Remote tracker and 6DOF using a webcam

The following video clips demonstrates a Masters thesis project from the AGH University of Science and Technology in Cracow, Poland. The method developed provides 6 degrees of freedom head tracking and 2D eye tracking using a simple, low resolution 640x480 webcam. Under the hood it's based on the Lucas-Kanade optical flow and POSIT. A great start as the head tracking seems relatively stable. Imagine it with IR illumination, a camera with slightly higher resolution and a narrow angle lens. And of course, pupil + glint tracking algorithms for calibrated gaze estimation.

ITU GazeTracker in the wild

Came across these two Youtube videos from students out there using the ITU GazeTracker in their HCI projects. By now the software has been downloaded 3000 times and the forum has seen close to three hundred posts. It's been a good start, better yet, a new version is in the makings. It offers a complete network API for third party applications, improved tracking performance, better camera control and a number of bugfixes (thanks for your feedback). It will be released when it's ready.







Thanks for posting the videos!

Nokia near-eye display gaze interaction update

The Nokia near-eye gaze interaction platform that I tried in Finland last year has been further improved. The cap used to support the weight has been replaced with a sturdy frame and the overall prototype seems lighter and also incorporates headphones. The new gaze based navigation interface support photo browsing based on the Image Space application, allowing location based accesses to user generated content. See the concept video at the bottom for their futuristic concept. Nokia research website. The prototype will be displayed at the International Symposium on Mixed and Augmented Reality conference in Orlando, October 19-22.

Medical Image Perception Society 2009 - Day three

Session 10. Displays and Tools. Chair: Kevin Berbaum, PhD

• Medical Virtual Imaging Chain (MEVIC). C Marchessoux, T Kimpe, E. Vansteenkiste, S Staelens, P Schelkens, H Bosmans, A Smith, & M. Guerchaft

• Objective methodology to compare clinical value of computed tomography artifact reduction algorithms. G Spalla, C Marchessoux, M Vaz, A Ricker, & T Kimpe

• LCD Spatial Noise Suppression: Large-field vs. ROI Image Processing. WJ Dallas, H Roehrig, J Fan, EA Krupinski, & J Johnson

• Three-dimensional Simulation of Lung Nodules for Pediatric CT. X Li, E Samei, DM DeLong, RP Jones, AM Gaca, CL Hollingsworth, CM Maxfield, CWT Carrico, & DP Frush

Session 11. Displays and Tools. Chair: Miguel Eckstein, PhD

• Abnormality Manipulation Tools for PET CT. MT Madsen, KS Berbaum, KM Schartz, & RT Caldwell.

• Stereoscopic Digital mammography: Improved Accuracy of Lesion Detection in Breast Cancer Screening. DJ Getty, CJ D’Orsi, & RM Pickett

• Conversion of WorkstationJ to ImageJ Plugin. KM Schartz, KS Berbaum, RT Caldwell, and MT Madsen

• Detectability in tomosynthesis projections, slices and volumes: Comparison of human observer performance in a SKE detection task. I Reiser, K Little, & RM Nishikawa

Thanks Craig, Miguel and Elisabeth for a wonderful event, learned so much in just three days. Plenty of inspiration for future research.

Medical Image Perception Society 2009 - Day two

Session 6. Performance Measurement II. Chair: Matthew Freedman, MD, MBA

• Coding of FDG Intensity as a 3-D Rendered Height Mapping to Improve Fusion Display of Co-Registered PET-CT Images. RM Shah, C Wood, YP Hu, & LS Zuckier

• Revisiting the General Three-class ROC Theory. X He & EC Frey

• Estimation of AUC from Normally Distributed Rating Data with Known Variance Ratio. A Wunderlich & F Noo

• Using the Mean-to-Variance Ratio as a Diagnostic for Unacceptably Improper Binormal ROC Curves. SL Hillis & KS Berbaum

Session 7. Performance Measurement II. Chair: Stephen Hillis, PhD

• BI-RADS Data Should Not be Used to Estimate ROC Curves. Y Jiang & CE Metz
  
  
• Estimating the utility of screening mammography in large clinical studies. CK Abbey, JM Boone, & MP Eckstein
  
  
• Issues Related to the Definition of Image Contrast, DL Leong & PC Brennan

Session 8. Models of Perceptual processing. Chair: Yulei Jiang, PhD

• Channelized Hotelling Observers for Detection Tasks in Multi-Slice Images. L Platiša, B Goossens, E Vansteenkiste, A Badano & W Philips
  
  
• Channelized Hotelling observers adapted to irregular signals in breast tomosynthesis detection tasks. I Diaz, P Timberg, CK Abbey, MP Eckstein, FR Verdun, C Castella, FO Bochud
  
  
• Detecting Compression Artifacts in Virtual Pathology Images Using a Visual Discrimination Model. J Johnson & EA Krupinski
  
  
• Automatic MRI Acquisition Parameters Optimization Using HVS-Based Maps. J Jacobsen, P Irarrázabal, & C Tejos
  
