• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.126-129
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• 2008

In this paper, we proposed a method extracting an object from background of the satellite image. The image segmentation techniques have been widely studied for the technology to segment image and to synthesis segment object with other images. Proposed algorithm is to perform the edge detection of a selected object using genetic algorithm. We segment region of object based on detection edge using watershed algorithm. We separated background and object in indefinite region using gradual region merge from segment object. And, we make GUI for the application of the proposed algorithm to various tests. To demonstrate the effectiveness of the proposed method, several analysis on the satellite images are performed.

• Journal of Institute of Convergence Technology
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• v.11 no.1
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• pp.1-5
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• 2021

Deep learning based methods achieve state-of-the-art accuracy, however, they typically rely on supervised training with large labeled datasets. It is known in many medical applications that labeling medical images requires significant expertise and much time, and typical hand-tuned approaches for data augmentation fail to capture the complex variations in such images. This paper proposes a 3D image augmentation method to overcome these difficulties. It allows us to enrich diversity of training data samples that is essential in medical image segmentation tasks, thus reducing the data overfitting problem caused by the fact the scale of medical image dataset is typically smaller. Our numerical experiments demonstrate that the proposed approach provides significant improvements over state-of-the-art methods for 3D medical image segmentation.

• IEIE Transactions on Smart Processing and Computing
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• v.1 no.3
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• pp.125-132
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• 2012

This paper proposes an automatic object segmentation and background composition method for video communication over consumer mobile phones. The object regions were extracted based on the motion and color variance of the first two frames. To combine the motion and variance information, the Euclidean distance between the motion boundary pixel and the neighboring color variance edge pixels was calculated, and the nearest edge pixel was labeled to the object boundary. The labeling results were refined using the morphology for a more accurate and natural-looking boundary. The grow-cut segmentation algorithm begins in the expanded label map, where the inner and outer boundary belongs to the foreground and background, respectively. The segmented object region and a new background image stored a priori in the mobile phone was then composed. In the background composition process, the background motion was measured using the optical-flow, and the final result was synthesized by accurately locating the object region according to the motion information. This study can be considered an extended, improved version of the existing background composition algorithm by considering motion information in a video. The proposed segmentation algorithm reduces the computational complexity significantly by choosing the minimum resolution at each segmentation step. The experimental results showed that the proposed algorithm can generate a fast, accurate and natural-looking background composition.

• The Transactions of the Korea Information Processing Society
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• v.7 no.9
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• pp.2994-3001
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• 2000

In this paper. a method for color image segmentation using region merging is proposed. A inhomogeneity which exists in image is reduced by smoothing with non-linear filtering. saturation enhancement and intensity averaging in previous step of image segmentation. and a similar regions are segmented by non-uniform quantization using zero-crossing information of color histogram. A edge strength of initial region is measured using high frequency energy of wavelet transform. A candidate region which is merged in next step is selected by doing this process. A similarity measure for region merging is processed using Euclidean distance of R. G. B color channels. A Proposed method can reduce an over-segmentation results by irregular light sources et. al, and we illustrated that the proposed method is reasonable by simulation.

• 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.

• Journal of Korea Multimedia Society
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• v.17 no.10
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• pp.1171-1181
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• 2014

In this paper, we propose improved methods which are image conversion and extraction method of watershed seed using morphological characteristic of teeth on complement image. Conventional tooth segmentation methods are occurred low detection ratio at molar region and over, overlap segmentation owing to specular reflection and morphological feature of molars. Therefore, in order to solve the problems of the conventional methods, we propose the image conversion method and improved extraction method of watershed seed. First, the image conversion method is performed using RGB, HSI space of tooth image for to extract boundary and seed of watershed efficiently. Second, watershed seed is reconstructed using morphological characteristic of teeth. Last, individual tooth segmentation is performed using proposed seed of watershed by watershed algorithm. Therefore, as a result of comparison with marker controlled watershed algorithm and the proposed method, we confirmed higher detection ratio and accuracy than marker controlled watershed algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2082-2102
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• 2024

Accurate segmentation of magnetic resonance (MR) images is crucial for providing doctors with effective quantitative information for diagnosis. However, the presence of weak boundaries, intensity inhomogeneity, and noise in the images poses challenges for segmentation models to achieve optimal results. While deep learning models can offer relatively accurate results, the scarcity of labeled medical imaging data increases the risk of overfitting. To tackle this issue, this paper proposes a novel fuzzy c-means (FCM) model that integrates a deep learning approach. To address the limited accuracy of traditional FCM models, which employ Euclidean distance as a distance measure, we introduce a measurement function based on the skewed normal distribution. This function enables us to capture more precise information about the distribution of the image. Additionally, we construct a regularization term based on the Kullback-Leibler (KL) divergence of high-confidence deep learning results. This regularization term helps enhance the final segmentation accuracy of the model. Moreover, we incorporate orthogonal basis functions to estimate the bias field and integrate it into the improved FCM method. This integration allows our method to simultaneously segment the image and estimate the bias field. The experimental results on both simulated and real brain MR images demonstrate the robustness of our method, highlighting its superiority over other advanced segmentation algorithms.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.469-472
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• 2003

Skew is inevitably occurred in a scanned document image Thus, character recognition systems are generally very sensitive to a skew angle. In this paper, we propose a robust slant correction algorithm based on dithering and estimating vortical transition. Character strings are segmented by projecting the vertical transition point and the slant corrected image. The segmentation method using the vertical transition point can effectively split the character strings touching vertically each other. Experimental results show that the proposed method has achieved robust slant correction and good performance of character string segmentation.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.6
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• pp.1020-1024
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• 1987

Image segmentation based on the curvature of bi-directiona distribution functions of histogram with no mode informations is proposed. The curvature is an oscillating function and can be approximated to a polynomial form with a least square method using the Chebyshev basis. Nonhomogeneous linea equations are solved by Gauss-elimination method. In the proposed algorithm, critical points of the curvature are obtained on each direction to compensate the segmentation parameters, which can be ignored in only one-directional histogram.

• Journal of Biomedical Engineering Research
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• v.22 no.3
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• pp.231-239
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• 2001

This paper proposes a region segmenting method for the Pap-smear image. The proposed method uses a pixel classifier based on neural network, which consists of four stages : preprocessing, feature extraction, region segmentation and postprocessing. In the preprocessing stage, brightness value is normalized by histogram stretching. In the feature extraction stage, total 36 features are extracted from $3{\times}3$ or $5{\times}5$ window. In the region segmentation stage, each pixel which is associated with 36 features, is classified into 3 groups : nucleus, cytoplasm and background. The backpropagation network is used for classification. In the postprocessing stage, the pixel, which have been rejected by the above classifier, are re-classified by the relaxation algorithm. It has been shown experimentally that the proposed method finds the nucleus region accurately and it can find the cytoplasm region too.