This paper describes a semi-automatic method for moving object segmentation and tracking. This method is suitable when a few objects have to be tracked, while the camera moves and fixates on them. The user delineates approximately the initial locations in a selected frame and specifies the depth ordering of the objects to be tracked. First, motion-based segmentation is obtained through an initi...

In this paper, an automatic seeded region growing algorithm is proposed for cellular image segmentation. First, the regions of interest (ROIs) extracted from the preprocessed image. Second, the initial seeds are automatically selected based on ROIs extracted from the image. Third, the most reprehensive seeds are selected using a machine learning algorithm. Finally, the cellular image is segment...

This paper proposes an effective framework to compute the visibility guarding and star decomposition of 3D solid shapes. We propose a progressive integer linear programming algorithm to solve the guarding points that can visibility cover the entire shape; we also develop a constrained region growing scheme seeded on these guarding points to get the star decomposition. We demonstrate this guardi...

The most widely followed procedure for diagnosis and prognosis of dementia is structural neuroimaging of hippocampus by means of MR. Hippocampus segmentation is of wide interest as it enables quantitative assessment of the structure. In this paper, we propose an algorithm for hippocampus segmentation that is unsupervised and image driven. It is based on a hybrid approach which combines a coarse...

This paper focuses on flood-region detection using monitoring images. However, adverse weather affects the outcome of image segmentation methods. In this paper, we present an experimental comparison of an outdoor visual sensing system using region-growing methods with two different growing rules-namely, GrowCut and RegGro. For each growing rule, several tests on adverse weather and lens-stained...

Recently Adams and Bischof (1994) proposed a novel region growing algorithm for segmenting intensity images. The inputs to the algorithm are the intensity image and a set of seeds individual points or connected components that identify the individual regions to be segmented. The algorithm grows these seed regions until all of the image pixels have been assimilated. Unfortunately the algorithm i...

To cope with the difficulty of 3D MRI brain scans segmentation, specification and instantiation of a priori models should be constrained by local images characteristics. We introduce situated cooperative agents for the extraction of domain and control knowledge from image grey levels. Their dedicated behaviours, i.e segmentation of one type of tissue, are dynamically adapted function of their p...

We develop an algorithm to segment a 3D surface mesh intovisually meaningful regions based on analyzing the local geometry ofvertices. We begin with a novel characterization of mesh vertices as convex,concave or hyperbolic depending upon their discrete local geometry.Hyperbolic and concave vertices are considered potential feature regionboundaries. We propose a new region growing technique star...

This paper presents the region based segmentation method for ultrasound images using local statistics. In this segmentation approach the homogeneous regions depends on the image granularity features, where the interested structures with dimensions comparable to the speckle size are to be extracted. This method uses a look up table comprising of the local statistics of every pixel, which are con...

This work presents a region-growing image segmentation approach based on superpixel decomposition. From an initial contour-constrained over-segmentation of the input image, the image segmentation is achieved by iteratively merging similar superpixels into regions. This approach raises two key issues: (1) how to compute the similarity between superpixels in order to perform accurate merging and ...