Computer Vision, Graphics, and Image Processing
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
The adaptive pyramid: a framework for 2D image analysis
CVGIP: Image Understanding
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Elliptical Head Tracking Using Intensity Gradients and Color Histograms
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Learning a Classification Model for Segmentation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
ACM SIGGRAPH 2004 Papers
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Image Segmentation by Mean Shift Clustering and MDL-Guided Region Merging
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
ACM SIGGRAPH 2005 Papers
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A 2-phase 2-D thresholding algorithm
Digital Signal Processing
ACM SIGGRAPH Asia 2010 papers
Fast interactive image segmentation by discriminative clustering
Proceedings of the 2010 ACM multimedia workshop on Mobile cloud media computing
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
Interactive segmentation of non-star-shaped contours by dynamic programming
Pattern Recognition
Color texture image segmentation based on neutrosophic set and wavelet transformation
Computer Vision and Image Understanding
Entropy-based automatic segmentation of bones in digital X-ray images
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Expert Systems with Applications: An International Journal
Interactive image segmentation by matching attributed relational graphs
Pattern Recognition
A dynamic threshold approach for skin tone detection in colour images
International Journal of Biometrics
A local region-based Chan-Vese model for image segmentation
Pattern Recognition
Robust interactive segmentation via coloring
Proceedings of the 1st International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications
Mean shift-based lesion detection of gastroscopic images
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
LS-SVM based image segmentation using color and texture information
Journal of Visual Communication and Image Representation
Error-tolerant interactive image segmentation using dynamic and iterated graph-cuts
Proceedings of the 2nd ACM international workshop on Interactive multimedia on mobile and portable devices
A survey of graph theoretical approaches to image segmentation
Pattern Recognition
Interactive segmentation with recommendation of most informative regions
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
An Adaptive Thresholding algorithm of field leaf image
Computers and Electronics in Agriculture
SPOID: a system to produce spot-the-difference puzzle images with difficulty
The Visual Computer: International Journal of Computer Graphics
Object information based interactive segmentation for fatty tissue extraction
Computers in Biology and Medicine
Automatic image segmentation using constraint learning and propagation
Digital Signal Processing
Segmentation of color images using a linguistic 2-tuples model
Information Sciences: an International Journal
Multi-objective image segmentation with an interactive evolutionary computation approach
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Computational intelligence models for image processing and information reasoning
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Efficient and effective image segmentation is an important task in computer vision and object recognition. Since fully automatic image segmentation is usually very hard for natural images, interactive schemes with a few simple user inputs are good solutions. This paper presents a new region merging based interactive image segmentation method. The users only need to roughly indicate the location and region of the object and background by using strokes, which are called markers. A novel maximal-similarity based region merging mechanism is proposed to guide the merging process with the help of markers. A region R is merged with its adjacent region Q if Q has the highest similarity with Q among all Q's adjacent regions. The proposed method automatically merges the regions that are initially segmented by mean shift segmentation, and then effectively extracts the object contour by labeling all the non-marker regions as either background or object. The region merging process is adaptive to the image content and it does not need to set the similarity threshold in advance. Extensive experiments are performed and the results show that the proposed scheme can reliably extract the object contour from the complex background.