This paper presents a comparative study and survey of model-based object-recognition algorithms for robot vision. The goal of these algorithms is to recognize the identity, position, and orientation of randomly oriented industrial parts. In one form this is commonly referred to as the “bin-picking ” problem, in which the parts to be recognized are presented in a jumbled bin. The paper is organized according to 2-D, 2&D, and 3-D object representations, which are used as the basis for the recognition algorithms. Three

Edge detection is the process that attempts to characterize the intensity changes in the image in terms of the physical processes that have originated them. A critical, intermediate goal of edge detection is the detection and characterization of significant intensity changes. This paper discusses this part of the edge d6tection problem. To characterize the types of intensity changes derivatives of different types, and possibly different scales, are needed. Thus, we consider this part of edge detection as a problem in numerical differentiation.

This paper continues ou,' work' on vlsuM representations of three-dimensional surfaces [Brady and Yuille 1984b]. The theoretical component o our work is a study of classes of surface curves as a source of constraint on the surface on which they lie, and as a basis for describing it. We analyze bounding contours, sin face intersections, lines of cunature, and asymptotes. Our experimental work hives.igates whether the information suggested by our theoretical study can be computed reliably mid efficiently. We demonstrate algorithms that compute lines of curvature of a (Gaussian smoothed) surface; determine planar patches and umbi!ic regions; extract axes of surfaces of revolution and tube surfaces. We report preliminary results on adapting the curvature primM sketch algorithms of Asada and Brady [1984] to detect and describe surface intersections. () Massachusetts Institute of Technology, 1984 This report describes research done at the Artificial Intelligeice Laboratory of the Massachusetts Institute of Technology. Support for the ]aboratory's Artificial Intelligence reseat.oh is provided in par. by the Adwmced Research Projects Agency of the Department of Defense under Office of Naval Research contract N00014-80-C-0505, the Office of Nax'al Research under contract number N000t4-77-C-0389, ,and the System Development Foundation. This wcrk was done while Haruo Asada was a visiting scientist at MIT on leave from Toshiba Corporation, Japan, and while Jean Ponce was a visking s.ientist on leave from I.'RIA, Paris, Fro,nee. ' Pr't of (t6:7)

An extremum principle is developed that determines three-dimensional surface orientation from a two-dimensional contour. The principle maximizes the ratio of the area to the square of the perimeter, a measure of the compactness or symmetry of the three-dimensional surface. I;he principle interprets regular figures correctly and it interprets skew symmetries as oriented real symmetries. The maximum likelihood method approximates the principle on irregular figures, but we show that it consistently overestimates the slant of an ellipse.

Many problems in early vision can be formulated in terms of minimizing an' energy or cost function. Examples are shape-from-shading, edge detection, motion snatysis, structure from motion and surface interpolation (Poggio, Torre and Koch, 1985). It has been shown that all quadratic variational problems, an important subset of early vision tasks, can be "solved" by linear, analog electrical or chemical networks (Poggio and Koch, 1985). In a variety of situations the cost function is non-quadratic, however, for instance in the presence of discontinuities. The use of non-quadratic cost functions raises the question of designing efficient algorithms for computing the optimal solution. Recently. Hopfield and Tank (1985) have shown that networks of nonlinear analog "neurons" can be effect. lye in computing the solution of optimization problems, In this paper, we show how these networks can be generalized to solve the non-convex energy functionals of early vision. We illustrate this approach by implementing a specific network solving the problem of reconstructing a smooth surface while preserving its discontinuities from sparsely sampled data (Geman and Geman, 1984; Marroquin, 1984; Terzopoulos, 1984). These results suggest a novel computational strategy for solving such problems for both biological and artificial vision systems.

Computer Science to the memory of my mother Acknowledgments This dissertation would not have been possible without the help of many people. First, I would like to thank my committee for their many helpful comments and suggestions. Specifically, Al Hanson who taught me about computer vision, Wayne Burleson who taught me about VLSI, and Don Towsley who taught me about performance evaluation. Most especially, I’d like to thank my committee chair and my advisor and mentor for my entire graduate career, Chip Weems. Besides teaching me about architecture and writing, he suggested the final form of the topic, pulled me out of many blind alleys, and his vast store of knowledge was a constant help. Many other professors at UMass also contributed to my knowledge of computer science and so helped me with this dissertation. I would especially like to thank Arny Rosenberg who not only taught me theory but more importantly how and where to apply it, and Ed Riseman who’s boundless energy and optimism serves as a model for all of us. The first level of discussion and comments is always with the fellow graduate students in one’s

This paper describes a hand-eye system we developed to perform the binpicking task. Two basic tools are employed: the photometric stereo method and the extended Gaussian image. The photometric stereo method generates the surface normal distribution of a scene. The extended Gaussian image allows us to determine the attitude of the object based on the normal distribution.

Abstract In this paper, we present a detailed model and analysis of several error sources and thier effects on measuring three-dimensional (3D) surface properties using the structured lighting technique. The analysis is based on a general system configuration and identifies three types of error surces--system modeling error, image processing error and experimental error. Absolute and relative error bounds in obtaining 3D surface orientation and curvature measurements using structured lighting are derived in terms of the system parameters and likely error sources. In addition to the quantization error, other likely error sources in system modeling and experimental setup are also considered. Even though our analysis is on structured lighting, the results are readily applicable to other triangulation-based techniques such as stereopsis. Finally, our analysis focuses on error in inferring surface orientation and principal surface curvature. Such analyses, to our knowledge, have never been attempted before. Image processing Structured light Orientation Curvature Error analysis 1.

In this paper, we propose a scheme for 3D model construction by fusing heterogeneous sensor data. The proposed scheme is intended for use in an environment where multiple, heterogeneous sensors operate asynchronously. Surface depth, orientation, and curvature measurements obtained from multiple sensors and vantage points are incorporated to construct a computer description of the imaged object. The proposed scheme uses Kalman filter as the sensor data integration tool and hierarchical spline surface as the recording data structure. Kalman filter is used to obtain statistically optimal estimates of the imaged surface structure based on possibly noisy sensor measurements. Hierarchical spline surface is used as the representation scheme because it maintains high-order surface derivative continuity, may be adaptively refined, and is storage efficient. We show in this paper how these mathematical tools can be used in designing a modeling scheme to fuse heterogeneous sensor data.

viii . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Declaration x . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgment xi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 1 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Introduction 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Infrared Images 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Background 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Tools Used For Algorithm Development 5 . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 Structure of the Thes...