Split-and-merge Procedure for Image Segmentation using Bimodality Detection Approach

Chaudhuri, Debasis; Agrawal, Ayush

doi:10.14429/dsj.60.356

Cited by 34 publications

(24 citation statements)

References 22 publications

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“…Region growing and region splitting-merging, on the basis of some predefined criteria, are two well-known techniques used for image segmentation [5,36]. First one is, in general, evaluated pixel-wise, whereas the second one splits or merges the image regions block-by-block employing a quad-tree structure.…”

Section: Region Shrink-mergementioning

confidence: 99%

Fingerprint Image Segmentation Using Block-Based Statistics and Morphological Filtering

Das

Mukhopadhyay

2015

Arab J Sci Eng

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Fingerprint segmentation is meant to separate the foreground region of a fingerprint image from its background region. This paper presents a block-based segmentation scheme which is executed in two passes. In the first pass, two sets of regions of interest (ROI) are identified separately using (i) morphological open-close filters and (ii) a statistical measure namely coefficient of variation (CV). These sets of ROIs are combined together to identify the overall ROI. In the second pass, a block-wise region shrink-merge technique, which employs a sequential combination of parameters like CV and average gray value, is applied to construct the final segmented image. The proposed method has been implemented and tested on a set of real fingerprint images and the experimental results visually establish the effectiveness of the proposed method. Besides, a comparative study based on some quantitative measures is furnished to verify the accuracy of the proposed segmentation algorithm.

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Section: Region Shrink-mergementioning

confidence: 99%

Fingerprint Image Segmentation Using Block-Based Statistics and Morphological Filtering

Das

Mukhopadhyay

2015

Arab J Sci Eng

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“…In this paper, we have implemented our previous split-and-merge segmentation procedure 16 for segmentation of the enhanced image. The conventional split-and-merge algorithm is lacking in adaptability to the image semantics because of its stiff quad tree based structure.…”

Section: Segmentationmentioning

confidence: 99%

“…The conventional split-and-merge algorithm is lacking in adaptability to the image semantics because of its stiff quad tree based structure. Author in his paper, an automatic thresholding technique based on bimodality detection approach with nonhomogeneity criterion is employed in the splitting phase of the split-and-merge segmentation scheme to directly reflect the image semantics to the image segmentation results 16 . The splitting technique is based on bimodality detection approach in recursive way until the homogeneous region is detected.…”

Section: Segmentationmentioning

confidence: 99%

“…In our paper, we have introduced a sequential arrange based or a minimal spanning tree based approach, that depends on data dimensionality of the weighted centroids of all splitted regions for finding the pairwise adjacent regions. Finally, to overcome the problems caused by the splitting technique, a novel merging technique based on the density ratio of the adjacent pair regions have been suggested 16 .…”

Section: Segmentationmentioning

confidence: 99%

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Automatic Bright Circular Type Oil Tank Detection Using Remote Sensing Images

Kushwaha¹,

Chaudhuri²,

Singh³

2013

DSJ

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Automatic target detection like oil tank from satellite based remote sensing imagery is one of the important domains in many civilian and military applications. This could be used for disaster monitoring, oil leakage, etc. We present an automatic approach for detection of circular shaped bright oil tanks with high accuracy. The image is first enhanced to emphasize the bright objects using a morphological approach. Then, the enhanced image is segmented using split-and-merge segmentation technique. Here, we introduce a knowledge base strategy based on the region removal technique and spatial relationship operation for detection of possible oil tanks from the segmented image using minimal spanning tree. Lastly, we introduce a supervised classifier, for identification of oil tanks, based on the knowledge database of large amount data of oil tanks. The uniqueness of the proposed technique is that it is useful for detection bright oil tanks from high as well as low resolution images, but the technique is always better for high-resolution imagery. We have systematically evaluated the algorithm on different satellite images like IRS -1C, IKONOS, QuickBird, and CARTOSAT -2A. The proposed technique is detected bright structures but unable to detect the dark structure. If the oil tank structures are bright relative to the background illumination in the image then the detection accuracy by the proposed technique for the high resolution image is more than 95 per cent.

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“…Developing segmentation algorithms still one of the most topics research common in the field of image processing. So far, there are many segmentation methods that can be classified into four main types including region based segmentation like region-growing [1,2] and region based split and merging [3,4], edge-based segmentation [5,6], histogram thresholding based method [7,8] and Segmentation based on hybridization between two of the first three segmentations.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Multi Level Thresholding Method for Image Segmentation Based on the Hybridization of Modified PSO and Otsu’s Method

Hamdaoui

Sakly

Mtibaa

2014

Studies in Computational Intelligence

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In the area of image processing, segmentation of an image into multiple regions is very important for classification and recognition steps. It has been widely used in many application fields such as medical image analysis to characterize and detect anatomical structures, robotics features extraction for mobile robot localization and detection and map procession for lines and legends finding. Many techniques have been developed in the field of image segmentation. Methods based on intelligent techniques are the most used such as Genetic Algorithm (GA), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and Particle Swarm Optimization (PSO) called metaheuristics algorithms. In this paper, we describe a novel method for segmentation of images based on one of the most popular and efficient metaheuristic algorithm called Particle Swarm optimization (PSO) for determining multilevel threshold for a given image. The proposed method takes advantage of the characteristics of the particle swarm optimization and improves the objective function value to updating the velocity and the position of particles. This method is compared to the basic PSO method, also, it is compared with other known multilevel segmentation methods to demonstrate its efficiency. Experimental results show that this method can reliably segment and give threshold values than other methods considering different measures.

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Split-and-merge Procedure for Image Segmentation using Bimodality Detection Approach

Cited by 34 publications

References 22 publications

Fingerprint Image Segmentation Using Block-Based Statistics and Morphological Filtering

Fingerprint Image Segmentation Using Block-Based Statistics and Morphological Filtering

Automatic Bright Circular Type Oil Tank Detection Using Remote Sensing Images

An Efficient Multi Level Thresholding Method for Image Segmentation Based on the Hybridization of Modified PSO and Otsu’s Method

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