Showing papers by "Takeo Kanade published in 1993"

A multiple-baseline stereo

Abstract: A stereo matching method that uses multiple stereo pairs with various baselines generated by a lateral displacement of a camera to obtain precise distance estimates without suffering from ambiguity is presented. Matching is performed simply by computing the sum of squared-difference (SSD) values. The SSD functions for individual stereo pairs are represented with respect to the inverse distance and are then added to produce the sum of SSDs. This resulting function is called the SSSD-in-inverse-distance. It is shown that the SSSD-in-inverse-distance function exhibits a unique and clear minimum at the correct matching position, even when the underlying intensity patterns of the scene include ambiguities or repetitive patterns. The authors first define a stereo algorithm based on the SSSD-in-inverse-distance and present a mathematical analysis to show how the algorithm can remove ambiguity and increase precision. Experimental results with real stereo images are presented to demonstrate the effectiveness of the algorithm. >

Visual tracking of a moving target by a camera mounted on a robot: a combination of control and vision

Abstract: The authors present algorithms for robotic (eye-in-hand configuration) real-time visual tracking of arbitrary 3D objects traveling at unknown velocities in a 2D space (depth is given as known). Visual tracking is formulated as a problem of combining control with computer vision. A mathematical formulation of the control problem that includes information from a novel feedback vision sensor and represents everything with respect to the camera frame is presented. The sum-of-squared differences (SSD) optical flow is used to compute the vector of discrete displacements each instant of time. These displacements can be fed either directly to a PI (proportional-integral) controller or to a pole assignment controller or discrete steady-state Kalman filter. In the latter case, the Kalman filter calculates the estimated values of the system's states and the exogenous disturbances, and a discrete LQG (linear-quadratic Gaussian) controller computes the desired motion of the robotic system. The outputs of the controllers are sent to the Cartesian robotic controller. Performance results are presented. >

DigitEyes: Vision-Based Human Hand Tracking

Abstract: Passive sensing of human hand and limb motion is important for a wide range of applications from human-computer interaction to athletic performance measurement. High degree of freedom articulated mechanisms like the human hand are difficult to track because of their large state space and complex image appearance. This article describes a model-based hand tracking system, called DigitEyes, that can recover the state of a 27 DOF hand model from gray scale images at speeds of up to 10 Hz. We employ kinematic and geometric hand models, along with a high temporal sampling rate, to decompose global image patterns into incremental, local motions of simple shapes. Hand pose and joint angles are estimated from line and point features extracted from images of unmarked, unadorned hands, taken from one or more viewpoints. We present some preliminary results on a 3D mouse interface based on the DigitEyes sensor.

Uncertainty in object pose determination with three light-stripe range measurements

Abstract: The pose (position and orientation) of a polyhedral object can be determined by using a set of simple light-stripe range sensors. Data from these sensors, however, inherently contains errors which result in pose uncertainty. Therefore, in addition to calculating the pose itself, it is often desirable to estimate how certain the pose determination is. This paper presents a method for estimating the uncertainty in determining the pose of an arbitrarily positioned object with three light-stripe range finders. We analyze the perturbation of a least squares fit of sensed 3D segments to object faces, and obtain a relationship between the sensing error and the object pose error. Experiments demonstrate that the method provides the estimate of accuracy in pose determination. >

A paraperspective factorization method for shape and motion recovery

Abstract: The factorization method, first developed by Tomasi and Kanade (1992), recovers both the shape of an object and its motion from a sequence of images, using many images and tracking many feature points to obtain highly redundant feature position information The method robustly processes the feature trajectory information using singular value decomposition (SVD), taking advantage of the linear algebraic properties of orthographic projection However, an orthographic formulation limits the range of motions the method can accommodate Paraperspective projection, first introduced by Ohta et al (1981), is a projection model that closely approximates perspective projection by modeling several effects not modeled under orthographic projection, while retaining linear algebraic properties Our paraperspective factorization method can be applied to a much wider range of motion scenarios, including image sequences containing motion toward the camera and aerial image sequences of terrain taken from a low-altitude airplane

Real-time 3-D pose estimation using a high-speed range sensor

Abstract: This paper describes a system which can perform full 3-D pose estimation of a single arbitrarily shaped, rigid object at rates up to 10 Hz. A triangular mesh model of the object to be tracked is generated offline using conventional range sensors. Real-time range data of the object is sensed by the CMU high speed VLSI range sensor. Pose estimation is performed by registering the real-time range data to the triangular mesh model using an enhanced implementation of the Iterative Closest Point (ICP) Algorithm introduced by Besl and McKay (1992). The method does not require explicit feature extraction or specification of correspondence. Pose estimation accuracies of the order of 1% of the object size in translation, and 1 degree in rotation have been measured. >

CMU Very Fast Range-Imaging System

Abstract: : We present a high-speed range-imaging system based on a VLSI computational sensor developed in the CMU Computer Science Department. The VLSI range sensor is a custom chip consisting of an array of cells which combine photo-sensing and computation. Unlike conventional 'step-and-repeat' light- stripe range finders, our sensor gathers range images in parallel as a scene is swept by a continuously moving, plane of light. A prototype range-finding system has been built using a second-generation sensor and is capable of acquiring a 32 x 32 point frame of 3-D measurements in a millisecond - two orders of magnitude faster than currently available range-finding systems. The accuracy and the repeatability of the acquired range data has been measured to he less than 0.2%. In this paper, we discuss the range-finding system and present experimental results that measure its performance.