A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Spatial Thresholding Method for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchy in Picture Segmentation: A Stepwise Optimization Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
An efficient agglomerative clustering algorithm using a heap
Pattern Recognition
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Topographic distance and watershed lines
Signal Processing - Special issue on mathematical morphology and its applications to signal processing
A VHDL primer (3rd ed.)
Finding salient regions in images: nonparametric clustering for image segmentation and grouping
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
The watershed transform: definitions, algorithms and parallelization strategies
Fundamenta Informaticae - Special issue on mathematical morphology
Adaptive Split-and-Merge Segmentation Based on Piecewise Least-Square Approximation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Quantitative methods of evaluating image segmentation
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
Feature-based cluster segmentation of image sequences
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 3 - Volume 3
EURASIP Journal on Embedded Systems - Special issue on design and architectures for signal and image processing
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Image segmentation is the process of isolating objects in an input image, that is, partitioning the image into disjoint regions, such that each region is homogeneous with respect to some property, such as gray value or texture. Watershed-based image segmentation has gained much popularity in the field of biomedical image processing and computer vision where large images are not uncommon. Time-critical applications like road traffic monitoring, and steel fissure analysis require fast realization of the segmentation results. The present paper proposes a fast watershed transform based on hillclimbing technique. The complexity of the algorithm has been reduced by doing away with multiplication normally required to form a lower complete image in an intermediate step of the overall segmentation. The reduced complexity makes the algorithm suitable for dedicated hardware implementation. An FPGA-based architecture has been developed to implement the proposed algorithm involving moderate hardware complexity. This architecture enhances the applicability of this algorithm for real-time applications.