• Journal of Broadcast Engineering
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• v.2 no.2
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• pp.156-169
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• 1997

This paper proposes an image segmentation technique using watershed algorithm followed by region merging method. A gradient image is obtained by applying multiscale gradient algorithm to the image simplified by morphological filters. Since the watershed algorithm produces the oversegmented image. it is necessary to merge small segmented regions as wel]' as region having similar characteristics. For region merging. we utilize the merging criteria based on both the mean value of the pixels of each region and the edge intensities between regions obtained by the contour following process. Experimental results show that the proposed method produces meaningful image segmentation results.

• ETRI Journal
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• v.43 no.1
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• pp.109-119
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• 2021

Self-recovery is a tamper-detection and image recovery methods based on data hiding. It generates two types of data and embeds them into the original image: authentication data for tamper detection and reference data for image recovery. In this paper, a region-based scalable self-recovery (RSS) method is proposed for salient-object images. As the images consist of two main regions, the region of interest (ROI) and the region of non-interest (RONI), the proposed method is aimed at achieving higher reconstruction quality for the ROI. Moreover, tamper tolerability is improved by using scalable recovery. In the RSS method, separate reference data are generated for the ROI and RONI. Initially, two compressed bitstreams at different rates are generated using the embedded zero-block coding source encoder. Subsequently, each bitstream is divided into several parts, which are protected through various redundancy rates, using the Reed-Solomon channel encoder. The proposed method is tested on 10 000 salient-object images from the MSRA database. The results show that the RSS method, compared to related methods, improves reconstruction quality and tamper tolerability by approximately 30% and 15%, respectively.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.2
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• pp.65-73
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• 2011

In this paper, we present a novel digital multifocusing approach using adaptive region-based histogram equalization for the multiple color-filter aperture (MCA) system with insufficient amount of incoming light. From the image acquired by the MCA system, we can estimate the depth information of objects at different distances by measuring the amount of misalignment among the RGB color planes. The estimated depth information is used to obtain multifocused images together with the process of the region-of-interests (ROIs) classification, registration, and fusion. However, the MCA system results in the low-exposure problem because of the limited size of the apertures. For overcoming this problem, we propose adaptive region-based histogram equalization. Based on the experimental results, the proposed algorithm is proved to be able to obtain in-focused images under the low light level environment.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.8B
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• pp.1391-1399
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• 2000

In this paper, we discussed the region-based image retrieval method using image segmentation. We proposed a segmentation method which can reduce the effect of a irregular light sources. The image segmentation method uses a region-merging, and candidate regions which are merged were selected by the energy values of high frequency bands in discrete wavelet transform. The content-based image retrieval is executed by using the segmented region information, and the images are retrieved by a color, texture, shape feature vector. The similarity measure between regions is processed by the Euclidean distance of the feature vectors. The simulation results shows that the proposed method is reasonable.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.4
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• pp.634-640
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• 2018

In this paper, we propose an automatic extraction algorithm of region of interest(ROI) based on medical x-ray images. The proposed algorithm uses segmentation, feature extraction, and reference image matching to detect lesion sites in the input image. The extracted region is searched for matching lesion images in the reference DB, and the matched results are automatically extracted using the Kalman filter based fitness feedback. The proposed algorithm is extracts the contour of the left hand image for extract growth plate based on the left x-ray input image. It creates a candidate region using multi scale Hessian-matrix based sessionization. As a result, the proposed algorithm was able to split rapidly in 0.02 seconds during the ROI segmentation phase, also when extracting ROI based on segmented image 0.53, the reinforcement phase was able to perform very accurate image segmentation in 0.49 seconds.

