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Large scale metric learning from equivalence constraints
TLDR
This paper introduces a simple though effective strategy to learn a distance metric from equivalence constraints, based on a statistical inference perspective, which is orders of magnitudes faster than comparable methods.
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Person Re-identification by Descriptive and Discriminative Classification
TLDR
The proposed approach is demonstrated on two datasets, where it is shown that the combination of a generic descriptive statistical model and a discriminatively learned feature-based model attains considerably better results than the individual models alone.
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Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization
TLDR
AFLW provides a large-scale collection of images gathered from Flickr, exhibiting a large variety in face appearance as well as general imaging and environmental conditions, and is well suited to train and test algorithms for multi-view face detection, facial landmark localization and face pose estimation.
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The Visual Object Tracking VOT2016 Challenge Results
TLDR
The Visual Object Tracking challenge VOT2016 goes beyond its predecessors by introducing a new semi-automatic ground truth bounding box annotation methodology and extending the evaluation system with the no-reset experiment.
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Real-Time Tracking via On-line Boosting
TLDR
A novel on-line AdaBoost feature selection algorithm for tracking that allows to adapt the classifier while tracking the object and selects the most features for tracking resulting in stable tracking results.
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A Duality Based Approach for Realtime TV-L1 Optical Flow
TLDR
This work presents a novel approach to solve the TV-L1 formulation, which is based on a dual formulation of the TV energy and employs an efficient point-wise thresholding step.
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Image Guided Depth Upsampling Using Anisotropic Total Generalized Variation
TLDR
This work formulate a convex optimization problem using higher order regularization for depth image up sampling, and derives a numerical algorithm based on a primal-dual formulation that is efficiently parallelized and runs at multiple frames per second.
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On-line Boosting and Vision
TLDR
This paper proposes a novel on-line AdaBoost feature selection method and demonstrates the multifariousness of the method on such diverse tasks as learning complex background models, visual tracking and object detection.
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Semi-supervised On-Line Boosting for Robust Tracking
TLDR
The main idea is to formulate the update process in a semi-supervised fashion as combined decision of a given prior and an on-line classifier, without any parameter tuning, which significantly alleviates the drifting problem in tracking applications.
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Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets
TLDR
A comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.
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