Dr. Terrance E. Boult Profile

Dr. Terrance E. Boult

at Univ of Colorado at Colorado Springs

SPIE Involvement:

Author

Proceedings Article | 29 May 2014 Paper

Remote authentication using vaulted fingerprint verification

Hamdan Alzahrani, Terrance Boult

Proceedings Volume 9075, 90750K (2014) https://doi.org/10.1117/12.2053126

KEYWORDS: Biometrics, Computer security, Security technologies, Lens design, Symmetric-key encryption, Control systems, Fluctuations and noise, Surveillance, Current controlled current source, Detection and tracking algorithms

Read Abstract +

Proceedings Article | 31 May 2013 Paper

Secure voice-based authentication for mobile devices: vaulted voice verification

R. C. Johnson, Walter Scheirer, Terrance Boult

Proceedings Volume 8712, 87120P (2013) https://doi.org/10.1117/12.2015649

KEYWORDS: Biometrics, Computer security, Data modeling, Mobile devices, Binary data, Cell phones, Information security, Instrument modeling, Iris, Statistical modeling

Read Abstract +

Proceedings Article | 31 May 2013 Paper

How reliable are your visual attributes?

W. Scheirer, N. Kumar, V. Iyer, P. Belhumeur, T. Boult

Proceedings Volume 8712, 87120Q (2013) https://doi.org/10.1117/12.2018974

KEYWORDS: Reliability, Visualization, Image quality, Data modeling, Facial recognition systems, Statistical modeling, Binary data, Image processing, Image retrieval, Nose

Read Abstract +

Proceedings Article | 17 March 2008 Paper

FAAD: face at a distance

Terrance Boult, Walter Scheirer, R. Woodworth

Proceedings Volume 6944, 694402 (2008) https://doi.org/10.1117/12.781497

KEYWORDS: Facial recognition systems, Sensors, Biometrics, Directed energy weapons, Lead, Optical resolution, Light sources and illumination, Failure analysis

Read Abstract +

Proceedings Article | 4 May 2007 Paper

24/7 security system: 60-FPS color EMCCD camera with integral human recognition

T. Vogelsong, T. Boult, D. Gardner, R. Woodworth, R. Johnson, B. Heflin

Proceedings Volume 6538, 65381S (2007) https://doi.org/10.1117/12.719690

KEYWORDS: Cameras, Electron multiplying charge coupled devices, Detection and tracking algorithms, Target detection, Image processing, Sensors, Imaging systems, Charge-coupled devices, Field programmable gate arrays, Facial recognition systems

Read Abstract +

Proceedings Article | 2 March 2001 Paper

Tubular mosaics

Michael Eckmann, Terrance Boult

Proceedings Volume 4195, (2001) https://doi.org/10.1117/12.417301

KEYWORDS: Cameras, Video, Mirrors, Sensors, Image resolution, Video compression, Volume rendering, Matrices, Image compression, Navigation systems

Read Abstract +

Proceedings Article | 15 November 1999 Paper

Increasing robustness in self-localization and pose estimation

Ross Micheals, Terrance Boult

Proceedings Volume 3838, (1999) https://doi.org/10.1117/12.369246

KEYWORDS: Algorithm development, Adaptive optics, Computer simulations, Visualization, Chemical elements, Samarium, Computer graphics, Silicon, Radon, Reconstruction algorithms

Read Abstract +

Proceedings Article | 1 December 1993 Paper

G-neighbors

Terrance Boult, Robert Melter, Frank Skorina, Ivan Stojmenovic

Proceedings Volume 2060, (1993) https://doi.org/10.1117/12.165007

KEYWORDS: Smoothing, Binary data, Digital filtering, Vision geometry, Sensors, Image processing, Cameras, Light sources and illumination, Surgery, Nonlinear filtering

Read Abstract +

Proceedings Article | 30 April 1992 Paper

Motion segmentation using singular value decomposition

Terrance Boult, Lisa Brown

Proceedings Volume 1611, (1992) https://doi.org/10.1117/12.57937

KEYWORDS: Image segmentation, Matrices, Sensor fusion, Cameras, Principal component analysis, Motion analysis, Motion estimation, Algorithm development, Detection and tracking algorithms, Visualization

Read Abstract +

Proceedings Article | 30 April 1992 Paper

Introduction of explicit sensor models in error recovery

Thomas O'Donnell, Terrance Boult

Proceedings Volume 1611, (1992) https://doi.org/10.1117/12.57914

KEYWORDS: Sensors, Data modeling, Mathematical modeling, Visual process modeling, 3D modeling, Distance measurement, Sensor fusion, Cameras, Error analysis, Systems modeling

Read Abstract +

Proceedings Article | 1 April 1991 Paper

Energy-based segmentation of very sparse range surfaces

Mark Lerner, Terrance Boult

Proceedings Volume 1383, (1991) https://doi.org/10.1117/12.25264

KEYWORDS: Fusion energy, Reconstruction algorithms, Image segmentation, Sensor fusion, Edge detection, Motion detection, Data processing, Fourier transforms, Optical spheres, Computer science

Read Abstract +

This paper describes a new segmentation technique for very sparse surfaces which is based on minimizing the energy of the surfaces in the scene. While it could be used in almost any system as part of surface reconstruction/model recovery, the algorithm is designed to be usable when the depth information is scattered and very sparse, as is generally the case with depth generated by stereo algorithms. We describe a sequential implementation that constructs seed surfaces, automatically sets thresholds, adds points to the seeds, merges surfaces, and corrects for incorrectly added points. We discuss a parallel implementation that runs on the Connection Machine™. We show results from a sequential algorithm that processes synthetic or range finder data. The idea of segmentation by energy minimization is not new. However, prior techniques have relied on discrete regularization or Markov random fields to model the surfaces to build smooth surfaces and detect depth edges. Both of the aforementioned techniques are ineffective at energy minimization for very sparse data. In addition, out method does not require edge detection and is thus also applicable when edge information is unreliable or unavailable. The technique presented herein models the surfaces with reproducing kernel-based splines which can be shown to solve a regularized surface reconstruction problem. From the functional form of these splines we derive computable bounds on the energy of a surface over a given finite region. The computation of the spline, and the corresponding surface representation are quite efficient for very sparse data. An interesting property of the algorithm is that it makes no attempt to determine segmentation boundaries; the algorithm can be viewed as a classification scheme which partitions the data into collections of points which are “from” the same surface. Among the significant advantages of the method is the capacity to process overlapping transparent surfaces, as well as surfaces with large occluded areas.

Showing 5 of 11 publications

Conference Committee Involvement (4)

Biometric and Surveillance Technology for Human and Activity Identification XII

22 April 2015 | Baltimore, MD, United States

Biometric and Surveillance Technology for Human and Activity Identification XI

8 May 2014 | Baltimore, MD, United States

Biometric and Surveillance Technology for Human and Activity Identification X

2 May 2013 | Baltimore, Maryland, United States

Biometric Technology for Human Identification IV

9 April 2007 | Orlando, Florida, United States

Keywords/Phrases

Search In:

Publication Years