The two main motivators in computer vision research are to develop algorithms to solve vision problems and to understand and model the human visual system. This work focuses on developing solutions to vision problems from the computer vision and pattern recognition community's point of view.
Empirical Evaluation Techniques in Computer Vision covers methods that allow comparative assessment of algorithms and the accompanying benefits:
- Places computer vision on solid experimental and scientific grounds
- Assists the development of engineering solutions to practical problems
- Allows accurate assessments of computer vision research
- Provides convincing evidence that computer vision research results in practical solutions
Empirical evaluations are divided into three basic categories providing useful insights into computer vision algorithms. Independently administered evaluations make up the first category. The second is evaluations of a set of classification algorithms by one group. The third category is composed of problems where the ground truth is not self evident. A major component of the evaluation process is to develop a method of obtaining the ground truth.
Empirical evaluations of algorithms are slowly emerging as a serious subfield in computer vision. The text builds a foundation for developing accepted practices for evaluating algorithms that determine the strengths and weaknesses of different approaches while identifying necessary further research. Successful evaluations can help convince potential users that an algorithm has matured to the point that it can be successfully fielded.
In the last decade, as computer vision has matured, methods to evaluate the performance of computer vision algorithms have been developed. The interest is motivated by a desire to place computer vision on solid experimental and scientific grounds, and to facilitate the transfer of algorithms from the laboratory to the marketplace.
The growth of the evaluation field has seen the development of numerous practices and methodologies for evaluating algorithms. The text builds a foundation for developing accepted practices for evaluating algorithms that determine the strengths and weaknesses of different approaches while identifying future research directions.
Empirical Evaluation Techniques in Computer Vision presents methods that allow comparative assessment of algorithms and the accompanying benefits:
- places computer vision on solid experimental and scientific grounds
- assists the development of engineering solutions to practical problems
- allows accurate assessments of computer vision research
- provides convincing evidence that computer vision research results in practical solutions
The chapters in this volume cover the three main paradigms for evaluating computer vision algorithms. The paradigms are: (1) evaluations that are independently administered, (2) evaluation of a set of algorithms by one research group, and (3) evaluation methods that feature ground truthing procedures as a major component. Topics covered include evaluating edge detectors, face recognition algorithms, medical image registration algorithms, graphics recognition algorithms, and performance assessment by resampling methods.
