Kevin W. Bowyer - Selected and Recent Publications

Sensed 3D head shape rendered as a texture-less surface, from 3D face recognition project.

~~~

Infrared (long-wave) face image, also from biometrics research project.

~~~

The texture pattern in the image of the iris region can be used for identity verification.

Selected and Recent Publications

Copyright Notice.

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

All works below that appear in an IEEE publication have the copyright assigned to IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Similar conditions apply to works appearing in publications by other publishers.

PDF versions posted here may not have the final edits and page numbers of the version appearing in a journal or conference.

The research efforts reported on here have been supported all or in part by the Central Intelligence Agency, the National Science Foundation, Sandia National Labs and other agencies. Any opinions, findings, and conclusions or recommendations expressed are those of the author(s) and do not necessarily reflect the views of the sponsor(s).

Pupil Dilation Degrades Iris Biometric Performance,
Karen Hollingsworth, Kevin W. Bowyer and Patrick J. Flynn,
Computer Vision and Image Understanding, to appear.
pdf of this paper.
... We found that when the degree of dilation is similar at enrollment and recognition, comparisons involving highly dilated pupils result in worse recognition performance than comparisons involving constricted pupils. We also found that when the matched images have similarly highly dilated pupils, the mean Hamming distance of the match distribution increases and the mean Hamming distance of the non-match distribution decreases, bringing the distributions closer together from both directions. We further found that when matching enrollment and recognition images of the same person, larger differences in pupil dilation yield higher template dissimilarities, and so a greater chance of a false non-match. ...
Our research group is part of the Multiple Biometric Grand Challenge and Iris Challenge Evaluation support teams.
The Best Bits in an Iris Code,
Karen Hollingsworth, Kevin W. Bowyer and Patrick J. Flynn,
IEEE Transactions on Pattern Analysis and Machine Intelligence, to appear.
pdf of this paper.
... The fractional Hamming distance weights all bits in an iris code equally. However, not all the bits in an iris code are equally useful. Our research is the first to present experiments documenting that some bits are more consistent than others. ... The inconsistencies are largely due to the coarse quantization of the phase response. Masking iris code bits corresponding to complex filter responses near the axes of the complex plane improves the separation between the match and nonmatch Hamming distance distributions.
Our research group is part of the Multiple Biometric Grand Challenge and Iris Challenge Evaluation support teams.
Using Multi-Instance Enrollment to Improve Performance 3D Face Recognition,
Timothy C. Faltemier, Kevin W. Bowyer and Patrick J. Flynn,
Computer Vision and Image Understanding, in press.
DOI link to CVIU version of this paper.
"This paper explores the use of multi-instance enrollment as a means to improve the performance of 3D face recognition. Experiments are performed using the ND-2006 3D face data set which contains 13,450 scans of 888 subjects. This is the largest 3D face data set currently available and contains a substantial amount of varied facial expression. Results indicate that the multi-instance enrollment outperforms a state-of-the-art component-based recognition approach ..."
A Region Ensemble for 3D Face Recognition,
Timothy Faltemier, Kevin W. Bowyer and Patrick J. Flynn,
IEEE Transactions on Information Forensics and Security, 3(1):62-73, March 2008. Digital Object Identifier 10.1109/TIFS.2007.916287.
link to IEEE Xplore version this paper.
... we introduce a new system for 3D face recognition based on the fusion of results from a committee of regions that have been independently matched. ... Rank-one recognition rates of 97.2% and verification rates of 93.2% at 0.1% false accept rate are reported and compared to other methods published on the Face Recognition Grand Challenge v2 data set."
Image Understanding for Iris Biometrics: a Survey,
Kevin W. Bowyer, Karen Hollingsworth and Patrick J. Flynn,
Computer Vision and Image Understanding, 110(2), 281-307, May 2008.
DOI link to CVIU version of this paper.
... Most research publications can be categorized as making their primary contribution to one of the four major modules in iris biometrics: image acquisition, iris segmentation, texture analysis and matching of texture representations. Other important research includes experimental evaluations, image databases, applications and systems, and medical conditions that may affect the iris. ...
