By
G. Medioni
Mi-Suen Lee
Chi-Keung Tang, Department of Computer Science and Electrical Engineering, Institute for Robotics and Intelligent Systems, University of South California, Los Angeles, CA, USA
Description
This book represents a summary of the research we have been conducting since the early 1990s, and describes a conceptual framework which
addresses some current shortcomings, and proposes a unified approach for a broad class of problems. While the framework is defined, our
research continues, and some of the elements presented here will no doubt evolve in the coming years.It is organized in eight chapters.
In the Introduction chapter, we present the definition of the problems, and give an overview of the proposed approach and its implementation.
In particular, we illustrate the limitations of the 2.5D sketch, and motivate the use of a representation in terms of layers instead.
In chapter 2, we review some of the relevant research in the literature. The discussion focuses on general computational approaches for
early vision, and individual methods are only cited as references. Chapter 3 is the fundamental chapter, as it presents the elements
of our salient feature inference engine, and their interaction. It introduced tensors as a way to represent information, tensor fields
as a way to encode both constraints and results, and tensor voting as the communication scheme. Chapter 4 describes the feature extraction
steps, given the computations performed by the engine described earlier. In chapter 5, we apply the generic framework to the inference
of regions, curves, and junctions in 2-D. The input may take the form of 2-D points, with or without orientation. We illustrate the approach
on a number of examples, both basic and advanced. In chapter 6, we apply the framework to the inference of surfaces, curves and junctions
in 3-D. Here, the input consists of a set of 3-D points, with or without as associated normal or tangent direction. We show a number
of illustrative examples, and also point to some applications of the approach. In chapter 7, we use our framework to tackle 3 early vision
problems, shape from shading, stereo matching, and optical flow computation. In chapter 8, we conclude this book with a few remarks,
and discuss future research directions.
We include 3 appendices, one on Tensor Calculus, one dealing with proofs and details of the Feature
Extraction process, and one dealing with the companion software packages.
