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» Meta-Evaluation of Image Segmentation Using Machine Learning
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ACSW
2004
13 years 9 months ago
Detecting Stress in Spoken English using Decision Trees and Support Vector Machines
This paper describes an approach to the detection of stress in spoken New Zealand English. After identifying the vowel segments of the speech signal, the approach extracts two dif...
Huayang Xie, Peter Andreae, Mengjie Zhang, Paul Wa...
ECCV
2008
Springer
14 years 9 months ago
Learning Visual Shape Lexicon for Document Image Content Recognition
Developing effective content recognition methods for diverse imagery continues to challenge computer vision researchers. We present a new approach for document image content catego...
Guangyu Zhu, Xiaodong Yu, Yi Li, David S. Doermann
ICCV
2009
IEEE
15 years 14 days ago
Using machine learning to predict where people look
For many applications in graphics, design, and human computer interaction, it is essential to understand where humans look in a scene. Where eye tracking devices are not a viable o...
Tilke Judd, Krista Ehinger, Fr´edo Durand, Antoni...
EMMCVPR
1999
Springer
13 years 11 months ago
Markov Random Field Modelling of fMRI Data Using a Mean Field EM-algorithm
This paper considers the use of the EM-algorithm, combined with mean field theory, for parameter estimation in Markov random field models from unlabelled data. Special attention ...
Markus Svensén, Frithjof Kruggel, D. Yves v...
PAMI
2012
11 years 10 months ago
CPMC: Automatic Object Segmentation Using Constrained Parametric Min-Cuts
—We present a novel framework to generate and rank plausible hypotheses for the spatial extent of objects in images using bottom-up computational processes and mid-level selectio...
João Carreira, Cristian Sminchisescu