We propose a method for learning using a set of feature representations which retrieve different amounts of information at different costs. The goal is to create a more efficient ...
Anelia Angelova, Larry Matthies, Daniel M. Helmick...
This paper presents a novel approach for landmarkbased shape deformation, in which fitting error and shape difference are formulated into a support vector machine (SVM) regression...
This paper describes ongoing work on creating a benchmarking and validation dataset for biological image segmentation. While the primary target is biological images, we believe th...
In this paper, a supervised pixel-based classifier approach for segmenting different anatomical regions in abdominal Computed Tomography (CT) studies is presented. The approach co...
Mikhail Kalinin, Daniela Stan Raicu, Jacob D. Furs...
Abstract. We present iCluster, a fast and efficient algorithm that clusters a set of images while co-registering them using a parameterized, nonlinear transformation model. The out...