Camera spectral sensitivity plays an important role for various color-based computer vision tasks. Although several methods have been proposed to estimate it, their applicability ...
Multiple Instance Learning (MIL) has been widely exploited in many computer vision tasks, such as image retrieval, object tracking and so on. To handle ambiguity of instance label...
The supervised learning paradigm assumes in general that both training and test data are sampled from the same distribution. When this assumption is violated, we are in the setting...
Visual tracking plays an important role in many computer vision tasks. A common assumption in previous methods is that the video frames are blur free. In reality, motion blurs are...
Computer vision tasks such as learning, recognition, classification or segmentation applied to spatial data often requires spatial normalization of repeated features and structure...
Computer vision tasks often require the robust fit of a model to some data. In a robust fit, two major steps should be taken: i) robustly estimate the parameters of a model, and ii...
Matching feature points between images is a key point in many Computer Vision tasks. As the number of images increases, this rapidly becomes a bottleneck. We here present how to u...