Lazy Snapping

"Lazy Snapping," Li, et. al., ACM SIGGRAPH, 2004

The paper presents a coarse-to-fine UI design for image cutout which is the technique of removing an object in an image from the background. There are two steps : (1) object marking (2) boundary editing.
In the first step, user can specify the foreground and background region respectively. Then, the uncertain region can be labeled by calculating the likelihood energy function. In order to get likelihood energy of each region, first cluster the colors in seeds F and B by the K-means method. And compute the minimum distance for each node. Moreover, add a penalty term to specify the gradient effect. Finally, minimize the energy and get the result. To improve the efficiency, apply watershed algorithm on the graph cut formulation to dealt with the segmented regions, and possibly get the reasonable result in a significantly improved speed.
The fine boundary can be adjusted in the second step. But the prior energy is fixed by adding the polygon locations as soft constraints.
In conclusion, they succeed in develop an interactive system to cut out the foreground object from the background in an image. However, they want to do better at the thin and branch structures.

Learning Low-Level Vision

"Learning Low-Level Vision," Freeman, IJCV, 2000.

The paper describes a learning-based method for low-level vision problems, such as motion analysis, inferring shape and reflectance from a photograph, or extrapolating image detail, that is, how to estimate scenes from images is the goal. Given training sets, they succeed in enumerating a coarse sampling of all input patch values by preprocessing or restricting to some classes. Breaking the scenes into a Markov network, the algorithm can find the optimal scene explanation if given any image data. It shows that applying machine learning methods has the benefits to problems of visual interpretations.

An introduction to graphical models

"An introduction to graphical models," Kevin Murphy, 2001.

Graphical models are the combination of probability theory and graph theory. They can be directed or undirected models. The directed graphical models are known as Bayesian networks, where the ancestor/parent relationship is with respect to some fixed topological ordering of the nodes, and the undirected graphical models are known as Markov networks. There are some hidden causes in the graph, and their values must be estimated from observation, that is inference. There are some popular approximate inference methods: sampling(Monte Carlo) methods, variational methods, and loopy brief propagation. According to the structure and the observation, Learning methods are simply classified into four category:

  observability
structure\ full  partial
known    closed form EM
unknown  local search structural EM

Finally, computing the optimal actions to perform to get the maximum expected utility and making decisions under uncertainty. The decision algorithm is similar to inference algorithm.