Empirical Evaluation Techniques in Computer Vision. Practitioners

  • ID: 2181784
  • Book
  • 262 Pages
  • John Wiley and Sons Ltd
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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.

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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).

Author Index.
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Kevin W. Bowyer
P. Jonathon Phillips
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