Intelligent scissors for image composition
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Interactive segmentation with Intelligent Scissors
Graphical Models and Image Processing
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Image Foresting Transform: Theory, Algorithms, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM SIGGRAPH 2004 Papers
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Multilabel Random Walker Image Segmentation Using Prior Models
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
SIOX: Simple Interactive Object Extraction in Still Images
ISM '05 Proceedings of the Seventh IEEE International Symposium on Multimedia
A Closed Form Solution to Natural Image Matting
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Random Walks for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Practical Approach to Boundary Accurate Multi-Object Extraction from Still Images and Videos
ISM '06 Proceedings of the Eighth IEEE International Symposium on Multimedia
Weights and Topology: A Study of the Effects of Graph Construction on 3D Image Segmentation
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
GeoS: Geodesic Image Segmentation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Links Between Image Segmentation Based on Optimum-Path Forest and Minimum Cut in Graph
Journal of Mathematical Imaging and Vision
Synergistic arc-weight estimation for interactive image segmentation using graphs
Computer Vision and Image Understanding
An energy minimization approach to the data driven editing of presegmented images/volumes
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
User-steered image segmentation using live markers
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Interactive object segmentation from multi-view images
Journal of Visual Communication and Image Representation
Active learning for interactive segmentation with expected confidence change
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Object information based interactive segmentation for fatty tissue extraction
Computers in Biology and Medicine
User assisted disparity remapping for stereo images
Image Communication
Automatic image segmentation using constraint learning and propagation
Digital Signal Processing
Depth sculpturing for 2D paintings: A progressive depth map completion framework
Journal of Visual Communication and Image Representation
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One weakness in the existing interactive image segmentation algorithms is the lack of more intelligent ways to understand the intention of user inputs. In this paper, we advocate the use of multiple intuitive user inputs to better reflect a user's intention. In particular, we propose a constrained random walks algorithm that facilitates the use of three types of user inputs: 1] foreground and background seed input, 2] soft constraint input, and 3] hard constraint input, as well as their combinations. The foreground and background seed input allows a user to draw strokes to specify foreground and background seeds. The soft constraint input allows a user to draw strokes to indicate the region that the boundary should pass through. The hard constraint input allows a user to specify the pixels that the boundary must align with. Our proposed method supports all three types of user inputs in one coherent computational framework consisting of a constrained random walks and a local editing algorithm, which allows more precise contour refinement. Experimental results on two benchmark data sets show that the proposed framework is highly effective and can quickly and accurately segment a wide variety of natural images with ease.