Cornelia Fermüller
Research Scientist
4216 Iribe Center
(301) 405-1768
(301) 314 9115
Research Group(s):
Education:
Ph.D., Technical University of Vienna, Austria (Applied Mathematics)
Biography:
Cornelia Fermüller is a research scientist in the University of Maryland Institute for Advanced Computer Studies.
Her research focuses on computer vision, robotics and human vision, emphasizing biologically-inspired solutions for active vision systems. Fermüller models perception problems using geometry, statistics and signal processing, while developing software for multiple view geometry, motion, navigation and action recognition.
Go here to view Fermüller's academic publications on Google Scholar.
Publications
2011
2011. A Corpus-Guided Framework for Robotic Visual Perception. Workshops at the Twenty-Fifth AAAI Conference on Artificial Intelligence.
2011. Visual Scene Interpretation as a Dialogue between Vision and Language. Workshops at the Twenty-Fifth AAAI Conference on Artificial Intelligence.
2011. Active scene recognition with vision and language. 2011 IEEE International Conference on Computer Vision (ICCV). :810-817.
2011. Language Models for Semantic Extraction and Filtering in Video Action Recognition. Workshops at the Twenty-Fifth AAAI Conference on Artificial Intelligence.
2010
2010. An Experimental Study of Color-Based Segmentation Algorithms Based on the Mean-Shift Concept. Computer Vision – ECCV 2010Computer Vision – ECCV 2010. 6312:506-519.
2010. Better Flow Estimation from Color Images-Volume 2007, Article ID 53912, 9 pages. EURASIP Journal on Advances in Signal Processing. 2007(23)
2010. Illusory Lightness Perception Due to Signal Compression and Reconstruction. Journal of VisionJ Vis. 10(7):426-426.
2010. Illusory motion due to causal time filtering. Vision research. 50(3):315-329.
2010. Learning shift-invariant sparse representation of actions. 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). :2630-2637.
2009
2009. Real-time shape retrieval for robotics using skip Tri-Grams. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009. IROS 2009. :4731-4738.
2009. Robust Wavelet-Based Super-Resolution Reconstruction: Theory and Algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence. 31(4):649-660.
2009. Viewpoint Invariant Texture Description Using Fractal Analysis. International Journal of Computer Vision. 83(1):85-100.
2009. Combining powerful local and global statistics for texture description. IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009. :573-580.
2009. Active segmentation for robotics. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009. IROS 2009. :3133-3139.
2009. MEASURING 1ST ORDER STRETCH WITH A SINGLE FILTER. Relation. 10(1.132):691-691.
2008
2008. Bilateral symmetry of object silhouettes under perspective projection. 19th International Conference on Pattern Recognition, 2008. ICPR 2008. :1-4.
2008. A View-Point Invariant Texture Descriptor. Journal of VisionJ Vis. 8(6):354-354.
2008. Measuring 1st order stretchwith a single filter. IEEE International Conference on Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. :909-912.
2007
2007. Object Detection Using Shape Codebook. British Machine Vision Conference (BMVC'07).
2007. Better flow estimation from color images. EURASIP Journal on Advances in Signal Processing. 2007(1):133-133.
2007. Object detection using a shape codebook. British Machine Vision Conference. 4
2007. Combining motion from texture and lines for visual navigation. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007. IROS 2007. :232-239.
2006
2006. A 3D shape constraint on video. IEEE Transactions on Pattern Analysis and Machine Intelligence. 28(6):1018-1023.
2006. Noise causes slant underestimation in stereo and motion. Vision Research. 46(19):3105-3120.
2006. A Projective Invariant for Textures. 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2:1932-1939.
2006. Wavelet-Based Super-Resolution Reconstruction: Theory and Algorithm. Computer Vision – ECCV 2006Computer Vision – ECCV 2006. 3954:295-307.
2006. Depth estimation using the compound eye of dipteran flies. Biological Cybernetics. 95(5):487-501.
2005
2005. Motion segmentation using occlusions. IEEE Transactions on Pattern Analysis and Machine Intelligence. 27(6):988-992.
2005. On the Anisotropy in the Perception of Stereoscopic Slant. Journal of VisionJ Vis. 5(8):516-516.
2005. Chromatic Induction and Perspective Distortion. Journal of VisionJ Vis. 5(8):1026-1026.
2005. Integration of motion fields through shape. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005. 2:663-669vol.2-663-669vol.2.
2005. Discovering a language for human activity. Proceedings of the AAAI 2005 Fall Symposium on Anticipatory Cognitive Embodied Systems, Washington, DC.
