Algorithms for clustering data
Algorithms for clustering data
Depth from focus using pyramid architecture
Pattern Recognition Letters
Ten lectures on wavelets
Efficient and effective querying by image content
Journal of Intelligent Information Systems - Special issue: advances in visual information management systems
Communications of the ACM
Segmenting focused objects in complex visual images
Pattern Recognition Letters
IRM: integrated region matching for image retrieval
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Active Computer Vision by Cooperative Focus and Stereo
Active Computer Vision by Cooperative Focus and Stereo
Digital Image Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Edge Flow: A Framework of Boundary Detection and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Microscopic shape from focus using a projected illumination pattern
Mathematical and Computer Modelling: An International Journal
Multiresolution 3-D range segmentation using focus cues
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Scalable integrated region-based image retrieval using IRM and statistical clustering
Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Region-Based Fuzzy Feature Matching Approach to Content-Based Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
An HMM-Based Segmentation Method for Traffic Monitoring Movies
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning-based linguistic indexing of pictures with 2--d MHMMs
Proceedings of the tenth ACM international conference on Multimedia
Multi-scale binarization of images
Pattern Recognition Letters
Multiresolution-based watersheds for efficient image segmentation
Pattern Recognition Letters
Pivot Vector Space Approach for Audio-Video Mixing
IEEE MultiMedia
Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
Depth-of-field-based alpha-matte extraction
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Integrated Computer-Aided Engineering
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Pattern Recognition
Focus Area Extraction by Blind Deconvolution for Defining Regions of Interest
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Image Communication
Image retrieval: Ideas, influences, and trends of the new age
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KPCA for semantic object extraction in images
Pattern Recognition
Lossy compression through segmentation on low depth-of-field images
Digital Signal Processing
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Expert Systems with Applications: An International Journal
Automatic segmentation of focused objects from images with low depth of field
Pattern Recognition Letters
Single image defocus map estimation using local contrast prior
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Multimedia news exploration and retrieval by integrating keywords, relations and visual features
Multimedia Tools and Applications
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Spot detection in images with noisy background
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A fuzzy segmentation of salient region of interest in low depth of field image
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
Segmenting focused objects based on the Amplitude Decomposition Model
Pattern Recognition Letters
Hi-index | 0.15 |
Unsupervised segmentation of images with low depth of field (DOF) is highly useful in various applications including image enhancement for digital cameras, target recognition, image indexing for content-based retrieval, and 3D microscopic image analysis. This paper describes a novel multiresolution image segmentation algorithm for low DOF images. The algorithm is designed to separate a sharply focused object-of-interest from other foreground or background objects. The algorithm is fully automatic in that all parameters are image independent. A multiscale approach based on high frequency wavelet coefficients and their statistics is used to perform context-dependent classification of individual blocks of the image. Unlike other edge-based approaches, our algorithm does not rely on the process of connecting object boundaries. The algorithm has achieved high accuracy when tested on more than 100 low DOF images, many with inhomogeneous foreground or background distractions. Compared with the state of the art algorithms, this new algorithm provides better accuracy at higher speed.