5,247 research outputs found
A Comprehensive Performance Evaluation of Deformable Face Tracking "In-the-Wild"
Recently, technologies such as face detection, facial landmark localisation
and face recognition and verification have matured enough to provide effective
and efficient solutions for imagery captured under arbitrary conditions
(referred to as "in-the-wild"). This is partially attributed to the fact that
comprehensive "in-the-wild" benchmarks have been developed for face detection,
landmark localisation and recognition/verification. A very important technology
that has not been thoroughly evaluated yet is deformable face tracking
"in-the-wild". Until now, the performance has mainly been assessed
qualitatively by visually assessing the result of a deformable face tracking
technology on short videos. In this paper, we perform the first, to the best of
our knowledge, thorough evaluation of state-of-the-art deformable face tracking
pipelines using the recently introduced 300VW benchmark. We evaluate many
different architectures focusing mainly on the task of on-line deformable face
tracking. In particular, we compare the following general strategies: (a)
generic face detection plus generic facial landmark localisation, (b) generic
model free tracking plus generic facial landmark localisation, as well as (c)
hybrid approaches using state-of-the-art face detection, model free tracking
and facial landmark localisation technologies. Our evaluation reveals future
avenues for further research on the topic.Comment: E. Antonakos and P. Snape contributed equally and have joint second
authorshi
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True-false lumen segmentation of aortic dissection using multi-scale wavelet analysis and generative-discriminative model matching
Computer aided diagnosis in the medical image domain requires sophisticated probabilistic models to formulate quantitative behavior in image space. In the diagnostic process detailed knowledge of model performance with respect to accuracy, variability, and uncertainty is crucial. This challenge has lead to the fusion of two successful learning schools namely generative and discriminative learning. In this paper, we propose a generative-discriminative learning approach to predict object boundaries in medical image datasets. In our approach, we perform probabilistic model matching of both modeling domains to fuse into the prediction step appearance and structural information of the object of interest while exploiting the strength of both learning paradigms. In particular, we apply our method to the task of true-false lumen segmentation of aortic dissections an acute disease that requires automated quantification for assisted medical diagnosis. We report empirical results for true-false lumen discrimination of aortic dissection segmentation showing superior behavior of the hybrid generative-discriminative approach over their non hybrid generative counterpart
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