  
• Parametric Assessment of Lesion Detection Using a Pre-whitened Matched Filter on Projected Breast CT Images. N Packard, CK Abbey, & JM Boone
  
  
• Model Observers for Complex Discrimination Tasks: Deployment Assessment of Multiple Coronary Stents. S Zhang, CK Abbey, X Da, JS Whiting, & MP Eckstein

Session 9. Special Invited Session on Neuroscience and Medical Image Perception. Chair: Miguel Eckstein, PhD

• Natural image statistics and visual coding. BA Olshausen

• Decoding Information Processing When Attention Fails: An Electrophysiological Approach. B Giesbrecht

• Some Neural Bases of Radiological Expertise. SA Engel

Medical Image Perception Society 2009 - Day one

The first day of the Medical Image Perception Society conference, held biannual, this year in Santa Barbara was filled with interesting talks. Plenty of research utilizing eye tracking as a means of obtaining data. The conference is hosted by Craig Abbey and Miguel Eckstein at the Department of Psychology at the University of California, Santa Barbara in cooperation with Elizabeth Krupinski (book1 , book2) from University of Arizona whom has performed extensive research on eye movements (among other things) in relation to medical imaging and radiology.

Session 1. Visual Search. Chair: Claudia Mello-Thoms, PhD

• Seeing trouble in a flash: Discrimination of normal from abnormal mammograms with brief exposure. KK Evans (current), D Georgian-Smith, R Birdwell, & JM Wolfe

• Mapping Eye Movements in Three-Dimensions: Analyzing Gaze Paths when Interpreting Volumetric Chest CT Data. M Tall, DL Ly, F Schmitzberger, D Rasooly, TJ Kim, JE Roos, DS Paik, S Napel, & GD Rubin

• Measuring Axial CTC Interaction. P Phillips

• Using eye-tracking to study diagnostic process during MRI scanning. C Cavaro-Ménard, J-Y Tanguy, & P Le Callet

Session 2. Visual Search. Chair: Elizabeth Krupinski, PhD

• Visual Search Characteristics of Pathology Residents Reading Dermatopathology Slides. J Law & C Mello-Thoms

• Are you a good eye-witness? Perceptual differences between physicians and lay people. C Mello-Thoms

• Eye movements and computer-based mammographic interpretation training. Y Chen & A Gale

Session 3. Perceptual Effects. Chair: David Manning, PhD

• Nuisance levels of noise effects Radiologists Performance. MF Mc Entee, A O'Beirne, J Ryan, R Toomey, M Evanoff, D Chakraborty, D Manning, & PC. Brennan

• Observer Performance in Stroke Interpretation: The Influence of Experience and Clinical Information in Multidimensional Magnetic Resonance Imaging. L Cooper, A Gale, J Saada, S Gedela, H Scott, & A Toms

• Interpretation of wrist radiographs: A comparison between final year medical and radiography students. L Hutchinson, P Brennan & L Rainford

• Tumor measurement for revised TNM staging of lung cancer. FL Jacobson, A Sitek, D Getty, & SE Seltzer

• Does Reader Visual Fatigue Impact Performance? EA Krupinski & KS Berbaum

• Ambient Temperature is an Important Consideration in the Radiology Reading Room. MF Mc Entee & S Gafoor

Session 4. Performance Measurement I. Chair: Dev Chakraborty, PhD

• Perceptual indicators of the holistic view in pulmonary nodule detection. MW Pietrzyk, DJ Manning, T Donovan, & Alan Dix

• An e-learning tutorial demonstrates significant improvements in ROC performance amongst naive observers in breast image interpretation. PBL Soh, PC Brennan, A Poulos, W Reed

• Is n ROC-type response Truly always better than A Binary Response? D Gur, AI Bandos, HE Rockette, ML Zuley, CM Hakim, DM Chough, MA Ganott

• Recognition of Images in Reader Studies: How Well Can We Predict Which Will Be Remembered? T Miner Haygood, P O’Sullivan, J Ryan, E Galvan, J-M Yamal, M Evanoff, M McEntee, J Madewell, C Sandler, E Lano, & P Brennan

Session 5. Performance Measurement I. Chair: Alastair Gale, PhD

• New classes of models with monotonic likelihood ratios. F Samuelson

• Sample size estimation procedure for free-response (FROC) studies. DP Chakraborty & M Bath

• Comparison of Four Methods (ROC, JAFROC, IDCA, and ROI) for Analysis of Free Response Clinical Data. F Zanca, DP Chakraborty, J Jacobs, G. Marchal, and H Bosmans

Feel free to post additional links in the comments. Slides will be posted as they become available.

DoCoMo EOG update

While eye movement detection using EOG is nothing new the latest demonstration by Japanese NTT DoCoMo illustrates recent developments in the field. The innovation here is the form factor which is quite impressive. Typically EOG is detected using electrodes placed around the eyes as in Andreas Bullings prototype demonstrated at CHI 09 in Boston. Now it can be done using tiny sensors inside the ear. Just compare it to the prototype demonstrated last year!







