This paper describes a volumetric view-merging algorithm that generates a consensus surface of an object from its range images. Our original method merges a set of range images into a volumetric implicit-surface representation, which is converted to a surface mesh by using a variant of the marching-cubes algorithm. We propose a method that increases the computation and memory efficiency for computing signed distances and the method of parallel computing on a PC cluster Since our method permits a reduction in the data amount allocated in memory, the closest point is searched efficiently; this allows us to increase the number of parallel traversals and to reduce the computation time. In this paper, we describe the following two algorithms which are complementary in terms of the efficiency of CPU and memory usage: distributed allocation of range data and parallel traversal of partial octrees. By adjusting them according to the system specifications, we can build the model efficiently by a PC cluster We have implemented this system and evaluated its performance.

In this paper, we present a robust method for creating a triangulated surface mesh from multiple range images. Our method merges a set of range images into a volumetric implicit-surface representation which is converted to a surface mesh using a variant of the marching-cubes algorithm. Unlike previous techniques based on implicit-surface representations, our method estimates the signed distance to the object surface by finding a consensus of locally coherent observations of the surface. We call this method the consensus-surface algorithm. This algorithm effectively eliminates many of the troublesome effects of noise and extraneous surface observations without sacrificing the accuracy of the resulting surface. We utilize octrees to represent volumetric implicit surfaces-effectively reducing the computation and memory requirements of the volumetric representation without sacrificing accuracy of the resulting surface. We present results which demonstrate that our consensus-surface algorithm can construct accurate geometric models from rather noisy input range data.

Kyushu Electric has developed a dual-armed mobile-robot for use in electricity distribution systems. Although some human intervention is still required, the robot greatly reduces the demands on the human operator. In order to automate some of the robot's capabilities, we have developed a 3D object-localization method for robot positional adjustment. The method is designed to be insensitive to noise and outliers while, at the same time, it has optimal run-time efficiency. The paper first describes our algorithm, and then presents a performance evaluation.

An object model for computer graphics applications should contain two aspects of information: shape and reflectance properties of the object.A number of techniques have been developed for modeling object shapes by observing real objects.In contrast, attempts to model reflectance properties of real objects have been rather limited.In most cases, modeled reflectance properties are too simple or too complicated to be used for synthesizing realistic images of the object.In this paper, we propose a new method for modeling object reflectance properties, as well as object shapes, by observing real objects.First, an object surface shape is reconstructed by merging multiple range images of the object.By using the reconstructed object shape and a sequence of color images of the object, parameters of a reflection model are estimated in a robust manner.The key point of the proposed method is that, first, the diffuse and specular reflection components are separated from the color image sequence, and then, reflectance parameters of each reflection component are estimated separately.This approach enables estimation of reflectance properties of real objects whose surfaces show specularity as well as diffusely reflected lights.The recovered object shape and reflectance properties are then used for synthesizing object images with realistic shading effects under arbitrary illumination conditions.

Recognizing a target in synthetic-aperture radar (SAR) images is an important, yet challenging, application of the model-based vision technique. This paper describes a model-based SAR recognition system based on invariant histograms and deformable template matching techniques. An invariant histogram is a histogram of invariant values defined by geometric features such as points and lines in SAR images. Although a few invariants are sufficient to recognize a target, we use a histogram of all invariant values given by all possible target feature pairs. This redundant histogram enables robust recognition under severe occlusions typical in SAR recognition scenarios. Multi-step deformable template matching examines the existence of an object by superimposing templates over potential energy field generated from images or primitive features. It determines the template configuration which has the minimum deformation and the best alignment of the template with features. The deformability of the template absorbs the instability of SAR features. We have implemented the system and evaluated the system performance using hybrid SAR images, generated from synthesized model signatures and real SAR background signatures.

This paper presents a general and robust approach to hand action perception for automatic robot programming using depth image sequences. The human instructor must simply demonstrate an assembly task in front of a vision system in the human world; no dataglove or special markings are necessary. The recorded image sequences are used to recover a depth image sequence for model-based human hand and object tracking to form the perceptual data stream. The data stream is then segmented and interpreted for generating a task sequence which describes the human hand action and the relationship between the manipulated object and the hand. The task sequence might be composed of a series of subtasks and each subtask involves four phases: approaching, pre-manipulating, manipulating and departing. In this paper we also discuss a robot system that replicates the observed task and automatically validates the replication results in the robot world.

We present a linear smoothing operator which has low-pass characteristics similar to a Butterworth filter and limited spatial extent similar to a Gaussian. The smoothing operator also has closed forms in the spatial and frequency domains which facilitate analysis and implementation. A formula is derived that allows us to explicitly control shrinkage.

In an effort to make object recognition efficient and accurate enough for real applications; we have developed three probabilistic techniques-sensor modeling, probabilistic hypothesis generation, and robust localization-which form the basis of a promising paradigm for object recognition. Our techniques effectively exploit prior knowledge to reduce the number of hypotheses that must be tested during recognition. Our recognition approach utilizes statistical constraints on the matches between image and model features. These statistical constraints are computed using a model of the entire sensing process-resulting in more realistic and tighter constraints on matches. The candidate hypotheses are pruned by probabilistic constraint satisfaction to select likely matches based on the image evidence and prior statistical constraints. The resulting hypotheses are ordered most-likely first for verification. Thus minimizing unnecessary verifications. The reliability of the verification decision is significantly increased by the use of a robust localization algorithm.>