Abstract. Many problems of low-level computer vision and image processing, such as denoising, deconvolution, tomographic reconstruction or superresolution, can be addressed by maxi...
Graph-based methods form a main category of semisupervised
learning, offering flexibility and easy implementation
in many applications. However, the performance of
these methods...
Wei Liu (Columbia University), Shih-fu Chang (Colu...
Distance functions are an important component in many learning applications. However, the correct function is context dependent, therefore it is advantageous to learn a distance f...
Quadratic optimization lies at the very heart of many structural pattern recognition and computer vision problems, such as graph matching, object recognition, image segmentation, ...
Computational vision algorithms are often developed in a Bayesian framework. Two estimators are commonly used: maximum a posteriori (MAP), and minimum mean squared error (MMSE). W...