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An Iterative Image Registration Technique with an Application to Stereo Vision
This work presents a new image registration technique that makes use of the spatial intensity gradient of the images to find a good match using a type of Newton-Raphson iteration, and can be generalized to handle rotation, scaling and shearing.
The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression
- P. Lucey, J. Cohn, T. Kanade, Jason M. Saragih, Z. Ambadar, I. Matthews
- Computer ScienceIEEE Computer Society Conference on Computer…
- 13 June 2010
The Cohn-Kanade (CK+) database is presented, with baseline results using Active Appearance Models (AAMs) and a linear support vector machine (SVM) classifier using a leave-one-out subject cross-validation for both AU and emotion detection for the posed data.
Shape and motion from image streams under orthography: a factorization method
- Carlo Tomasi, T. Kanade
- Mathematics, Computer ScienceInternational Journal of Computer Vision
- 1 November 1992
A factorization method is developed that can overcome the difficulty by recovering shape and motion under orthography without computing depth as an intermediate step, and gives accurate results.
Convolutional Pose Machines
- Shih-En Wei, V. Ramakrishna, T. Kanade, Yaser Sheikh
- Computer ScienceIEEE Conference on Computer Vision and Pattern…
- 30 January 2016
This work designs a sequential architecture composed of convolutional networks that directly operate on belief maps from previous stages, producing increasingly refined estimates for part locations, without the need for explicit graphical model-style inference in structured prediction tasks such as articulated pose estimation.
- R. Gross, I. Matthews, J. Cohn, T. Kanade, S. Baker
- Medicine, Computer Science8th IEEE International Conference on Automatic…
- 1 September 2008
This paper introduces the database, describes the recording procedure, and presents results from baseline experiments using PCA and LDA classifiers to highlight similarities and differences between PIE and Multi-PIE.
Neural Network-Based Face Detection
A neural network-based upright frontal face detection system that arbitrates between multiple networks to improve performance over a single network, and a straightforward procedure for aligning positive face examples for training.
Comprehensive database for facial expression analysis
- T. Kanade, Ying-li Tian, J. Cohn
- Computer ScienceProceedings Fourth IEEE International Conference…
- 26 March 2000
The problem space for facial expression analysis is described, which includes level of description, transitions among expressions, eliciting conditions, reliability and validity of training and test data, individual differences in subjects, head orientation and scene complexity image characteristics, and relation to non-verbal behavior.
A statistical method for 3D object detection applied to faces and cars
- H. Schneiderman, T. Kanade
- Computer ScienceProceedings IEEE Conference on Computer Vision…
- 13 June 2000
Using this method, this work has developed the first algorithm that can reliably detect human faces with out-of-plane rotation and the first algorithms thatCan reliably detect passenger cars over a wide range of viewpoints.
Limits on super-resolution and how to break them
- S. Baker, T. Kanade
- Mathematics, Computer ScienceProceedings IEEE Conference on Computer Vision…
- 15 June 2000
An algorithm is proposed that learns recognition-based priors for specific classes of scenes, the use of which gives far better super-resolution results for both faces and text.
Recognizing Action Units for Facial Expression Analysis
- Ying-li Tian, T. Kanade, J. Cohn
- Computer Science, MedicineIEEE Trans. Pattern Anal. Mach. Intell.
- 1 February 2001
An Automatic Face Analysis (AFA) system to analyze facial expressions based on both permanent facial features and transient facial features in a nearly frontal-view face image sequence and Multistate face and facial component models are proposed for tracking and modeling the various facial features.