Mean Shift Segmentation - Evaluation of Optimization Techniques
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Summary
This paper discusses and evaluates different optimization strategies for mean shift based image segmentation, and compares segmentation results of heuristic-based, performance-optimized implementations with the segmentation result of the original mean shift algorithm as a gold standard.
- Type
- article
- Published
- 2008-01-01
- Cited by
- 22
- References
- 18
- Access
- Open access
- OpenAlex
- https://openalex.org/W56630691
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:42496113
Keywords
Mean-shift, Segmentation, Image segmentation, Computer science, Cluster analysis
References
- Supervised range-constrained thresholding
- Columbia Object Image Library (COIL100)
- Advances in Pattern Recognition
- Mean shift analysis and applications
- Mean Shift, Mode Seeking, and Clustering
- High Dynamic Range Images as a Basis for Detection of Argyrophilic Nucleolar Organizer Regions Under Varying Stain Intensities
- Mean Shift: A Robust Approach Toward Feature Space Analysis
- A novel multipurpose tree and path matching algorithm with application to airway trees
- Synergism in low level vision
- Toward a generic evaluation of image segmentation
- Mean shift based clustering in high dimensions: a texture classification example
- Random Walks for Image Segmentation
- Acceleration Strategies for Gaussian Mean-Shift Image Segmentation
- The estimation of the gradient of a density function, with applications in pattern recognition
- Robust analysis of feature spaces: color image segmentation
- Level Set Methods and Fast Marching Methods/ J. A. Sethian
- Methodology for evaluating image-segmentation algorithms
- The Hungarian method for the assignment problem
- Advanced Algorithmic Approaches to Medical Image Segmentation
- Level Set Methods and Fast Marching Methods
Cited by
- Influence of image compression on object detection in natural images segmented with mean shift
- Recovering depth from images using adaptive depth from focus
- An Automatic Algorithm for Object Recognition and Detection Based on ASIFT Keypoints
- An Enhanced Segmentation Method by Combining Super Resolution and Level Set
- Full Object Boundary Detection by Applying Scale Invariant Features in a Region Merging Segmentation Algorithm
- Image segmentation from scale and rotation invariant texture features from the double dyadic dual-tree complex wavelet transform
- Depth estimation from a single image using defocus cues
- A Histogram Transform for ProbabilityDensity Function Estimation
- A framework based on the Affine Invariant Regions for improving unsupervised image segmentation
- Neuronal mapped hybrid background segmentation for video object tracking
- GEO matching regions: multiple regions of interests using content based image retrieval based on relative locations
- Using Geodesic Space Density Gradients for Network Community Detection
- A procedure for semi-automated cadastral boundary feature extraction from high-resolution satellite imagery
- A contemporary approach for object recognition based on spatial layout and low level features’ integration
- Using Mean Shift for Iranian License Plate Detection
- A review on medical image segmentation: techniques and its efficiency
- Evaluation of land resource balance using interpretation and object-based classification method (case study : BWP Lumajang,Lumajang district)
- Comparison of mean shift algorithms and evaluation of their stability
- Information extraction from paper maps using object oriented analysis OOA
- Source Code Analysis for Performance Enhancement of the Mean Shift Algorithm
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