Dr. David G. Stork
Rambus Fellow at Rambus Inc
SPIE Involvement:
Author | Instructor
Publications (39)

Proceedings Article | 14 May 2018
Proc. SPIE. 10656, Image Sensing Technologies: Materials, Devices, Systems, and Applications V
KEYWORDS: Optical design, Digital signal processing, Optical signal processing, Computational imaging, Visualization, Imaging systems, Sensors, Image sensors, Visual optics, Information visualization

Proceedings Article | 1 May 2017
Proc. SPIE. 10222, Computational Imaging II
KEYWORDS: Optical components, Digital signal processing, Optical signal processing, Imaging systems, Sensors, Matrices, Image processing, Signal processing, Sensing systems, Compressed sensing

SPIE Journal Paper | 13 February 2014
JEI Vol. 23 Issue 01
KEYWORDS: Sensors, Image sensors, Binary data, Signal to noise ratio, Electrons, Monte Carlo methods, Cameras, High dynamic range imaging, Statistical modeling, Photodetectors

Proceedings Article | 21 February 2012
Proc. SPIE. 8291, Human Vision and Electronic Imaging XVII
KEYWORDS: Statistical analysis, 3D image reconstruction, Data modeling, Cameras, Error analysis, 3D modeling, Image analysis, Electronic imaging, Statistical modeling, Expectation maximization algorithms

Proceedings Article | 21 February 2012
Proc. SPIE. 8291, Human Vision and Electronic Imaging XVII
KEYWORDS: MATLAB, Detection and tracking algorithms, Image segmentation, Image restoration, Image analysis, Colorimetry, Digital imaging, Quantization, Reconstruction algorithms, Algorithm development

Showing 5 of 39 publications
Proceedings Volume Editor (3)

SPIE Conference Volume | 1 February 2011

SPIE Conference Volume | 2 February 2010

SPIE Conference Volume | 18 March 2008

Conference Committee Involvement (3)
Computer Vision and Image Analysis of Art II
26 January 2011 | San Francisco Airport, California, United States
Computer Vision and Image Analysis of Art
18 January 2010 | San Jose, California, United States
Computer Image Analysis in the Study of Art
28 January 2008 | San Jose, California, United States
Course Instructor
SC965: Joint Design of Optics and Image Processing for Imaging Systems
For centuries, optical imaging system design centered on exploiting the laws of the physics of light and materials (glass, plastic, reflective metal, ...) to form high-quality (sharp, high-contrast, undistorted, ...) images that "looked good." In the past several decades, the optical images produced by such systems have been ever more commonly sensed by digital detectors and the image imperfections corrected in software. The new era of electro-optical imaging offers a more fundamental revision to this paradigm, however: now the optics and image processing can be designed jointly to optimize an end-to-end digital merit function without regard to the traditional quality of the intermediate optical image. Many principles and guidelines from the optics-only era are counterproductive in the new era of electro-optical imaging and must be replaced by principles grounded on both the physics of photons and the information of bits. This short course will describe the theoretical and algorithmic foundations of new methods of jointly designing the optics and image processing of electro-optical imaging systems. The course will focus on the new concepts and approaches rather than commercial tools.
SC1219: Joint design of optics and image processing in computational sensing and imaging
This course provides an overview of the motivation, theory and basic examples of joint design of optics and image processing in computational sensing and imaging systems in which image processing performs significant, non-trivial role in creating the final digital output. 1) The course will review the history of optical imaging and its four revolutions: 1) forming a real image with lenses and curved mirrors through basic physical optics, 2) fixing the image with silver halide through photography, 3) capturing the image using CCDs and CMOS image sensors through digital photography, and 4) deep integration of optics and signal processing through computational imaging, where an image or image estimate is not simply captured but instead computed. 2) Joint design of imaging systems based on traditional lenses and linear signal processing, including electro-optical compensation for manufacturing variations. 3) Lensless computational sensing and imaging based on diffraction (rather than refraction or reflection). 4) Joint design of application-specific sensors, in which the output is not a two-dimensional digital image but instead a numerical output or discrete decision based on the input scene.
SC814: Computer Vision, Image Understanding, and the Analysis of Master Drawings and Paintings
This course is an introduction to the application of computer vision and image analysis to problems in art and art history, specifically realist art. Realist paintings are a rich source of information, both of the scene portrayed and the techniques the artist used to render that scene. Students will learn the principles of perspective and how to apply perspective analysis to paintings to infer vanishing points, locate perspective inconsistencies and to determine whether the artist used perspective constructions or tools. Students will learn how to infer the number, color, and position of light sources based on position, color and blur of cast shadows and highlights along occluding boundaries. Students will learn how to estimate sizes of depicted objects based on perspective and fiducial or reference objects or relationships. Students will learn how to estimate "camera parameters" of the artist (or imaging system), such as the effective magnification, focal length and in some cases aberrations. Some of these methods require no more than ruler and pencil, others require commercial software (e.g., Adobe Illustrator), others were adapted from their use in forensic analysis of digital photographs and require powerful commercial image processing packages (including ones based on C++, Matlab, Mathematica), and yet others require researchers to write special code. This course will be excellent introduction and background for research presented in symposium EI122, "Computer image analysis in the study of art."
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