procedures that analyze monochrome, color, stereo, or 3D range images. Interpreting the results along with relevant contextual knowledge makes it possible to achieve a reliable recognition result, even when using imperfect visual procedures. Initial experimentation with the system on ground-level outdoor imagery has demonstrated competence beyond what is attainable with other vision systems. >

Model building of three-dimensional polyhedral objects using 3D edge information and hemispheric histogram

An algorithm for extracting edges and plane regions of a polyhedral object in a three-dimensional (3D) range image is described. The object may be Convex or nonconvex. A model of the object is built with the regions extracted. Possible extension to cylindrical objects is also considered. The range images are obtained with a novel range-finder camera that can produce 128 × 256 or 256 × 256 surface element (surfcels) images. The edge detection is accomplished in five steps and yields edges one surfcel wide. The region-finding algorithm relies on the concept of the histogram. The histogram is built with the normals of groups of surfcels (patches) forming the image. Analysis of the hemispheric histogram gives global information on the surface orientation of the visible regions of an object. Once these regions are extracted, they are expanded with a region growing process. Geometric properties of the regions are computed by a simple contour following algorithm. Then, a relational model of the regions is built. The model gathers information that is independent of the position and orientation of the object ill the reference plane and could be Used for object recognition in an unsupervised 3D vision system.