• Journal of Broadcast Engineering
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• v.27 no.5
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• pp.619-628
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• 2022

Although face recognition and face generation have been growing in popularity, the privacy issues of using facial images in the wild have been a concurrent topic. In this paper, we propose a face editing network that can reduce privacy issues by generating face images with various properties from a small number of real face images and facial mask information. Unlike the existing methods of learning face attributes using a lot of real face images, the proposed method generates new facial images using a facial segmentation mask and texture images from five parts as styles. The images are then trained with our network to learn the styles and locations of each reference image. Once the proposed framework is trained, we can generate various face images using only a small number of real face images and segmentation information. In our extensive experiments, we show that the proposed method can not only generate new faces, but also localize facial attribute editing, despite using very few real face images.

• Journal of Advanced Navigation Technology
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• v.18 no.2
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• pp.151-157
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• 2014

Recently UAV(unmanned aerial vehicle) is frequently used not only for military purpose but also for civil purpose. UAV automatically navigates following the coordinates input in advance using GPS information. However it is impossible when GPS cannot be received because of jamming or external interference. In order to solve this problem, we propose a real-time segmentation and classification algorithm for the specific regions from UAV image in this paper. We use the super-pixels algorithm using graph-based image segmentation as a pre-processing stage for the feature extraction. We choose the most ideal model by analyzing various color models and mixture color models. Also, we use support vector machine for classification, which is one of the machine learning algorithms and can use small quantity of training data. 18 color and texture feature vectors are extracted from the UAV image, then 3 classes of regions; river, vinyl house, rice filed are classified in real-time through training and prediction processes.

• Journal of Korea Multimedia Society
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• v.12 no.2
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• pp.272-280
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• 2009

The recent mobile industry is providing of a lot of image on/off-line contents are being converted into the mobile contents for architectural design. However, it is difficult to provide users with the existing on/off-line contents without any considerations due to the small size of the mobile screen. In existing methods to overcome the problem, the comic contents on mobile devices are manually produced by computer software such as Photoshop. In this paper, I describe the Automatic Comics Conversion(ACC) system that provides the variedly form of offline comic contents into mobile device of the small screen using Multi-Layer Perceptorn(MLP). ACC produces an experience together with the comic contents fitting for the small screen, which introduces a clustering method that is useful for variety types of comic images and characters as a prerequisite as a stage for preserving semantic meaning. An application is to use the frame form of pictures, website and images in order into mobile device the availability and can bounce back the freeze images contents into dynamic images content.

• Journal of the Korea Society of Computer and Information
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• v.17 no.12
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• pp.101-109
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• 2012

In many diagnostic and treatment procedures for prostate disease accurate detection of prostate boundaries in transrectal ultrasound(TRUS) images is required. This is a challenging and difficult task due to weak prostate boundaries, speckle noise and the short range of gray levels. In this paper a method for automatic prostate segmentation inTRUS images using support vectors and snake-like contour is presented. This method involves preprocessing, extracting Gabor feature, training, and prostate segmentation. Gabor filter bank for extracting the texture features has been implemented. A support vector machine(SVM) for training step has been used to get each feature of prostate and nonprostate. The boundary of prostate is extracted by the snake-like contour algorithm. The results showed that this new algorithm extracted the prostate boundary with less than 9.3% relative to boundary provided manually by experts.

• The Transactions of the Korea Information Processing Society
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• v.7 no.11
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• pp.3333-3340
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• 2000

Recently, Requnremeni for video data have been increased rapidly by high progress of both hardware and cornpression technique. Because digital video data are unformed and mass capacity, it needs various retrieval techniquesjust as contednt-based rehieval Superimposed-caption ina digital video can help us to analyze the video story easier and be used as indexing information for many retrieval techniques In this research we propose a new method that segments the caption as analyzing texture eature of caption regions in each video frame, and that extracts the accurate scope of superimposed-caption frame and its key regions and color by measunng cominuity of caption regions between frames

• Journal of Digital Contents Society
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• v.17 no.6
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• pp.603-611
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• 2016

Generally, the doctors manually delineated the prostate boundary seeing the image by their eyes, but the manual method not only needed quite much time but also had different boundaries depending on doctors. To reduce the effort like them the automatic delineating methods are needed, but detecting the boundary is hard to do since there are lots of uncertain textures or speckle noises. There have been studied in SVM, SIFT, Gabor texture filter, snake-like contour, and average-shape model methods. Besides, there were lots of studies about 2 and 3 dimension images and CT and MRI. But no studies have been developed superior to human experts and they need additional studies. For this, this paper proposes a method that delineates the boundary predicting its texture features and its average distribution on the prostate image. As result, we got the similar boundary as the method of human experts.

