• Journal of information and communication convergence engineering
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• v.20 no.3
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• pp.153-159
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• 2022

Plain radiographic analysis is the initial imaging modality for suspected small bowel obstruction. Among the many features that affect the diagnosis of small bowel obstruction (SBO), the presence of gas-filled or fluid-filled small bowel loops is the most salient feature that can be automatized by computer vision algorithms. In this study, we compare three frequently applied pixel-clustering algorithms for extracting gas-filled areas without human intervention. In a comparison involving 40 suspected SBO cases, the Possibilistic C-Means and Fuzzy C-Means algorithms exhibited initialization-sensitivity problems and difficulties coping with low intensity contrast, achieving low 72.5% and 85% success rates in extraction. The Adaptive Resonance Theory 2 algorithm is the most suitable algorithm for gas-filled region detection, achieving a 100% success rate on 40 tested images, largely owing to its dynamic control of the number of clusters.

• Journal of the Korea Convergence Society
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• v.11 no.12
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• pp.39-47
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• 2020

In this paper, we proposed a deep learning-based detection method using Saliency Attention to detect salient objects in images. The salient object detection separates the object where the human eye is focused from the background, and determines the highly relevant part of the image. It is usefully used in various fields such as object tracking, detection, and recognition. Existing deep learning-based methods are mostly Autoencoder structures, and many feature losses occur in encoders that compress and extract features and decoders that decompress and extend the extracted features. These losses cause the salient object area to be lost or detect the background as an object. In the proposed method, Saliency Attention is proposed to reduce the feature loss and suppress the background region in the Autoencoder structure. The influence of the feature values was determined using the ELU activation function, and Attention was performed on the feature values in the normalized negative and positive regions, respectively. Through this Attention method, the background area was suppressed and the projected object area was emphasized. Experimental results showed improved detection results compared to existing deep learning methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1159-1181
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• 2016

This study introduces an image-based 2D-to-3D conversion system that provides significant stereoscopic visual effects for humans. The linear and atmospheric perspective cues that compensate each other are employed to estimate depth information. Rather than retrieving a precise depth value for pixels from the depth cues, a direction angle of the image is estimated and then the depth gradient, in accordance with the direction angle, is integrated with superpixels to obtain the depth map. However, stereoscopic effects of synthesized views obtained from this depth map are limited and dissatisfy viewers. To obtain impressive visual effects, the viewer's main focus is considered, and thus salient object detection is performed to explore the significance region for visual attention. Then, the depth map is refined by locally modifying the depth values within the significance region. The refinement process not only maintains global depth consistency by correcting non-uniform depth values but also enhances the visual stereoscopic effect. Experimental results show that in subjective evaluation, the subjectively evaluated degree of satisfaction with the proposed method is approximately 7% greater than both existing commercial conversion software and state-of-the-art approach.

• The KIPS Transactions:PartB
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• v.17B no.3
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• pp.239-248
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• 2010

Leukocyte in blood smear image provides significant information to doctors for diagnosis of patient health status. Therefore, it is necessary step to separate leukocyte from blood smear image among various blood cells for early disease prediction. In this paper, we present a saliency map and stepwise region merging based leukocyte segmentation method. Since leukocyte region has salient color and texture, we create a saliency map using these feature map. Saliency map is used for sub-image separation. Then, clustering is performed on each sub-image using mean-shift. After mean-shift is applied, stepwise region-merging is applied to particle clusters to obtain final leukocyte nucleus. The experimental results show that our system can indeed improve segmentation performance compared to previous researches with average accuracy rate of 71%.

• International Journal of Contents
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• v.7 no.4
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• pp.24-29
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• 2011

In this paper, we propose a method to segment the bottle label in images taken by mobile phones using multi-gradient approaches. In order to segment the label region of interest-object, the saliency map method and Hough Transformation method are first applied to the original images to obtain the candidate region. The saliency map is used to detect the most salient area based on three kinds of features (color, orientation and illumination features). The Hough Transformation is a technique to isolated features of a particular shape within an image. Therefore, we utilize it to find the left and right border of the bottle. Next, we segment the label based on the gradient information obtained from the structure tensor method and edge method. The experimental results have shown that the proposed method is able to accurately segment the labels as the first step of product label recognition system.

