With the rapid developments of higher resolution imaging systems, larger image data are produced. To process the increasing image data with conventional methods, the processing time increases tremendously. Image segmentation is one of the many image processing algorithms, and it is widely used in medical imaging (i.e. find tumor in MRI), robotic vision (i.e. vision-based navigation), and face r...

Watershed transform is considered to be an important technique in image segmentation. It is the method for image segmentation in the section of mathematical morphology. Work of watershed segmentation proceeds using the neighboring pixels. We present a critical review of several definitions of the watershed transform and the associated sequential algorithms, immersion models and therefore parall...

Watershed transform is considered to be an important technique in image segmentation. It is the method for image segmentation in the section of mathematical morphology. Work of watershed segmentation proceeds using the neighboring pixels. We present a critical review of several definitions of the watershed transform and the associated sequential algorithms, immersion models and therefore parall...

Watershed segmentation/transform is a classical method for image segmentation in gray scale mathematical morphology. Nevertheless watershed algorithm has strong recursive nature, so straightforward parallel one has a very low efficiency. Firstly, the advantages and disadvantages of some existing parallel algorithms are analyzed. Then, a Further Optimized Parallel Watershed Algorithm (FOPWA) is ...

The stochastic watershed is a probabilistic segmentation approach which estimates the probability density of contours of the image from a given gradient. In complex images, the stochastic watershed can enhance insignificant contours. To partially address this drawback, we introduce here a fully unsupervised multi-scale approach including bagging. Re-sampling and bagging is a classical stochasti...

In this paper, we discuss the use of graph-cuts to merge the regions of the watershed transform optimally. Watershed is a simple, intuitive and efficient way of segmenting an image. Unfortunately it presents a few limitations such as over-segmentation and poor detection of low boundaries. Our segmentation process merges regions of the watershed over-segmentation by minimizing a specific criteri...

Watershed transform is considered to be an important technique in image segmentation. It is the method for image segmentation in the section of mathematical morphology. Work of watershed segmentation proceeds using the neighboring pixels. We present a critical review of several definitions of the watershed transform and the associated sequential algorithms, immersion models and therefore parall...

Watershed transform is considered to be an important technique in image segmentation. It is the method for image segmentation in the section of mathematical morphology. Work of watershed segmentation proceeds using the neighboring pixels. We present a critical review of several definitions of the watershed transform and the associated sequential algorithms, immersion models and therefore parall...

Watershed segmentation/transform is a classical method for image segmentation in gray scale mathematical morphology. Nevertheless watershed algorithm has strong recursive nature, so straightforward parallel one has very low efficiency. Firstly, the advantages and disadvantages of some existing parallel algorithms are analyzed. Secondly, a Further Optimized Parallel Watershed Algorithm (FOPWA) i...

In this paper, a formal de"nition and a new algorithmic technique for the watershed transformation is presented. The novelty of the approach is to adapt the connected component operator to solve the watershed segmentation problem. The resulting algorithm is independent of the number of grey-levels, employs simple data structures, requires less error prone memory management, and issues a lower c...