Pixel- and region-based image fusion with complex wavelets
Information Fusion
Combined edge detection using wavelet transform and signal registration
Image and Vision Computing
Segmentation of small objects in color images
Programming and Computing Software
Region-Oriented Visual Attention Framework for Activity Detection
Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint
Modeling Attention and Perceptual Grouping to Salient Objects
Attention in Cognitive Systems
Segmentation-driven image fusion based on alpha-stable modeling of wavelet coefficients
IEEE Transactions on Multimedia
Hybrid Framework to Image Segmentation
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Unsupervised image compression using graphcut texture synthesis
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
An agglomerative segmentation framework for non-convex regions within uterine cervix images
Image and Vision Computing
Journal of Biomedical Imaging
Adaptive edge detection with directional wavelet transform
SSIP'05 Proceedings of the 5th WSEAS international conference on Signal, speech and image processing
Biometric animal databases from field photographs: identification of individual zebra in the wild
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Statistical multisensor image segmentation in complex wavelet domains
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
Constrained region-growing and edge enhancement towards automated semantic video object segmentation
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
A regional image fusion based on similarity characteristics
Signal Processing
Multimedia Tools and Applications
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The goal of segmentation is to partition an image into disjoint regions, in a manner consistent with human perception of the content. For unsupervised segmentation of general images, however, there is the competing requirement not to make prior assumptions about the scene. Here, a two-stage method for general image segmentation is proposed, which is capable of processing both textured and nontextured objects in a meaningful fashion. The first stage extracts texture features from the subbands of the dual-tree complex wavelet transform. Oriented median filtering is employed, to circumvent the problem of texture feature response at step edges in the image. From the processed feature images, a perceptual gradient function is synthesised, whose watershed transform provides an initial segmentation. The second stage of the algorithm groups together these primitive regions into meaningful objects. To achieve this, a novel spectral clustering technique is proposed, which introduces the weighted mean cut cost function for graph partitioning. The ability of the proposed algorithm to generalize across a variety of image types is demonstrated.