2005. Detecting Independent 3D Movement. Handbook of Geometric ComputingHandbook of Geometric Computing. :383-401.
2004
2004. A hierarchy of cameras for 3D photography. Computer Vision and Image Understanding. 96(3):274-293.
2004. Uncertainty in visual processes predicts geometrical optical illusions. Vision Research. 44(7):727-749.
2004. The Argus eye, a new tool for robotics. IEEE Robotics and Automation Magazine. 11(4):31-38.
2004. Bias in Shape Estimation. Computer Vision - ECCV 2004Computer Vision - ECCV 2004. 3023:405-416.
2004. Self-Calibration from Image Derivatives. International Journal of Computer Vision. 48(2):91-114.
2004. Compound eye sensor for 3D ego motion estimation. 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2004. (IROS 2004). Proceedings. 4:3712-3717vol.4-3712-3717vol.4.
2004. The Argus eye: a new imaging system designed to facilitate robotic tasks of motion. IEEE Robotics & Automation Magazine. 11(4):31-38.
2003
2003. Uncertainty in 3D shape estimation. ICCV Workshop on Statistical and Computational Theories of Vision.
2003. Polydioptric camera design and 3D motion estimation. 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. 2:II-294-301vol.2-II-294-301vol.2.
2003. New eyes for robotics. 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2003. (IROS 2003). Proceedings. 1:1018-1023vol.1-1018-1023vol.1.
2003. Eye design in the plenoptic space of light rays. Ninth IEEE International Conference on Computer Vision, 2003. Proceedings. :1160-1167vol.2-1160-1167vol.2.
2003. Statistical Bias Predicts Many Illusions. Journal of VisionJ Vis. 3(9):636-636.
2003. Plenoptic video geometry. The Visual Computer. 19(6):395-404.
2002
2002. Polydioptric Cameras: New Eyes for Structure from Motion. Pattern RecognitionPattern Recognition. 2449:618-625.
2002. Visual space-time geometry - A tool for perception and the imagination. Proceedings of the IEEE. 90(7):1113-1135.
2002. Eyes from eyes: new cameras for structure from motion. Third Workshop on Omnidirectional Vision, 2002. Proceedings. :19-26.
2002. Bias in visual motion processes: A theory predicting illusions. Statistical Methods in Video Processing.(in conjunction with European Conference on Computer Vision).
2002. Eyes form eyes: New cameras for structure from motion. Proceedings Workshop on Omnidirectional Vision (OMNIVIS).
2002. Polydioptric cameras: New eyes for structure from motion. Pattern Recognition. :618-625.
2001
2001. Animated heads: From 3d motion fields to action descriptions. Proceedings of the IFIP TC5/WG5. 10:1-11.
2001. Eyes from Eyes. 3D Structure from Images — SMILE 20003D Structure from Images — SMILE 2000. 2018:204-217.
2001. A spherical eye from multiple cameras (makes better models of the world). Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001. CVPR 2001. 1:I-576-I-583vol.1-I-576-I-583vol.1.
2001. The Statistics of Optical Flow. Computer Vision and Image Understanding. 82(1):1-32.
2001. Statistics Explains Geometrical Optical Illusions. Foundations of Image UnderstandingFoundations of Image Understanding. 628:409-445.
2000
2000. Analyzing Action Representations. Algebraic Frames for the Perception-Action CycleAlgebraic Frames for the Perception-Action Cycle. 1888:1-21.
2000. Multi-camera networks: eyes from eyes. IEEE Workshop on Omnidirectional Vision, 2000. Proceedings. :11-18.
2000. The statistics of optical flow: implications for the process of correspondence in vision. 15th International Conference on Pattern Recognition, 2000. Proceedings. 1:119-126vol.1-119-126vol.1.
2000. New eyes for building models from video. Computational Geometry. 15(1–3):3-23.
2000. Structure from motion: Beyond the epipolar constraint. International Journal of Computer Vision. 37(3):231-258.
2000. The Ouchi illusion as an artifact of biased flow estimation. Vision Research. 40(1):77-95.
2000. New Eyes for Shape and Motion Estimation. Biologically Motivated Computer VisionBiologically Motivated Computer Vision. 1811:23-47.
2000. A New Framework for Multi-camera Structure from Motion. Mustererkennung 2000, 22. DAGM-Symposium. :75-82.
2000. Observability of 3D Motion. International Journal of Computer Vision. 37(1):43-63.
1999
1999. Shape from Video. Computer Vision and Pattern Recognition, IEEE Computer Society Conference on. 2:2146-2146.
1999. Statistical biases in optic flow. Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on.. 1:566Vol.1-566Vol.1.
1999. Active Perception. Wiley Encyclopedia of Electrical and Electronics EngineeringWiley Encyclopedia of Electrical and Electronics Engineering.