Thanks Roman for the links!

Wearable Augmented Reality System using Gaze Interaction (Park, Lee & Choi)

Came across this paper on a wearable system that employs a small eye tracker and a head mounted display for augmented reality. I've previously posted a video on the same system. It's a future technology with great potential, only imagination sets the limit here. There is a lot of progress in image/object recognition and location awareness taking place right now (with all the associated non-trivial problems to solve!)


Abstract
"Undisturbed interaction is essential to provide immersive AR environments. There have been a lot of approaches to interact with VEs (virtual environments) so far, especially in hand metaphor. When the user‟s hands are being used for hand-based work such as maintenance and repair, necessity of alternative interaction technique has arisen. In recent research, hands-free gaze information is adopted to AR to perform original actions in concurrence with interaction. [3, 4]. There has been little progress on that research, still at a pilot study in a laboratory setting. In this paper, we introduce such a simple WARS(wearable augmented reality system) equipped with an HMD, scene camera, eye tracker. We propose „Aging‟ technique improving traditional dwell-time selection, demonstrate AR gallery – dynamic exhibition space with wearable system."

• Park, H. M., Seok Han Lee, and Jong Soo Choi 2008. Wearable augmented reality system using gaze interaction. In Proceedings of the 2008 7th IEEE/ACM international Symposium on Mixed and Augmented Reality - Volume 00 (September 15 - 18, 2008). Symposium on Mixed and Augmented Reality. IEEE Computer Society, Washington, DC, 175-176. DOI= http://dx.doi.org/10.1109/ISMAR.2008.4637353

The EyeWriter project

For some time I've been following the EyeWriter project which aims at enabling Tony, who has ALS, to draw graffiti using eye gaze alone. The open source eye tracker is available at Google code and is based on C++, OpenFrameworks and OpenCV. The current version supports basic pupil tracking based on image thresholding and blob detection but they are aiming for remote tracking using IR glints. Keep up the great work guys!

The Eyewriter from Evan Roth on Vimeo.

eyewriter tracking software walkthrough from thesystemis on Vimeo.

More information is found at http://fffff.at/eyewriter/

GaZIR: Gaze-based Zooming Interface for Image Retrieval (Kozma L., Klami A., Kaski S., 2009)

From the Helsinki Institute for Information Technology, Finland, comes a research prototype called GaZIR for gaze based image retrieval built by Laszlo Kozma, Arto Klami and Samuel Kaski. The GaZIR prototype uses a light-weight logistic regression model as a mechanism for predicting relevance based on eye movement data (such as viewing time, revisit counts, fixation length etc.) All occurring on-line in real time. The system is build around the PicSOM (paper) retrieval engine which is based on tree structured self-organizing maps (TS-SOMs). When provided a set of reference images the PicSOM engine goes online to download a set of similar images (based on color, texture or shape)

Abstract
"We introduce GaZIR, a gaze-based interface for browsing and searching for images. The system computes on-line predictions of relevance of images based on implicit feedback, and when the user zooms in, the images predicted to be the most relevant are brought out. The key novelty is that the relevance feedback is inferred from implicit cues obtained in real-time from the gaze pattern, using an estimator learned during a separate training phase. The natural zooming interface can be connected to any content-based information retrieval engine operating on user feedback. We show with experiments on one engine that there is sufficient amount of information in the gaze patterns to make the estimated relevance feedback a viable choice to complement or even replace explicit feedback by pointing-and-clicking."

Fig1. "Screenshot of the GaZIR interface. Relevance feedback gathered from outer rings influences the images retrieved for the inner rings, and the user can zoom in to reveal more rings."

Fig2. "Precision-recall and ROC curves for userindependent relevance prediction model. The predictions (solid line) are clearly above the baseline of random ranking (dash-dotted line), showing that relevance of images can be predicted from eye movements. The retrieval accuracy is also above the baseline provided by a naive model making a binary relevance judgement based on whether the image was viewed or not (dashed line), demonstrating the gain from more advanced gaze modeling."

Fig 3. "Retrieval performance in real user experiments. The bars indicate the proportion of relevant images shown during the search in six different search tasks for three different feedback methods. Explicit denotes the standard point-and-click feedback, predicted means implicit feedback inferred from gaze, and random is the baseline of providing random feedback. In all cases both actual feedback types outperform the baseline, but the relative performance of explicit and implicit feedback depends on the search task."

• László Kozma, Arto Klami, and Samuel Kaski: GaZIR: Gaze-based Zooming Interface for Image Retrieval. To appear in Proceedings of 11th Conference on Multimodal Interfaces and The Sixth Workshop on Machine Learning for Multimodal Interaction (ICMI-MLMI), Boston, MA, USA, Novermber 2-6, 2009. (abstract, pdf)