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Hybrid Image Segmentation Using Watersheds and Fast Region Merging
 IEEE transactions on Image Processing
, 1998
"... Abstract—A hybrid multidimensional image segmentation algorithm is proposed, which combines edge and regionbased techniques through the morphological algorithm of watersheds. An edgepreserving statistical noise reduction approach is used as a preprocessing stage in order to compute an accurate est ..."
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Cited by 141 (1 self)
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Abstract—A hybrid multidimensional image segmentation algorithm is proposed, which combines edge and regionbased techniques through the morphological algorithm of watersheds. An edgepreserving statistical noise reduction approach is used as a preprocessing stage in order to compute an accurate estimate of the image gradient. Then, an initial partitioning of the image into primitive regions is produced by applying the watershed transform on the image gradient magnitude. This initial segmentation is the input to a computationally efficient hierarchical (bottomup) region merging process that produces the final segmentation. The latter process uses the region adjacency graph (RAG) representation of the image regions. At each step, the most similar pair of regions is determined (minimum cost RAG edge), the regions are merged and the RAG is updated. Traditionally, the above is implemented by storing all RAG edges in a priority queue. We propose a significantly faster algorithm, which additionally maintains the socalled nearest neighbor graph, due to which the priority queue size and processing time are drastically reduced. The final segmentation provides, due to the RAG, onepixel wide, closed, and accurately localized contours/surfaces. Experimental results obtained with twodimensional/threedimensional (2D/3D) magnetic resonance images are presented. Index Terms — Image segmentation, nearest neighbor region merging, noise reduction, watershed transform. I.
Automatic Image Segmentation by Dynamic Region Merging
 IEEE Trans. On Image Processing
, 2011
"... Abstract: This paper addresses the automatic image segmentation problem in a region merging style. With an initially oversegmented image, in which the many regions (or superpixels) with homogeneous color are detected, image segmentation is performed by iteratively merging the regions according to ..."
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Cited by 7 (0 self)
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Abstract: This paper addresses the automatic image segmentation problem in a region merging style. With an initially oversegmented image, in which the many regions (or superpixels) with homogeneous color are detected, image segmentation is performed by iteratively merging the regions according to a statistical test. There are two essential issues in a region merging algorithm: order of merging and the stopping criterion. In the proposed algorithm, these two issues are solved by a novel predicate, which is defined by the sequential probability ratio test (SPRT) and the minimal cost criterion. Starting from an oversegmented image, neighboring regions are progressively merged if there is an evidence for merging according to this predicate. We show that the merging order follows the principle of dynamic programming. This formulates image segmentation as an inference problem, where the final segmentation is established based on the observed image. We also prove that the produced segmentation satisfies certain global properties. In addition, a faster algorithm is developed to accelerate the region merging process, which maintains a nearest neighbor graph (NNG) in each iteration. Experiments on real natural images are conducted to demonstrate the performance of the proposed dynamic region merging algorithm.
Hybrid Image Segmentation Using Watersheds
, 1996
"... A hybrid image segmentation algorithm is proposed which combines edge and regionbased techniques through the morphological algorithm of watersheds. The algorithm consists of the following steps: a) Edgepreserving statistical noise reduction, b) Gradient Approximation, c) Detection of watersheds o ..."
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Cited by 2 (1 self)
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A hybrid image segmentation algorithm is proposed which combines edge and regionbased techniques through the morphological algorithm of watersheds. The algorithm consists of the following steps: a) Edgepreserving statistical noise reduction, b) Gradient Approximation, c) Detection of watersheds on gradient magnitude image, and d) Hierarchical Region Merging (HRM) in order to get semantically meaningful segmentations. The HRM process uses the Region Adjacency Graph (RAG) representation of the image regions. At each step, the most similar pair of regions is determined (minimum cost RAG edge), the regions are merged and the RAG is updated. Traditionally, the above is implemented by storing all the RAG edges in a priority queue (heap). We propose a signi cantly faster algorithm which maintains an additional graph, the Most Similar Neighbor Graph, through which the priority queue size and processing time are drastically reduced. The nal segmentation is an image partition which, through the RAG, provides information that can be used by knowledgebased high level processes, i.e. recognition. In addition, this region based representation provides onepixel wide, closed, and accurately localized contours/surfaces. Due to the small number of free parameters, the algorithm can be quite eectively used in interactive image processing. Experimental results obtained with 2D MR images are presented.
Color Image Segmentation Based on Adaptive Local Thresholds
"... The goal of still color image segmentation is to divide the image into homogeneous regions. Object extraction, object recognition and objectbased compression are typical applications that use still segmentation as a lowlevel image processing. In this paper we present a new method for color image s ..."
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The goal of still color image segmentation is to divide the image into homogeneous regions. Object extraction, object recognition and objectbased compression are typical applications that use still segmentation as a lowlevel image processing. In this paper we present a new method for color image segmentation. The proposed algorithm divides the image into homogeneous regions by local thresholds. The number of thresholds and their values are adaptively derived by an automatic process, where local information is taken into consideration. First, the watershed algorithm is applied. Its results are used as an initialization for the next step, which is iterative merging process. During the iterative process regions are merged and local thresholds are derived. The thresholds are determined onebyone at different times during the merging process. Every threshold is calculated by local information on any region and its surroundings. Any statistical information on the input images is not given. The algorithm is found to be reliable and robust to different kind of images.
unknown title
, 1205
"... In region merging the criterion of stopping and merging order are essential. In this paper this two issues are solved by SPRT and the minimal cost criterion. Starting from an over segmented image, neighbouring regions are progressively merged if there is an evidence for merging. The principle of dyn ..."
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In region merging the criterion of stopping and merging order are essential. In this paper this two issues are solved by SPRT and the minimal cost criterion. Starting from an over segmented image, neighbouring regions are progressively merged if there is an evidence for merging. The principle of dynamic programming used for merging. The final segmentation is based on the observed image. We are also satisfies the certain global properties of segmentation. In this algorithm region merging process become faster due to nearest neighbour graph in each iteration. 1.