Our research group is part of the Multiple Biometric Grand Challenge and Iris Challenge Evaluation support teams.
Using Classifier Ensembles to Label Spatially Disjoint Data,
Larry Shoemaker, Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer and W. Philip Kegelmeyer,
Information Fusion 9(1), 120-133, January 2008.
pdf of this paper.
We describe an ensemble approach to learning from arbitrarily partitioned data. ... We combine a fast ensemble learning algorithm with probabilistic majority voting in order to learn an accurate classifier from such data. ...
Learning to Predict Gender from Irises,
Vince Thomas, Nitesh V. Chawla, Kevin W. Bowyer and Patrick J. Flynn,
IEEE International Conference on Biometrics: Theory, Applications, and Systems (BTAS 07), September 2007.
pdf of this paper.
This paper employs machine learning techniques to develop models that predict gender based on the iris texture features. ...
Our research group is part of the Multiple Biometric Grand Challenge and Iris Challenge Evaluation support teams.
Guest Editorial: Introduction to the Special Issue on Recent Advances in Biometric Systems,
Kevin W. Bowyer, Venu Govindaraju and Nalini Ratha,
IEEE Transactions on Systems, Man and Cybernetics - B 37 (5), October 2007.
pdf of this paper.
"We are pleased to present 14 papers in this special issue devoted to recent advances in biometric systems. ..."
Comment on the CASIA version 1.0 Iris Dataset,
P. Jonathon Phillips, Kevin W. Bowyer and Patrick J. Flynn,
IEEE Transactions on Pattern Analysis and Machine Intelligence 29 (10), October 2007.
pdf of this paper.
"We note that the images in the CASIA version 1.0 iris dataset have been edited so that the pupil area is replaced by a circular region of uniform intensity. We recommend that this dataset is no longer used in iris biometrics research ..."
FRVT 2006 and ICE 2006 Large-Scale Results,
P. J. Phillips, W. T. Scruggs, A. J. O'Toole, P. J. Flynn, K.W. Bowyer, C. L. Schott, and M. Sharpe.
National Institute of Standards and Technology, NISTIR 7408, http://face.nist.gov, 2007.
pdf of this report.
"This report describes the large-scale experimental results from the Face Recognition Vendor Test (FRVT) 2006 and the Iris Challenge Evaluation (ICE) 2006. ..."
Our research group is part of the Multiple Biometric Grand Challenge and Iris Challenge Evaluation support teams.
A Fast Algorithm for ICP-based 3D Shape Biometrics,
Ping Yan and Kevin W. Bowyer.
Computer Vision and Image Understanding, 107 (3), 195-202, September 2007.
pdf of this paper.
"... we present a novel approach, called "Pre-computed Voxel Nearest Neighbor," to reduce the computational time for shape matching in a biometrics context. The approach shifts the heavy computation burden to the enrollment stage, which is done offline. Experiments in 3D ear biometrics with 369 subjects and 3D face biometrics with 219 subjects demonstrate the effectiveness of our approach."
Biometric Recognition Using Three-dimensional Ear Shape,
Ping Yan and Kevin W. Bowyer.
IEEE Transactions on Pattern Analysis and Machine Intelligence 29 (8), 1297-1308, August 2007.
pdf of this paper.
"... We present a complete system for ear biometrics, including automated segmentation of the ear in a profile view image and 3D shape matching for recognition. We evaluated this system with the largest experimental study to date in ear biometrics, achieving a rank-one recognition rate of 97.8% for an identification scenario, and equal error rate of 1.2% for a verification scenario on a database of 415 subjects and 1,386 total probes."
Actively Exploring Face Space(s) for Improved Face Recognition,
Nitesh V. Chawla and Kevin W. Bowyer,
AAAI 2007, Vancouver, July 2007.
pdf of this paper.
"We propose a learning framework that actively explores creation of face space(s) by selecting images that are complementary to the images already represented in the face space. We also construct ensembles of classifiers learned from such actively sampled image sets, which further provides improvement in the recognition rates. ..."
Boosting Lite - Handling Larger Datasets and Slower Base Classifiers,
Lawrence O. Hall, Robert E. Banfield, Kevin W. Bowyer and W. Philip Kegelmeyer.
