Kevin W. Bowyer - Most-cited papers in ISI Web of Science.

• Combination of Multiple Classifiers Using Local Accuracy Estimates,
  Kevin S. Woods, W. Philip Kegelmeyer, and Kevin W. Bowyer
  IEEE Transactions on Pattern Analysis and Machine Intelligence 19 (4), 405-410, April 1997.
  pdf of this paper.
  We have shown that even if all the individual classifiers have been optimized, dynamic classifier selection by local accuracy is still capable of improving overall performance significantly. By contrast, simple voting techniques, and even a recently proposed CMC algorithm, were not able to show any significant improvement when the individual classifiers were sufficiently optimized.
  

• An Experimental Comparison of Range Image Segmentation Algorithms,
  Adam W. Hoover, Gillian Jean-Baptiste, Xiaoyi Jiang, Patrick Flynn, Horst Bunke, Dmitry Goldgof, Kevin W. Bowyer, David Eggert, Andrew Fitzgibbon, and Robert Fisher.
  IEEE Transactions on Pattern Analysis and Machine Intelligence 18, (7), 673-689, July 1996.
  pdf of this paper.
  This paper evaluates four segmentation algorithms on 80 real images with ground truth and objective performance measures. ... This type of framework for a competitive effort is essentially never used in mainstream computer vision, though it is standard practice in some related areas ... Beside the development of a philosophy of comparative experiment research, an important contribution here is an assessment of the state-of-the-art in planar range image segmentation. Based on our results, we assert that this problem is not 'solved.' This finding may be surprising and possibly controversial. We would welcome an empirical demonstration that the claim is false.
  

• SMOTE: Synthetic Minority Over-sampling TEchnique,
  Nitesh Chawla, Kevin W. Bowyer, Lawrence O. Hall, and W. Philip Kegelmeyer,
  Journal of Artificial Intelligence Research 16, 2002, 321-357.
  pdf of this paper.
  This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples.

• A Survey of Approaches and Challenges in 3D and Multi-modal 3D+2D Face Recognition,
  Kevin W. Bowyer, Kyong Chang, and Patrick J. Flynn,
  Computer Vision and Image Understanding 101 (1), January 2006, 1-15.
  pdf of this paper.
  ... This survey focuses on face recognition performed by matching models of the three-dimensional shape of the face, either alone or in combination with matching corresponding two-dimensional intensity images.
  This article was number one on the CVIU "Top 25" list for the quarters October - December 2005 and January - March of 2006, and in the top ten for seven consecutive quarters.

• The Human ID Gait Challenge Problem: Data Sets, Performance, and Analysis,
  Sudeep Sarkar, P. Jonathon Phillips, Zongyi Liu, Isidro Robledo, Patrick Grother, and Kevin W. Bowyer,
  IEEE Transactions on Pattern Analysis and Machine Intelligence 27 (2), February 2005, 162-177.
  pdf of this paper.
  ... To provide a means for measuring progress and characterizing the properties of gait recognition, we introduce the HumanID Gait Challenge Problem. The challenge problem consists of a baseline algorithm, a set of 12 experiments, and a large data set. The baseline algorithm estimates silhouettes by background subtraction and performs recognition by temporal correlation of silhouettes. The 12 experiments are of increasing difficulty, as measured by the baseline algorithm, and examine the effects of five covariates on performance. ...
  

• A Robust Visual Method for Assessing the Relative Performance of Edge-Detection Algorithms,
  Michael Heath, Sudeep Sarkar, Thomas A. Sanocki, and Kevin W. Bowyer.
  IEEE Transactions on Pattern Analysis and Machine Intelligence 19 (12), 1338-1359, December 1997.
  A new method for evaluating edge detection is presented ... The basic measure of performance is a visual rating score which indicates the perceived quality of the edges for identifying an object. ... The novel aspect of this work is the use of a visual task and real images of complex scenes in evaluating edge detectors.
  pdf of this paper.

• An Evaluation of Multi-modal 2D+3D Face Biometrics,
  Kyong I. Chang, Kevin W. Bowyer, and Patrick J. Flynn,
  IEEE Transactions on Pattern Analysis and Machine Intelligence 27 (4), April 2005, 619-624.
  pdf of this paper.
  We report on the largest experimental study to date in multi-modal 2D+3D face recognition ... Major conclusions are: (1) 2D and 3D have similar recognition performance when considered individually, (2) Combining 2D and 3D results using a simple weighting scheme outperforms either 2D or 3D alone, (3) Combining results from two or more 2D images using a similar weighting scheme also outperforms a single 2D image, and (4) Combined 2D+3D outperforms the multi-image 2D result. This is the first (so far, only) work to present such an experimental control to substantiate multi-modal performance improvement."