• Journal of Broadcast Engineering
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• v.15 no.1
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• pp.3-13
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• 2010

In this paper, we propose a method for filling out the occlusions in generating multi-view images from one source image and its ground-truth depth image. The method is based on image inpainting and layered interpolation. The source image is first divided into several layers using depth information. The occlusions are interpolated separately in every layered image using the image inpainting algorithm. Finally, the interpolated layered images are combined to obtain different viewpoint images. Interpolating occlusions with depth-correlated texture information that is contained to each layer makes it possible to obtain more detailed and accurate results than previous methods. The effectiveness of the proposed method is shown through experimental results.

• The KIPS Transactions:PartB
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• v.16B no.1
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• pp.7-16
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• 2009

Some clustering algorithms have a problem that an image is over-segmented since both the spatial information between the segmented regions is not considered and the number of the clusters is defined in advance. Therefore, they are difficult to be applied to the applicable fields. This paper proposes the new post-processing methods, a reclassification of the inhomogeneous clusters and a region merging using Baysian algorithm, that improve the segmentation results of the clustering algorithms. The inhomogeneous cluster is firstly selected based on variance and between-class distance and it is then reclassified into the other clusters in the reclassification step. This reclassification is repeated until the optimal number determined by the minimum average within-class distance. And the similar regions are merged using Baysian algorithm based on Kullbeck-Leibler distance between the adjacent regions. So we can effectively solve the over-segmentation problem and the result can be applied to the applicable fields. Finally, we design a classification system for the segmented regions to validate the proposed method. The segmented regions are classified by SVM(Support Vector Machine) using the principal colors and the texture information of the segmented regions. In experiment, the proposed method showed the validity for various real-images and was effectively applied to the designed classification system.

• Korean Journal of Food Science and Technology
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• v.48 no.5
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• pp.466-473
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• 2016

This study was performed to investigate the reduction in bitter taste and quality characteristics by pretreatments (brining; 1, 5% and blanching; 1, 3 min) in pickled bitter melon, respectively. We prepared picked bitter melon samples at 1%-1 min, 1%-3 min, 5%-1 min, 5%-3 min. Total polyphenol and total flavonoid contents were found to be the highest in 5%-1 min at $14.23{\pm}0.40mg\;CE/g$ (dry) and $4.46{\pm}0.10mg\;RE/g$ (dry), respectively. L-ascorbic acid level was the highest in control samples. Arginine and glutamic acid were increased by brining and blanching. ABTS and DPPH radical scavenging activity were found to be the highest at $43.60{\pm}0.40$ and $44.88{\pm}0.20%$ at 5%-1 min, respectively. ${\alpha}-glucosidase$ inhibitory activity was the highest at 5%-1 min. The a value was statistically different, whereas L and b values were similar among different pretreatments. Hardness in pretreated samples was decreased as compared to that in the control. Among sensory evaluations, 'color' did not indicate any statistical difference, while 'texture', 'bitterness preference' and 'overall preference' increased with pretreatments, and 'bitter intensity' decreased.

• Korean Journal of Food Science and Technology
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• v.48 no.4
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• pp.366-371
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• 2016

This study was carried out to evaluate the feasibility of using domestic wheat flour as ingredient of pizza dough by examining the physicochemical and sensory properties of the dough. Five pizza dough samples were prepared with different ratio of domestic and imported wheat flour. Significant differences were observed in crude protein content, crude ash content, and color of the pizza dough samples; however, no significant difference was found in mechanically measured rheology. A sensory evaluation based on 23 descriptive attributes indicated significant differences between pizza dough made from domestic wheat flour and that made from imported wheat flour for springiness and softness of the surface, roughness, and chewiness (p<0.05). Principal component analysis showed that the greater amount of Korean wheat flour in the dough resulted in different values from negative to positive ends of principal component 1 (37.4%).