Multiple Classifier Systems (MCS) 2007, Prague, May 2007.
pdf of this paper.
"... we examine ensemble algorithms (Boosting Lite and Ivoting) that provide accuracy approximating a single classifier, but which require significantly fewer training examples. ..."
A Comparison of Decision Tree Ensemble Creation Techniques,
Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, and W. Philip Kegelmeyer.
IEEE Transactions on Pattern Analysis and Machine Intelligence 29 (1), 173-180, January 2007.
pdf of this paper. appendix to the paper.
"We experimentally evaluate bagging and seven other randomization-based approaches to creating an ensemble of decision tree classifiers. Statistical tests were performed on experimental results from 57 publicly available data sets. ..."
Face Recognition Using 2D, 3D and Infra-Red: Is multi-modal better than multi-sample?
Kevin W. Bowyer, Kyong I. Chang, Patrick J. Flynn and Xin Chen.
Proceedings of the IEEE, 94 (11), 2000-2012, November 2006.
pdf of this paper.
"We compare the performance improvement obtained by combining three-dimensional or infra-red with normal intensity images (a multi-modal approach) to the performance improvement obtained by using multiple intensity images (a multi-sample approach). Combining results from different types of imagery gives significantly higher recognition rates than are obtained by using a single intensity image. However, significantly higher recognition rates are also obtained by combining results from multiple intensity images."
Multiple Nose Region Matching for 3D Face Recognition under varying facial expression,
Kyong I. Chang, Kevin W. Bowyer, and Patrick J. Flynn,
IEEE Transactions on Pattern Analysis and Machine Intelligence, 28 (10), 1695-1700, October 2006.
pdf of this paper.
"An algorithm is proposed for 3D face recognition in the presence of varied facial expressions. It is based on combining the match scores from matching multiple overlapping regions around the nose. Experimental results are presented using the largest database employed to date in 3D face recognition studies, over 4,000 scans of 449 subjects. ..."
Multi-modal Biometrics: An Overview,
Kevin W. Bowyer, et al,
Second Workshop on Multi-Modal User Authentication (MMUA 2006), May 2006, Toulouse, France.
pdf of this paper.
"The topic of multi-modal biometrics has attracted strong interest in recent years. This paper categorizes approaches to multi-modal biometrics based on the biometric source, the type of sensing used, and the depth of collaborative interaction in the processing. This paper also attempts to identify some of the challenges and issues that confront research in multi-modal biometrics."
This paper represents the invited talk given to open the first day of the workshop.
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.
Over 100 citations in Google Scholar.
Experiments With an Improved Iris Segmentation Algorithm,
Xiaomei Liu, Kevin W. Bowyer, and Patrick J. Flynn,
Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID), October 2005, New York, 118-123.
pdf of this paper.
"... We have also developed and implemented an improved iris segmentation and eyelid detection stage of the algorithm, and experimentally verified the improvement in recognition performance using the collected dataset. Compared to Masek's original segmentation approach, our improved segmentation algorithm leads to an increase of over 6% in the rank-one recognition rate."
Infra-Red and Visible-Light Face Recognition,
Xin Chen, Patrick J. Flynn, and Kevin W. Bowyer,
Computer Vision and Image Understanding 99 (3), September 2005, 332-358.
pdf of this paper.
"... We find that in a scenario involving time lapse between gallery and probe, and relatively controlled lighting, (1) PCA-based recognition using visible images outperforms PCA-based recognition using infra-red images, and (2) the combination of PCA-based recognition using visible and infra-red imagery substantially outperforms either one individually..."
This article was number two on the CVIU "Top 25" list for July - September of 2005.
Ensembles of Classifiers from Spatially Disjoint Data,
Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, and W. Philip Kegelmeyer,
Springer-Verlag LNCS 3541: 6th International Workshop on Multiple Classifier Systems (MCS 2005), Monterey, CA, June 2005, 196-205.
pdf of this paper.
"... We describe an ensemble learning approach that accurately learns from data which has been partitioned according to the arbitrary spatial requirements of a large-scale simulation wherein classifiers may be trained only the data local to a given partition. As a result, the class statistics can vary from partition to partition; some classes may even be missing from some partitions."
