Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
Non-Rigid Motion Estimation Using the Robust Tensor Method
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 1 - Volume 01
Graph Partitioning Active Contours (GPAC) for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Variational Framework for Multiregion Pairwise-Similarity-Based Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Moving object segmentation using the flux tensor for biological video microscopy
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
Cell segmentation using coupled level sets and graph-vertex coloring
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Robust tracking of migrating cells using four-color level set segmentation
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Efficient and reliable schemes for nonlinear diffusion filtering
IEEE Transactions on Image Processing
Classification of cell cycle phases in 3D confocal microscopy using PCNA and chromocenter features
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
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We present a method to improve the accuracy and speed, as well as significantly reduce the memory requirements, for the recently proposed Graph Partitioning Active Contours (GPACs) algorithm for image segmentation in the work of Sumengen and Manjunath (2006). Instead of computing an approximate but still expensive dissimilarity matrix of quadratic size, (Ns2 Ms2)/(nsms), for a 2D image of size Ns×Ms and regular image tiles of size ns×ms, we use fixed length histograms and an intensity-based symmetric-centrosymmetric extensor matrix to jointly compute terms associated with the complete NsMs × NsMs dissimilarity matrix. This computationally efficient reformulation of GPAC using a very small memory footprint offers two distinct advantages over the original implementation. It speeds up convergence of the evolving active contour and seamlessly extends performance of GPAC to multidimensional images.