This book addresses an area of perception engineering which deals with constructive processes A model of the environment is analyzed using the information acquired from mUltiple viewpoints of multiple disparate sensors at multiple time instants Although the role of successive model building and active exploration of the environment, as is discussed in this book, is of great importance, only a few researchers of machine perception have thus far addressed the problem in these directions Krotkov's book, which is a modification and continuation of his highly successful dissertation, focuses on active exploratory sensing in the context of spatial layout perception He uses stereo and focus to obtain distance By information, and to eventually develop cooperative combining techniques means of a stereo system with verging cameras, it is demonstrated that the distance measurements can be significantly improved by combining two sources In addition, the problem of merging information from the multiple views is discussed in detail As the field of perception engineering seems to be of growing scientific and applied importance, both practitioners and researchers in machine perception will find this book a valuable addition to their libraries RameshJain Series Editor Acknowledgements I would like to thank Professor Ruzena Bajcsy for her constant encouragement and guidance during the five years of research leading to the dissertation upon which this book is based Without her help in all matters, this work would never have been possible

Active Computer Vision by Cooperative Focus and Stereo

The relationship between the geometry of a stereo camera setup and the accuracy in obtaining three-dimensional position information is of great practical importance in many imaging applications. Assuming a point in a scene has been correctly identified in each image, its three-dimensional position can be recovered via a simple geometrical method known as triangulation. The probability that position estimates from triangulation are within some specified error tolerance is derived. An ideal pinhole camera model is used and the error is modeled as known spatial image plane quantization. A point's measured position maps to a small volume in 3-D determined by the finite resolution of the stereo setup. With the assumption that the point's actual position is uniformly distributed inside this volume, closed form expressions for the probability distribution of error in position along each coordinate direction (horizontal, vertical, and range) are derived. Following this, the probability that range error dominates over errors in the point's horizontal or vertical position is determined. It is hoped that the results presented will have an impact upon both sensor design and error modeling of position measuring systems for computer vision and related applications.

Error Analysis in Stereo Determination of 3-D Point Positions

A method of recognizing partially occluded objects is presented in which each object is represented by a set of landmarks. Given a scene consisting of partially occluded objects, a model object in the scene is hypothesized by matching the landmarks of the model with those in the scene. A measure of similarity between two landmarks is needed to perform this matching. A local shape measure, sphericity, is introduced. It is shown that any invariant function under a similarity transformation is a function of the sphericity. To match landmarks between the model and the scene, a table of compatibility is constructed. A technique, known as hopping dynamic programming, is described to guide the landmark matching through the compatibility table. The location of the model in the scene is estimated with a least-squares fit among the matched landmarks. A heuristic measure is then computed to decide if the model is in the scene. >

Partial shape recognition: a landmark-based approach

The probability density function of the range estimation error and the expected value of the range error magnitude are derived in terms of the various design parameters of a stereo imaging system. In addition, the relative range error is proposed as a better way of quantifying the range resolution of a stereo imaging system than the percent range error when the depths in the scene lie within a narrow range. >

Stochastic analysis of stereo quantization error

Human Engineering Guide to Equipment Design

The distance between an object and stereo vision sensors can be measured using image processing and known system parameters. A detailed distance measurement synthesis procedure to meet system specifications is presented and illustrated with an example. An error analysis shows that error is proportional to distance. System parameters such as separation between sensor elements, sensor focal length, and sensor array dimensions are related in the design and error equations presented. The main desired design goal is to establish the smallest image sensor array size which will meet system operating specifications. Minimum and maximum distance, object height, optic parameters, scene shift, and sensor array parameters are related.

Jong W. Lee

Papers

Some Accuracy and Resolution Aspects of Computer Vision Distance Measurements