Overview of the Face Recognition Grand Challenge,
P. Jonathon Phillips, Patrick J. Flynn, Todd Scruggs, Kevin W. Bowyer, Jin Chang, Kevin Hoffman, Joe Marques, Jaesik Min, and William Worek,
Computer Vision and Pattern Recognition (CVPR 2005), San Diego, June 2005, I:947-954.
pdf of this paper.
Our research group is part of the Face Recognition Grand Challenge support team.
Over 200 citations in Google Scholar.
Random Subspaces and Subsampling for 2-D Face Recognition,
Nitesh V. Chawla and Kevin W. Bowyer,
Computer Vision and Pattern Recognition (CVPR 2005) , San Diego, June 2005, II: 582-589.
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."
Ensemble Diversity Measures and Their Application to Thinning,
Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, and W. Philip Kegelmeyer.
Information Fusion 6 (1), March 2005, 49-62.
pdf of this paper.
"... We evaluate thinning algorithms on ensembles created by several techniques on 22 publicly available datasets. When compared to other methods, our percentage correct diversity measure algorithm shows a greater correlation between the increase in voted ensemble accuracy and the diversity value. ... Finally, the methods proposed for thinning again show that ensembles can be made smaller without loss in accuracy.
Improved Range Image Segmentation by Analyzing Surface Fit Patterns,
Jaesik Min and Kevin W. Bowyer.
Computer Vision and Image Understanding 97(2), February 2005, 242-258.
pdf of this paper.
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.
Over 100 citations in Google Scholar.
Comments on "A Parallel Mixture of SVMs for Very Large Scale Problems,"
Xiaomei Liu, Lawrence O. Hall, and Kevin W. Bowyer.
Neural Computation 16 (7), July 2004, 1345-1351.
pdf of this paper.
"... Experiments on the Forest Cover data set show that this parallel mixture is more accurate than a single SVM, with 90.72% accuracy reported on an independent test set. While this accuracy is impressive, the referenced paper does not consider alternative types of classifiers. In this comment, we show that a simple ensemble of decision trees results in a higher accuracy, 94.75%, and is computationally efficient. This result is somewhat surprising and illustrates the general value of experimental comparisons using different types of classifiers."
Learning Ensembles from Bites: a Scalable and Accurate Approach,
Nitesh Chawla, Lawrence O. Hall, Kevin W. Bowyer and W. Philip Kegelmeyer.
Journal of Machine Learning Research 5, April 2004, 421-451.
pdf of this paper.
"... Voting many classifiers built on small subsets of data is a promising approach for learning from massive data sets, one that can utilize the power of boosting and bagging. We propose a framework for building hundreds or thousands of such classifiers on small subsets of data in a distributed environment. Experiments show this approach is fast, accurate, and scalable."
Face Recognition Technology and the Security Versus Privacy Tradeoff,
Kevin W. Bowyer,
IEEE Technology and Society, Spring 2004, 9-20.
pdf of this paper.
"Video surveillance and face recognition systems have become the subject of increased interest and controversy after the September 11 terrorist attacks on the United States. ... This paper analyzes the interplay of technical and social issues involved in the widespread application of video surveillance for person identification."
This paper received a 2005 Award of Excellence from the Society for Technical Communication.
Automated Performance Evaluation of Range Image Segmentation Algorithms,
Jaesik Min, Mark Powell, and Kevin W. Bowyer,
IEEE Transactions on Systems, Man, and Cybernetics - Part B, 34 (1), February 2004, 263-271.
pdf of this paper.
"... We present an automated framework for evaluating the performance of range image segmentation algorithms. Automated tuning of algorithm parameters in this framework results in performance as good as that previously obtained with careful manual tuning by the algorithm developers. ... This framework is demonstrated using range images, but in principle it could be used to evaluate region segmentation algorithms for any type of images."
Is Error-based Pruning Redeemable?,
Lawrence O. Hall, Kevin W. Bowyer, Robert E. Banfield and Steven Eschrich, and Richard Collins.
International Journal of Artificial Intelligence Tools, 12 (3), September 2003, 249-264.
pdf of this paper.
