The Fast watershed transform is an innovative approach that detects salient objects in an image. This transformation is entirely different from traditional watershed as it doesn’t depend on mathematical morphology. The flooding of water in the image is efficiently simulated using the FIFO queue of pixels. It starts with sorting image pixel according to their intensity levels and stores them in ...

The watershed transform is the method of choice for image segmentation in the field of mathematical morphology. We present a critical review of several definitions of the watershed transform and the associated sequential algorithms, and discuss various issues which often cause confusion in the literature. The need to distinguish between definition, algorithm specification and algorithm implemen...

The watershed transform is widely used for image segmentation on computer vision applications. However, sequential watershed algorithms are not suitable for real-time applications, once they are one demanding part of several tasks. This paper proposes a new parallel algorithm for the watershed transform focused on real-time image segmentation using off-the-shelf GPUs. In this sense, this algori...

The watershed algorithm based on connected components is selected for the implementation, as it exhibits least computational complexity, good segmentation quality and can be implemented in the FPGA. It has simplified memory access compared to all other watershed based image segmentation algorithms. This paper proposes a new hardware implementation of the selected watershed algorithm. The main a...

In this paper we propose an algorithm and associated data model for creating a watershed boundary using a 2D Flow Direction Grid. Flow Direction Grids (FDGs) are common abstractions for hydrodynamic models and are utilized for delineating physical systems (e.g. watersheds, fluvial, and non-fluvial flow paths). The proposed algorithm and associated data model provides geometric speed increases i...

In this work, we extend a common framework for graph-based image segmentation that includes the graph cuts, random walker, and shortest path optimization algorithms. Viewing an image as a weighted graph, these algorithms can be expressed by means of a common energy function with differing choices of a parameter q acting as an exponent on the differences between neighboring nodes. Introducing a ...

An efficient model to store and retrieve surface watershed boundaries using graph theoretic approaches is proposed. Our approach utilizes three algorithms and accepts as input standard digital elevation models derived stream catchment boundaries. The first is called Modified Nested Set (MNS), which is a generalized depth first graph traversal algorithm that searches across stream reaches (verti...

Image segmentation is the basis for computer vision and object recognition. Watershed transform is one of the common methods used for region based segmentation. The previous watershed methods results in over segmentation. In this paper we present a novel method for efficient image segmentation by using bit-plane slicing and markercontrolled watershed. Bit-Plane slicing method produces the slice...