OBJCUT: Efficient Segmentation Using Top-Down and Bottom-Up Cues

Unsupervised Tattoo Segmentation Combining Bottom-Up and Top-Down Cues

Tattoo segmentation is challenging due to the complexity and large variance in tattoo structures. We have developed a segmentation algorithm for finding tattoos in an image. Our basic idea is split-merge: split each tattoo image into clusters through a bottom-up process, learn to merge the clusters containing skin and then distinguish tattoo from the other skin via top-down prior in the image i...

Bottom-up, top-down

Bottom-Up Beats Top-Down

In PEPA, the calculation of the transitions enabled by a process accounts for a large part of the time for the state space exploration of the underlying Markov chain. Unlike other approaches based on recursion, we present a new technique that is iterative—it traverses the process’ binary tree from the sequential components at the leaves up to the root. Empirical results shows that this algorith...

Scalable Tracing of Electron Micrographs by Fusing Top Down and Bottom Up Cues Using Hypergraph Diffusion

A novel framework for robust 3D tracing in electron micrographs is presented. The proposed framework is built using ideas from hypergraph diffusion, and achieves two main objectives. Firstly, the approach scales to trace hundreds of targets without noticeable increase in runtime complexity. Secondly, the framework yields flexibility to fuse top down (global cues as hyperedges) and bottom up (lo...

Efficient visual search without top-down or bottom-up guidance.

Two types of mechanisms have dominated theoretical accounts of efficient visual search. The first are bottom-up processes related to the characteristics of retinotopic feature maps. The second are top-down mechanisms related to feature selection. To expose the potential involvement of other mechanisms, we introduce a new search paradigm whereby a target is defined only in a context-dependent ma...