Automatic User Interaction Correction via Multi-label Graph-cuts

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Presentation transcript:

Automatic User Interaction Correction via Multi-label Graph-cuts Workshop on Human Interaction in Computer Vision 12nd November 2011 Antonio Hernández-Vela, Carlos Primo and Sergio Escalera

Semi-automatic segmentation

Semi-automatic segmentation

Outline Introduction Methodology Experiments & results Conclusion

Introduction X How much interaction is required from the user? What if the user makes a mistake? Automatic correction Multi-label approach X

Outline Introduction Methodology Experiments & results Conclusion

Multi-label graph-cuts1 Energy minimization framework Image restoration Stereo reconstruction Image segmentation 1 Y. Boykov, O. Veksler, and R. Zabih. Fast approximate energy minimization via graph cuts. PAMI, nov 2001.

Segmentation with Multi-label graph-cuts User initialization Unary potential Pairwise potential Segmentation (α-expansion)

Likelihood-based correction Color model: Gaussian Mixture Model :Seeds for label : weight of GMM component

Likelihood-based correction: example

Likelihood-based correction: example

Outline Introduction Methodology Experiments & results Conclusion

Experiments: Data Human Limb DB2: 227 images from 25 people, GT provided Random selection of 10 images –from 10 different people– 2http://www.maia.ub.es/~sergio/linked/humanlimbdb.zip

Experiments: Setup Random interaction simulation Select 0.2% of total pixels from each label Repeat the process 10 times 4 scenarios: With/Without error Single/Combined mode

Results without errors

Results with 25% of error

Results with 45% of error

Qualitatve Results (single mode)

Qualitatve Results (combined mode)

Outline Introduction Methodology Experiments & results Conclusion

Conclusion Methodology for automatic user interaction correction in multi-label graph-cuts image segmentation. Study on the influence of the amount of interaction from the user. With and without errors. Future work Further experiment using real user interactions. Adapt the algorithm to the user. Finally, as conclusions, we have presented a methodology for gesture segmentation and recognition, applied to the detection of ADHD indicators. And first results indicate that the objectives are feasible, given the presented methodology. As future work, we plan firstly to make the callibration of the device fully-automatic, and secondly, to give different importance to body parts in order to improve gesture recognition.

Automatic User Interaction Correction via Multi-label Graph-cuts Thank You! Automatic User Interaction Correction via Multi-label Graph-cuts Workshop on Human Interaction in Computer Vision 12nd November 2011 Antonio Hernández-Vela, Carlos Primo and Sergio Escalera Questions?