Comparison and Combination of Ear and Face Images for Appearance-based Biometrics,
Kyong Chang, Kevin W. Bowyer, Sudeep Sarkar, and Barnabas Victor,
IEEE Transactions on Pattern Analysis and Machine Intelligence 25 (9), September 2003, 1160-1165.
pdf of this paper.
"In the experiments reported here, recognition performance is essentially identical using ear images or face images and combining the two for multimodal recognition results in a statistically significant performance improvement. ... To our knowledge, ours is the only work to present any experimental results of computer algorithms for biometric recognition based on the ear."
Over 100 citations in Google Scholar.
Face Recognition Using 2D and 3D Facial Data,
Kyong I. Chang, Kevin W. Bowyer and Patrick J. Flynn,
First Workshop on Multi-Modal User Authentication , Santa Barbara, 25-32, December 2003.
pdf of this paper.
"Results are presented for the largest experimental study to date that investigates the comparison and combination of 2D and 3D face recognition. To our knowledge, this is the only such study to incorporate significant time lapse between gallery and probe image acquisition ..."
Over 100 citations in Google Scholar.
Distributed Learning with Bagging-like Performance,
Nitesh Chawla, Thomas E. Moore, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer, and Clayton Springer,
Pattern Recognition Letters 24 (1-3), 2003, 455-471.
pdf of this paper.
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."
Over 200 citations in Google Scholar.
The Gait Identification Challenge Problem: Data Sets and Baseline Algorithm,
P. Jonathon Phillips, Sudeep Sarkar, Isidro Robledo, Patrick Grother, and Kevin W. Bowyer,
International Conference on Pattern Recognition (ICPR 2002) , August 2002, Montreal, I:385-388.
Over 100 citations in Google Scholar.
``Star Wars'' Revisited - A Continuing Case Study In Ethics and Safety-Critical Software,
Kevin W. Bowyer,
IEEE Technology and Society 21 (1), Spring 2002, 13-26.
pdf of this paper.
"The Reagan-era Strategic Defense Initiative was the focus of a great deal of technical argument relating to the design and testing of safety critical software. ... This paper describes a curriculum module developed around a Reagan-era SDI debate on the theme - 'Star Wars: Can the computing requirements be met?' This module should be appropriate for use in ethics-related or software-engineering-related courses taught in undergraduate Information Systems, Information Technology, Computer Science, or Computer Engineering programs."
Edge Detector Evaluation Using Empirical ROC Curves,
Kevin W. Bowyer, Christine Kranenburg, and Sean Dougherty.
Computer Vision and Image Understanding 84 (1), October 2001, 77-103.
"This paper focuses on evaluating the performance of edge detection algorithms. ... Performance is summarized using receiver operating characteristic curves. ... No other work uses adaptive parameter sampling to construct empirical ROC curves for pixel-level performance evaluation, or has used so many real images, or has compared such a broad selection of detectors."
Ethics and Computing: Living Responsibly In a Computerized World,
Kevin W. Bowyer.
IEEE Press (second edition), 2001.
The Digital Database for Screening Mammography,
Michael Heath, Kevin Bowyer, Daniel Kopans, Richard Moore and W. Philip Kegelmeyer. in Proceedings of the Fifth International Workshop on Digital Mammography, M.J. Yaffe, ed., 212-218, Medical Physics Publishing, 2001.
pdf of this paper.
The Digital Database for Screening Mammography (DDSM) is a database of digitized film-screen mammograms with associated ground truth information. The purpose of this resource is to provide a large set of mammograms in a digital format that may be used by researchers to evaluate and compare the performance of computer-aided detection (CAD) algorithms. ...
Current Status of the Digital Database for Screening Mammography,
Michael Heath, Kevin Bowyer, Daniel Kopans, W. Philip Kegelmeyer, Richard Moore, Kyong Chang and S. Munishkumaran. in Digital Mammography, 457-460, Kluwer Acaedemic Publishers, 1998; proceedings of the Fourth International Workshop on Digital Mammography.
pdf of this paper.
