A theory of origami world

Computer Science Departmen

Stereo by intra- and inter-scanline search using dynamic programming

Computer Science Departmen

The 3D Mosaic scene understanding system : incremental reconstruction of 3d scenes from complex images

Computer Science Departmen

Shape and motion from image streams : a factorization method.

Abstract: "The factorization method described in this series of reports requires an algorithm to track the motion of features in an image stream. Given the small inter-frame displacement made possible by the factorization approach, the best tracking method turns out to be the one proposed by Lucas and Kanade in 1981. The method defines the measure of match between fixed-size feature windows in the past and current frame as the sum of squared intensity differences over the windows. The displacement is then defined as the one that minimizes this sum. For small motions, a linearization of the image intensities leads to a Newton- Raphson style minimization.In this report, after rederiving the method in a physically intuitive way, we answer the crucial question of how to choose the feature windows that are best suited for tracking. Our selection criterion is based directly on the definition of the tracking algorithm, and expresses how well a feature can be tracked. As a result, the criterion is optimal by construction. We show by experiment that the performance of both the selection and the tracking algorithm are adequate for our factorization method, and we address the issue of how to detect occlusions. In the conclusion, we point out specific open questions for future research.

A stereo matching algorithm with an adaptive window : theory and experiment

Abstract: "A central problem in stereo matching by computing correlation or sum of squared differences (SSD) lies in selecting an appropriate window size. If the window is too small and does not cover enough intensity variation, it gives a poor disparity estimate, because the signal (intensity variation) to noise ratio is low. If, on the other hand, the window is too large and covers a region in which the depth of scene points varies, then the disparity within the window is not constant. As a result, the position of maximum correlation or minimum SSD may not represent a correct estimate of disparity. For this reason, an appropriate window size must be selected locally. There has been, however, little research directed toward the adaptive selection of matching windows.The stereo algorithm we propose in this paper selects a window adaptively by evaluating the local variation of the intensity and the disparity. We employ a statistical model that represents uncertainty of disparity of points over the window: the uncertainty is assumed to increase with the distance of the point from the center point. This modeling enables us to assess how disparity variation within a window affects the estimation of disparity. As a result, we can compute the uncertainty of the disparity estimate which takes into account both intensity and disparity variances. So, the algorithm can search for a window that produces the estimate of disparity with the least uncertainty for each pixel of an image. The method controls not only the size, but also the shape (rectangle) of the window.The algorithm has been tested on both synthetic and real images, and the quality of the disparity maps obtained demonstrates the effectiveness of the algorithm.

Towards automatic generation of object recognition programs

Computer Science Departmen

User-powered 'content-free' approach to image retrieval

Abstract: "Consider a stereotypical image-retrieval problem; a user submits a set of query images to a system and through repeated interactions during which the system presents its current choices and the user gives his/her preferences to them, the choices are narrowed to the image(s) that satisfies the user. The problem obviously must deal with image content, i.e., interpretation and preference. For this purpose, conventional so-called content-based image retrieval (CBIR) approach uses image-processing and computer-vision techniques, and tries to understand the image content. Such attempts have produced good but limited success, mainly because image interpretation is a highly complicated perceptive process. We propose a new approach to this problem from a totally different angle. It attempts to exploit the human's perceptual capabilities and certain common, if not identical, tendencies that must exist among people's interpretation and preference of images. Instead of processing images, the system simply accumulates records of user feedback and recycles them in the form of collaborative filtering, just like a purchase recommendation system such as Amazo-com.[sic] To emphasize the point that it does not deal with image pixel information, we dub the approach by a term 'content-free' image retrieval (CFIR). We discuss various issues of image retrieval, argue for the idea of CFIR, and present results of preliminary experiment. The results indicate that the performance of CFIR improves with the number of accumulated feedbacks, outperforming a basic but typical conventional CBIR system.

Kinematics of DDArm II

Institute for Software Researc

An Algorithm to Estimate Manipulator Dynamics Parameters

This paper presents algorithms for identifying parameters of an N degrees-of-freedom robotic manipulator. First, we outline the fundamental properties of the Newton-Euler formulation of robot dynamics from the view point of parameter identification. We then show that the Newton-Euler model which is nonlinear in the dynamic parameters can be transformed into an equivalent modified model which is linear in dynamic parameters. We develop both on-line and off-line parameter estimation procedures. To illustrate our approach, we identify the dynamic parameters of the cylindrical robot, and the three degree-of-freedom positioning system of the CMU Direct-Drive Arm II. The experimental implementation of our algorithm to estimate the dynamics parameters of the six degrees-of-freedom CMU DD Arm 11 is also presented.</p

An Algorithm to Estimate Manipulator Dynamics Parameters

This paper presents algorithms for identifying parameters of an N degrees-of-freedom robotic manipulator. First, we outline the fundamental properties of the Newton-Euler formulation of robot dynamics from the view point of parameter identification. We then show that the Newton-Euler model, which is nonlinear in the dynamic parameters, can be transformed into an equivalent modified model which is linear in dynamic parameters. We develop both on-line and off-line parameter estimation procedures. To illustrate our approach, we identify the dynamic parameters of the cylindrical robot, and the three degree-of-freedom positioning system of the CMU DirecbDrive Arm II. The experimental implementation of our algorithm to estimate the dynamics parameters of the six degrees-of-freedom CMU DD Arm II is also presented.</p