Empirical Evaluation Techniques In Computer Vision
Kevin W. Bowyer, P. Jonathon Phillips
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.
Table of Contents:
Overview of Work in Empirical Evaluation of Computer Vision Algorithms (Kevin W. Bowyer and P. Jonathon Phillips).
A Blinded Evaluation and Comparison of Image Registration Methods (J. Michael Fitzpatrick and Jay B. West).
A Benchmark for Graphics Recognition Systems (Atul K. Chhabra and Ihsin T. Phillips).
Performance Evaluation of Clustering Algorithms for Scalable Image Retrieval (mohammed Abdel-Mottaleb, Santhana Krishnamachari, and Nicholas J. Mankovich).
Analysis of PCA-Based Face Recognition Algorithms (Hyeonjoon Moon and P. Jonathan Phillips).
Performance Assessment by Resampling: Rigid Motion Estimators (Bogdan Matei, Peter Meer, and David Tyler).
Sensor Errors and the Uncertainties in Stereo Reconstruction (Gerda Kamberova and Ruzena Bajcsy).
Fingerprint Image Enhancement: Algorithm and Performance Evaluation (Lin Hong, Yifei Wan, and Anil Jain).
Empirical Evaluation of Laser Radar Recognition Algorithms Using Synthetic and Real Data (Sandor Der and Qinfen Zheng).
A WWW-Accessible Database for 3D Vision Research (Patrick J. Flynn and Richard J. Campbell).
Shape of Motion and the Perception of Human Gaits (Jeffrey E. Boyd and James J. Little).
Empirical Evaluation of Automatically Extracted Road Axes (Christain Wiedemann, Christian Heipke, Helmut Mayer, and Olivier Jamet).
Analytical and Empirical Performance Evaluation of Subpixel Line and Edge Detection (Carsten Steger).
Objective Evaluation of Edge Detectors Using a Formally Defined Framework (Sean Dougherty and Kevin W. Bowyer).
An Objective Comparison Methodology of Edge Detection Algorithms Using a Structure from Motion Task (Min C. Shin, Dmitry Goldgof, and Kevin W. Bowyer).