The Digital Database for Screening Mammography is a resource for use by researchers investigating mammogram image analysis. In particular, the resource is focused on the context of image analysis to aid is screening for breast cancer. The database now contains substantial numbers of "normal" and "cancer" cases. ...
Over 100 citations in Google Scholar.
Are Real Edges Sufficient for Object Recognition?,
Thomas A. Sanocki, Kevin W. Bowyer, Michael Heath, and Sudeep Sarkar.
Journal of Experimental Psychology: Human Perception and Performance 24 (1), 340-349, January 1998.
"The authors argue that the concept of `edges' as used in current research on object recognition obscures the significant difficulties involved in interpreting stimulus information. ... With 1-s exposures, the accuracy of identifying objects in the edge images was found to be less than half that with color photographs. Therefore, edges are far from being sufficient for object recognition."
Comparison of Edge Detectors: A Methodology and Initial Study,
Michael Heath, Sudeep Sarkar, Thomas A. Sanocki and Kevin W. Bowyer.
Computer Vision and Image Understanding 69, (1), 38-54, January 1998.
Over 100 citations in Google Scholar.
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.
Over 200 citations in Google Scholar.
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."
Over 300 citations in Google Scholar.
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."
Over 300 citations in Google Scholar.
Extracting a Valid Boundary Representation From a Segmented Range Image,
Adam W. Hoover, Dmitry Goldgof, and Kevin W. Bowyer.
IEEE Transactions on Pattern Analysis and Machine Intelligence 17, (9), 920-924, September 1995.
"A new approach is presented for extracting an explicit 3-D shape model from a single range image. One novel aspect is that the model represents both observed object surfaces, and surfaces which bound the volume of occluded space. Another novel aspect is that the approach does not require that the range image segmentation be perfect. ... A third novel aspect of this work is that the implementation has been evaluated on over 200 real images of polyhedral objects with no operator intervention and all parameters held constant, and obtained a 97% success rate in creating valid b-reps."
The Scale Space Aspect Graph,
David W. Eggert, Kevin W. Bowyer, Charles R. Dyer, Henrik I. Christensen, and Dmitry B. Goldgof.
IEEE Transactions on Pattern Analysis and Machine Intelligence 15 (11), 1114-1130, November 1993.
"This paper introduces the concept of the scale space aspect graph, defines three different interpretations of the scale dimension, and presents a detailed example for a simple class of objects, with scale defined in terms of the spatial extent of features in the image."
Computing the Generalized Aspect Graph for Objects with Moving Parts,
Kevin W. Bowyer, Maha Y. Sallam, David Eggert, and John H. Stewman,
IEEE Transactions on Pattern Analysis and Machine Intelligence 15 (6), 605-610, June 1993.
"A number of researchers have described algorithms for computing the aspect graph representation, but this work has thus far been limited to entirely rigid objects. We generalize this concept to include a larger, more realistic domain of objects known as articulated assemblies, those objects composed of rigid parts with articulated connections allowed between parts."
Computing the Perspective Projection Aspect Graph of Solids of Revolution,
David W. Eggert and Kevin W. Bowyer,
IEEE Transactions on Pattern Analysis and Machine Intelligence 15 (2), 109-128, February 1993.
"This paper presents the first (only) implemented algorithm to compute the aspect graph for a class of curved-surface objects based on an exact parcellation of 3-D viewpoint space. The class of objects considered is solids of revolution. ... the worst-case complexity of ... the number of cells in the aspect graph is is O(N^4), where N is the degree of a polynomial that defines the object shape."
Achieving Generalized Object Recognition Through Reasoning About Association of Function To Structure,
Louise Stark and Kevin W. Bowyer.
IEEE Transactions on Pattern Analysis and Machine Intelligence 13 (10), 1097-1104, October 1991.
pdf of this paper.
"The purpose of the work described here is to demonstrate the feasibility of defining an object category in terms of the functional properties shared by all objects in the category. ... A complete system has been implemented that takes the boundary surface description of a 3-D object as its input and attempts to recognize whether the object belongs to the category chair ... This is, to our knowledge the first (only) implemented system to explore the use of a purely function-based definition of an object category ... to recognize 3-D objects."
Over 100 citations in Google Scholar.