• Journal of the Optical Society of Korea
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• v.19 no.4
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• pp.390-396
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• 2015

We propose a partial occlusion removal method for computational integral imaging reconstruction (CIIR) based on the usage of the exemplar based inpainting technique. The proposed method is an improved version of the original linear inpainting based CIIR (LI-CIIR), which uses the inpainting technique to fill in the data missing region. The LI-CIIR shows good results for images which contain objects with smooth surfaces. However, if the object has a textured surface, the result of the LI-CIIR deteriorates, since the linear inpainting cannot recover the textured data in the data missing region well. In this work, we utilize the exemplar based inpainting to fill in the textured data in the data missing region. We call the proposed method the neighboring elemental image exemplar based inpainting (NEI-exemplar inpainting) method, since it uses sources from neighboring elemental images to fill in the data missing region. Furthermore, we also propose an automatic occluding region extraction method based on the use of the mutual constraint using depth estimation (MC-DE) and the level set based bimodal segmentation. Experimental results show the validity of the proposed system.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.661-664
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• 1998

Since the region segmentation at high resolution contains most of viable semantic object contours in an image, the bottom-up approach for image segmentation is appropriate for the application such as MPEG-4 which needs to preserve semantic object contours. However, the conventioal region merging methods, that follow the region segmentation, have poor performance in keeping low-contrast semantic object contours. In this paper, we propose an image segmentation algorithm based on classified region merging. The algorithm pre-segments an image with a large number of small regions, and also classifies it into several classes having similar gradient characteristics. Then regions only in the same class are merged according to the boundary weakness or statisticsal similarity. The simulation result shows that the proposed image segmentation preserves semantic object contours very well even with a small number of regions.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.215-218
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• 2013

In this paper, we proposed a method for enhancement of color images with sky regions. The input image is converted into HSV space and then sky and non-sky regions are separated. For sky region, saturation enhancement is performed for each pixel based on the enhancement factor calculated from the average saturation of its local neighborhood. On the other hand, for the non-sky region, the enhancement is applied only on the luminance value (V) component of the HSV color image, which is performed in two steps. The luminance enhancement, which is also called as dynamic range compression, is carried out using nonlinear transfer function. Again, each pixel is further enhanced for the adjustment of the image contrast depending upon the center pixel and its neighborhood pixel values. At last, the original H and V component image and enhanced S component image for the sky region, and original H and S component image and enhanced V component image for the non-sky region are converted back to RGB image.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.191-194
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• 2003

In this paper, we propose a new accurate and robust image mosaic technique of the mural underdrawing taken from the infra-red camera, which is based on multiple image registration and adaptive blending technique. The image mosaicing methods which have been developed so far have the following deficits. It is hard to generate a high resolution image when there are regions that do not have features or intensity gradients, and there is a trade-off in overlapping region site in view of registration and blending. We consider these issues as follows. First, in order to mosaic Images with neither noticeable features nor intensity gradients, we use a Projected supplementary pattern and pseudo color image for features in the image Pieces which are registered. Second, we search the overlapping region size with minimum blending error between two adjacent images and then apply blending technique to minimum error overlapping region. Finally, we could find our proposed method is more effective and efficient for image mosaicing than conventional mosaic techniques and also is more adequate for the application of infra-red mural underdrawing mosaicing. Experimental results show the accuracy and robustness of the algorithm.

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.49-60
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• 2020

To solve the edge ringing or block effect caused by the partial differential diffusion in image enhancement domain, a new image enhancement algorithm based on bidirectional diffusion, which smooths the flat region or isolated noise region and sharpens the edge region in different types of defect images on aviation composites, is presented. Taking the image pixel's neighborhood intensity and spatial characteristics as the attribute descriptor, the presented bidirectional diffusion model adaptively chooses different diffusion criteria in different defect image regions, which are elaborated are as follows. The forward diffusion is adopted to denoise along the pixel's gradient direction and edge direction in the pixel's smoothing area while the backward diffusion is used to sharpen along the pixel's gradient direction and the forward diffusion is used to smooth along the pixel's edge direction in the pixel's edge region. The comparison experiments were implemented in the delamination, inclusion, channel, shrinkage, blowhole and crack defect images, and the comparison results indicate that our algorithm not only preserves the image feature better but also improves the image contrast more obviously.