Titlebook: Computer Vision -- ACCV 2012; 11th Asian Conferenc Kyoung Mu Lee,Yasuyuki Matsushita,Zhanyi Hu Conference proceedings 2013 Springer-Verlag

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Adaptive Integration of Feature Matches into Variational Optical Flow Methods still poses a severe problem for many algorithms. In particular if the motion exceeds the size of an object, standard coarse-to-fine estimation schemes fail to produce meaningful results. While the integration of point correspondences may help to overcome this limitation, such strategies often dete

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Efficient Learning of Linear Predictors Using Dimensionality Reductionmits their use in applications where the scene is not known a priori and multiple templates have to be added online, such as SLAM or SfM. This especially holds for applications running on low-end hardware such as mobile devices. Previous approaches either had to learn Linear Predictors offline [1],

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Robust Visual Tracking Using Dynamic Classifier Selection with Sparse Representation of Label Noiselassifier online using the samples generated by the tracker to handle the appearance changes. However, the self-updating scheme makes these methods suffer from drifting problem because of the incorrect labels of weak classifiers in training samples. In this paper, we split the class labels into true

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An Anchor Patch Based Optimization Framework for Reducing Optical Flow Drift in Long Image Sequences pairs over time where error accumulation in tracking can result in .. In this paper, we propose an optimization framework that utilises a novel Anchor Patch algorithm which significantly reduces overall tracking errors given long sequences containing highly deformable objects. The framework may be

四溢

One-Class Multiple Instance Learning and Applications to Target Trackingbags are available. In this work, we propose the first analysis of the one-class version of MIL problem where one is only provided input data in the form of positive bags. We also propose an SVM-based formulation to solve this problem setting. To make the approach computationally tractable we furthe