• Journal of the Korean Society of Costume
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• v.56 no.8 s.108
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• pp.60-73
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• 2006

This study analyzes the Baekche official costume system, including the dress, cap and belt systems, by relating documentary records with the results of excavations. The study shows that the system differed depending on the time. A system of dress based on official ranks was instituted during the region of King Goi in the third century. The rap amd belt systems were begun in the first half of sixth century The official costume system, as recorded in the Chinese history $Sus{\u{o}}$, included many subdivisions of cap colors, which followed belt colors. According to $Gudangs{\u{o}}$, dress and cap systems changed in the seventh century. The official costume consisted of a $jacket(j{\u{o}}gori)$, trousers(baji), and coat(po). The coat had wide sleeves and reached below knees. Its collar had straight neckline. Officials of all rank wore silk caps and belts of matching colors. Officials above sixth rank used silver flower decorations on their caps. Officials wore wide-crouch trousers and generally adjusted the hems of the trousers, but this practice stopped in the sixth century. Officials wore shoes or boots depending on occasion. From a historical viewpoint, Baekche is important for having been the first Korean kingdom to establish a government organization. The salient feature of the system of organization established in A.D. 260(the twenty-seventh year of the region of King Goi) was the application of different colors to identify different ranks.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.2
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• pp.48-55
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• 2008

In this paper we propose a novel method for image retrieval system using image segmentation and salient points. The proposed method consists of four steps. In the first step, images are segmented into several regions by JSEG algorithm. In the second step, for the segmented regions, dominant colors and the corresponding color histogram are constructed. By using dominant colors and color histogram, we identify candidate regions where objects may exist. In the third step, real object regions are detected from candidate regions by SIFT matching. In the final step, we measure the similarity between the query image and DB image by using the color correlogram technique. Color correlogram is computed in the query image and object region of DB image. By experimental results, it has been shown that the proposed method detects multi-object very well and it provides better retrieval performance compared with object-based retrieval systems.

• Smart Media Journal
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• v.2 no.2
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• pp.8-13
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• 2013

Nowadays, the saliency region detection has become a popular research topic because of its uses for many applications like object recognition and object segmentation. Some of recent methods apply color distinctiveness based on an analysis of statistics of color image derivatives in order to boosting color saliency can produce the good saliency maps. However, if the salient regions comprise more than half the pixels of the image or the background is complex, it may cause bad results. In this paper, we introduce the method to handle these problems by using maximum symmetric surround. The results show that our method outperforms the previous algorithms. We also show the segmentation results by using Otsu's method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.3
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• pp.305-323
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• 2010

This paper proposes an efficient and yet powerful local descriptor called phase-space partition based descriptor (PPD). This descriptor is designed for the mobile image matching and retrieval. PPD, which is inspired from SIFT, also encodes the salient aspects of the image gradient in the neighborhood around an interest point. However, without employing SIFT's smoothed gradient orientation histogram, we apply the region based gradient statistics in phase space to the construction of a feature representation, which allows to reduce much computation requirements. The feature matching experiments demonstrate that PPD achieves favorable performance close to that of SIFT and faster building and matching. We also present results showing that the use of PPD descriptors in a mobile image retrieval application results in a comparable performance to SIFT.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.07a
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• pp.428-431
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• 2010

본 논문에서는 분별력 있는 색상 채널 선택을 통한 두드러진 영역 검출 방법을 제안한다. 제안하는 방법에서는 우선 분별력 있는 색상 채널의 선택을 위해 입력영상을 10개의 색상 채널로 변경하고, 각 채널을 NxN 블록으로 나눈다. 그리고 각 채널에서 나누어진 N 블록을 외각 블록, 중앙 블록으로 선정하고 중앙-외각 블록간의 대비와 외각 블록의 표준편차 정보를 이용하여 색상 채널 경쟁을 한다. 색상 채널 경쟁을 통해 선별된 K개의 색상 채널을 이용하여 특징맵을 만들고 이를 조합하여 두드러진 맵을 얻는다. 실험에서는 제안된 방법을 총 1000장의 자연 영상에 적용하여 성능을 평가하였으며, 83%의 평균 정확도를 보임으로써 기존 방법들보다 성능이 뛰어남을 확인하였다.