• Journal of Korea Multimedia Society
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• v.7 no.3
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• pp.319-326
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• 2004

In this paper, we propose color image segmentation by region merging method preserving the boundary of an object. The proposed method selects initial region by using quantized image's index map after vector quantizing an original image. After then, we merge regions by applying boundary restricted factor in order to consider the boundary of an object in HSI color space. Also we merge the regions in RGB color space for non-processed regions in HSI color space. And we reduce processing time by decreasing iterative process in region merging algorithm. Experimental results have demonstrated the superiority in region's segmentation results and processing time for various images.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.72-75
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• 2008

Image segmentation techniques becoming increasingly important in the field of remote sensing image analysis in areas such as object oriented image classification. This paper presents a new method for image segmentation in High Resolution Remote Sensing Image based on Seeded Region Growing (SRG) and Edge Information. Firstly, multi-spectral edge detection was done using an entropy operator in pan-sharpened QuickBird imagery. Then, the initial seeds were automatically selected from the obtained edge map. After automatic selection of significant seeds, an initial segmentation was achieved by applying SRG. Finally the region merging process, using region adjacency graph (RAG), was carried out to get the final segmentation result. Experimental results demonstrated that the proposed method has good potential for application in the segmentation of high resolution satellite images.

• Journal of Korea Multimedia Society
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• v.19 no.7
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• pp.1116-1126
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• 2016

Image segmentation is widely used in the pre-processing stage of image analysis and, therefore, the accuracy of image segmentation is important for performance of an image-based analysis system. An efficient image segmentation method is proposed, including a filtering process for super-pixels, improved saliency map information, and a merge process. The proposed algorithm removes areas that are not equal or of small size based on comparison of the area of smoothed superpixels in order to maintain generation of a similar size super pixel area. In addition, application of a bilateral filter to an existing saliency map that represents human visual attention allows improvement of separation between objects and background. Finally, a segmented result is obtained based on the suggested merging process without any prior knowledge or information. Performance of the proposed algorithm is verified experimentally.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.10
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• pp.2552-2562
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• 1996

In this paper, we propose a segmentation algorithm that can reliably separate moving objects from noisy background in the image sequance received from a camera at the fixed position. The proposed algorithm consists of three processes:generation of the difference image between the input image and the reference image, multilevel quantization of the difference image, and multistagemerging in the quantized image. The quantization process requantizes the difference image based on the multiple threshold values determined bythe histogram analysis. The merging starts from the seed region which created by using the highest threshold value and ends when termination conditions are met. the proposed method has been tested with various real imge sequances containing intruders. The test results show that the proposed algorithm can detect moving objects like intruders very effectively in the noisy environment.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1464-1466
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• 2003

classification of the satellite images provides information about land cover and/or land use. Quality of the classification result depends mainly on the spatial and spectral resolutions of the images. In this study, image fusion in terms of resolution merging, and band integration with multi-source of the satellite images; Landsat ETM+ and Ikonos were carried out to improve classification. Resolution merging and band integration could generate imagery of high resolution with more spectral bands. Precise image co-registration is required to remove geometric distortion between different sources of images. Combination of unsupervised and supervised classification of the fused imagery was implemented to improve classification. 3D display of the results was possible by combining DEM with the classification result so that interpretability could be improved.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.11
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• pp.102-113
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• 1997

In this paepr, a new image segmentation method reducing efficiently contour information causing bottleneck problem at segmentatio-based very low bit rate codingis proposed, while preserving objective and subjective quality. It consists of 4-level hierarchical image segmentation based on mathematical morphology and 1-leve region merging structure using contast of two adjacent regions. For two adjacent region pairs at the fourth level included in each region of the thid level, contrast is calculated. Among the pairs of two adjacent regions with less value than threshold, two adjacent regions having the minimum contrast are merged first. After region merging, texture of the merged region is updated. The procedure is performed recursively for all the adjacent region pairs at the fourth level included in each region of the third level. Compared with the previous method, the objective and subjective image qualities are similar. But it reduces 46.65% texture information on the average by decreasing total region number to be tansmitted. Specially, it shows reduction of the 23.95% contour information of the average. Thus, it can improve efficiently the bottleneck problem at segementation-based very low bit rate coding.

• Journal of Korea Multimedia Society
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• v.8 no.4
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• pp.473-481
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• 2005

In this paper, we propose an image segmentation method preserving object's boundaries by using the number of quantized colors and merging regions using adaptive threshold values. First of all, the proposed method quantizes an original image by a vector quantization and the number of quantized colors is determined differently using PSNR each image. We obtain initial regions from the quantized image, merge initial regions in CIE Lab color space and RGB color space step by step and segment the image into semantic regions. In each merging step, we use color distance between adjacent regions as similarity-measure. Threshold values for region-merging are determined adaptively according to the global mean of the color difference between the original image and its split-regions and the mean of those variations. Also, if the segmented image of RGB color space doesn't split into semantic objects, we merge the image again in the CIE Lab color space as post-processing. Whether the post-processing is done is determined by using the color distance between initial regions of the image and the segmented image of RGB color space. Experiment results show that the proposed method splits an original image into main objects and boundaries of the segmented image are preserved. Also, the proposed method provides better results for objective measure than the conventional method.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1996.06a
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• pp.175-180
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• 1996

In this paper, a new image segmentation method based on merging of two low contrast neighbor regions iteratively is proposed. It is suitable for very low bit rate coding. The proposed method reduces efficiently contour information and preserves subjective and objective image quality. It consists of image segmentation using 4-level hierarchical structure based on mathematical morphology and 1-level region merging structure using the contrast difference of two adjacent neighbor regions. For each segmented region of the third level, two adjacent neighbor regions having low contrast difference value in fourth level based on contrast difference value is merged iteratively. It preserves image quality and shows the noticeable reduction of the contour information, so that it can improve the bottleneck problem of segmentation-based coding at very low bit rate.

• Journal of information and communication convergence engineering
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• v.9 no.5
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• pp.487-490
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• 2011

This paper presents splitting and merging algorithm based on adaptive thresholding. The algorithm first divides the image into blocks, and then compares each block using the calculated thresholding value. The blocks which are same are merged using the certain threshold value and different blocks are split unless it satisfies the threshold value. When the block has been merged, maximum and minimum block sizes are determined then the average block size is determined. After the average block size is determined the average intensity and standard deviation of average block is calculated. The process of thresholding is applied to binarize the image. Finally, the experimental results show that the proposed method distinguishes clearly the background with text in the document image.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.100.5-100
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• 2002

A core technology for implementation of Augmented Reality is to develop a merging algorithm between interesting 3-D objects and real images. In this paper, we present a 3-D object recognition method to decide viewing direction toward the object from camera. This process is the starting point to merge with real image and 3-D objects. Perspective projection between a camera and 3-dimentional objects defines a plane in 3-D space that is from a line in an image and the focal point of the camera. If no errors with perfect 3-D models were introduced in during image feature extraction, then model lines in 3-D space projecting onto this line in the image would exactly lie in this plane. This observa...