Vision and Robotics

Vision and robotics has the well-defined goal of meeting or exceeding human-level capabilities in perception, locomotion, and manipulation. Not surprisingly, that is perhaps easier said than done. Beginning in the 1970s, the Defense Advanced Research Projects Agency started the ambitious Imaging Understanding program that would continue for more than 20 years. The Imaging Understanding program began with fundamental research and slowly evolved into a host of more applied efforts with specific systems goals. Robotics programs followed a similar arc as the early research-oriented programs generated capabilities from which practical systems could be built. A culmination of the vision and robotics research was the Defense Advanced Research Projects Agency Grand Challenge, which turned the impossibility of a self-driving car into an imminent reality. This article tells the story of how some of the modern-day technologies we enjoy today trace their evolution from research sponsored by the Defense Advanced Research Projects Agency over the last 40 years.

[1]  R. Weale.  Vision. A Computational Investigation Into the Human Representation and Processing of Visual Information. David Marr , 1983 .

[2]  Y. J. Tejwani,et al.  Robot vision , 1989, IEEE International Symposium on Circuits and Systems,.

[3]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Takeo Kanade,et al.  Three-Dimensional Machine Vision , 1987 .

[5]  D H Brainard,et al.  Analysis of the retinex theory of color vision. , 1986, Journal of the Optical Society of America. A, Optics and image science.

[6]  Oscar Firschein,et al.  Readings in computer vision: issues, problems, principles, and paradigms , 1987 .

[7]  Michael J. Brooks,et al.  The variational approach to shape from shading , 1986, Comput. Vis. Graph. Image Process..

[8]  T. Binford,et al.  Context and Quasi-Invariants in Automatic Target Recognition (ATR) with Synthetic Aperture Radar (SAR) Imagery , 2000 .

[9]  Larry Jackel,et al.  The DARPA Robotics Challenge Finals: Results and Perspectives , 2017, J. Field Robotics.

[10]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[11]  D Marr,et al.  A computational theory of human stereo vision. , 1979, Proceedings of the Royal Society of London. Series B, Biological sciences.

[12]  Bernt Schiele,et al.  Computer vision systems , 2003, Machine Vision and Applications.

[13]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[14]  Azriel Rosenfeld,et al.  Picture Processing by Computer , 1969, CSUR.

[15]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[16]  Azriel Rosenfeld,et al.  Computer Vision , 1988, Adv. Comput..

[17]  Eric L. W. Grimson,et al.  From Images to Surfaces: A Computational Study of the Human Early Visual System , 1981 .

[18]  S. Ullman.  The Interpretation of Visual Motion , 1979 .

[19]  Yiannis Aloimonos,et al.  Active vision , 2004, International Journal of Computer Vision.

[20]  Takeo Kanade,et al.  Shape and motion from image streams under orthography: a factorization method , 1992, International Journal of Computer Vision.

[21]  Phillipp Meister.  Intelligence The Eye The Brain And The Computer , 2016 .

[22]  W E Grimson,et al.  A computer implementation of a theory of human stereo vision. , 1981, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[23]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Pat Langley,et al.  Improving Rooftop Detection with Interactive Visual Learning , 1998 .