I work in Computer Vision and Natural User Interfaces.
|LightRing is a wearable sensor in a ring form factor that senses the 2d location of a fingertip on any surface. The device uses an infrared proximity sensor for measuring finger flexion and a 1-axis gyroscope for measuring finger rotation. LightRing tracks subtle fingertip movements from the finger base without requiring instrumentation of other body parts or the environment. This keeps the normal hand function intact and allows for socially acceptable appearance. [paper] [video] [Ken Hinckley's blog]|
As touch screens are getting smaller, soft keyboards are getting harder to use. With the Analog Keyboard Project we are exploring handwriting recognition for text input on very small touch screens. We have built a prototype for Android Wear that is available for download for research purposes. [project page] [video] [download] [Ken Hinckley's blog]
Image Watch is a Visual Studio extension for debugging C++ image processing code. The plug-in introduces a new watch window that displays in-memory bitmaps while your app is running, which means you no longer have to litter your code with "save-this-image-to-a-file" statements when tracking down bugs. This can be a huge time-saver, especially when debugging remotely on a tablet, phone, or wearable device. [project page on MSDN] [channel 9 interview] [//build 2013 talk]
|We have developed a method for composing handwritten messages and notes on a small touchscreen device. A word is entered by drawing overlapped, screen sized letters on top of each other. The system does not require gestures or timeouts to delimit characters within a word—it automatically segments the overlapping strokes and renders the message in real-time as the user is writing. Drawings may also be included with the text. [paper] [video] [Ken Hinckley's blog]|
I joined Microsoft in 2008 to work on handwriting recognition algorithms in Windows 7. From 2009 to 2010 I worked on large-scale machine learning algorithms in Bing. Before joining Microsoft I worked as Research Scientist in at the Max-Planck Institute for Intelligent Systems. Here's my old homepage.
I received my PhD from the University of Tübingen, Germany. My thesis is about modeling human eye movements with statistical learning methods.
- Wolf Kienzle and Ken Hinckley, LightRing: Always-Available 2D Input on Any Surface, ACM UIST, October 2014.
- Wolf Kienzle and Ken Hinckley, Writing Handwritten Messages on a Small Touchscreen, ACM MobileHCI, July 2013.
- P. Dollár, R. Appel, and W. Kienzle, Crosstalk Cascades for Frame-Rate Pedestrian Detection, in ECCV, European Conference on Computer Vision, September 2012.
- Johannes Kopf, Wolf Kienzle, Steven Drucker, and Sing Bing Kang, Quality Prediction for Image Completion, in ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2012), vol. 31, no. 6, 2012.
- W Kienzle, MO Franz, B Schölkopf, and FA Wichmann, Center-surround patterns emerge as optimal predictors for human saccade targets, in Journal of Vision, vol. 9, no. 5:7, pp. 1-15, May 2009.
- S Klöppel, C Chu, GC Tan, B Draganski, H Johnson, JS Paulsen, W Kienzle, SJ Tabrizi, J Ashburner, and RSJ Frackowiak, Automatic detection of preclinical neurodegeneration: Presymptomatic Huntington disease, in Neurology, vol. 72, no. 5, pp. 426-431, February 2009.
- FA Wichmann, W Kienzle, B Schölkopf, and M Franz, Non-linear System Identification: Visual Saliency Inferred from Eye-Movement Data, in Journal of Vision, vol. 9, no. 8, pp. article 32, 2009.
- P Breuer, KI Kim, W Kienzle, B Schölkopf, and V Blanz, Automatic 3D Face Reconstruction from Single Images or Video, in FG 2008, IEEE Computer Society, Los Alamitos, CA, USA, September 2008.
- W Kienzle, FA Wichmann, B Schölkopf, and MO Franz, A Nonparametric Approach to Bottom-Up Visual Saliency, in Advances in Neural Information Processing Systems 19, MIT Press, Cambridge, MA, USA, September 2007.
- W Kienzle, B Schölkopf, F Wichmann, and MO Franz, How to Find Interesting Locations in Video: A Spatiotemporal Interest Point Detector Learned from Human Eye movements, in Pattern Recognition, Springer, Berlin, Germany, September 2007.
- W Kienzle and K Chellapilla, Personalized handwriting recognition via biased regularization, in ICML 2006, ACM Press, New York, NY, USA, June 2006.
- W Kienzle, FA Wichmann, B Schölkopf, and MO Franz, Learning an Interest Operator from Human Eye Movements, in CVPWR 2006, IEEE Computer Society, Los Alamitos, CA, USA, April 2006.
- W Kienzle and B Schölkopf, Training Support Vector Machines with Multiple Equality Constraints, in Proceedings of the 16th European Conference on Machine Learning, Lecture Notes in Computer Science, Vol. 3720, Springer, Berlin, Germany, November 2005.
- W Kienzle, G BakIr, M Franz, and B Schölkopf, Face Detection: Efficient and Rank Deficient, in Advances in Neural Information Processing Systems 17, MIT Press, Cambridge, MA, USA, July 2005.
- W Kienzle, G BakIr, M Franz, and B Schölkopf, Efficient Approximations for Support Vector Machines in Object Detection, in DAGM 2004, Springer, Berlin, Germany, 2004.