• The KIPS Transactions:PartB
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• v.10B no.5
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• pp.527-534
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• 2003

An active contour model called snake is powerful tool for object contour extraction. But, conventional snakes require exhaustive computing time, sometimes can´t extract complex shape contours due to the properties of energy function, and are also heavily dependent on the position and the shape of an initial snake. To solving these problems, we propose in this paper an improved snake called "shaking snake", based on a greedy algorithm. A shaking snake consist of two steps. According to their appropriateness, we in the first step move each points directly to locations where contours are likely to be located. In the second step, we then align some snake points with a tolerable bound in order to prevent local minima. These processes shake the proposed snake. In the experimental results, we show the process of shaking the proposed shake and comparable performance with a greedy snake. The proposed snake can extract complex shape contours very accurately and run fast, approximately by the factor of five times, than a greedy snake.

• Journal of Korea Multimedia Society
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• v.25 no.5
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• pp.685-694
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• 2022

The goal of few-shot semantic segmentation is to build a network that quickly adapts to novel classes with extreme data shortage regimes. Most existing few-shot segmentation methods leverage single or multiple prototypes from extracted support features. Although there have been promising results for natural images, these methods are not directly applicable to the aerial image domain. A key factor in few-shot segmentation on aerial images is to effectively exploit information that is robust against extreme changes in background and object scales. In this paper, we propose a Mask-Guided Attention module to extract more comprehensive support features for few-shot segmentation in aerial images. Taking advantage of the support ground-truth masks, the area correlated to the foreground object is highlighted and enables the support encoder to extract comprehensive support features with contextual information. To facilitate reproducible studies of the task of few-shot semantic segmentation in aerial images, we further present the few-shot segmentation benchmark iSAID-, which is constructed from a large-scale iSAID dataset. Extensive experimental results including comparisons with the state-of-the-art methods and ablation studies demonstrate the effectiveness of the proposed method.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.4
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• pp.1-11
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• 2022

Recently, research on license plate recognition, which is a core technology of an intelligent transportation system(ITS), is being actively conducted. In this paper, we propose a method to extract numbers and characters from low-quality license plate images by applying the YOLOv4 algorithm. YOLOv4 is a one-stage object detection method using convolution neural network including BACKBONE, NECK, and HEAD parts. It is a method of detecting objects in real time rather than the previous two-stage object detection method such as the faster R-CNN. In this paper, we studied a method to directly extract number and character regions from low-quality license plate images without additional edge detection and image segmentation processes. In order to evaluate the performance of the proposed method we experimented with 500 license plate images. In this experiment, 350 images were used for training and the remaining 150 images were used for the testing process. Computer simulations show that the mean average precision of detecting number and character regions on vehicle license plates was about 93.8%.

• Laboraroty Animal Research
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• v.34 no.4
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• pp.239-247
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• 2018

Bacopa monnieri is a medicinal plant with a long history of use in Ayurveda, especially in the treatment of poor memory and cognitive deficits. In the present study, we hypothesized that Bacopa monnieri extract (BME) can improve memory via increased cell proliferation and neuroblast differentiation in the dentate gyrus. BME was administered to 7-week-old mice once a day for 4 weeks and a novel object recognition memory test was performed. Thereafter, the mice were euthanized followed by immunohistochemistry analysis for Ki67, doublecortin (DCX), and phosphorylated cAMP response element-binding protein (CREB), and western blot analysis of brain-derived neurotrophic factor (BDNF). BME-treated mice showed moderate increases in the exploration of new objects when compared with that of familiar objects, leading to a significant higher discrimination index compared with vehicle-treated mice. Ki67 and DCX immunohistochemistry showed a facilitation of cell proliferation and neuroblast differentiation following the administration of BME in the dentate gyrus. In addition, administration of BME significantly elevated the BDNF protein expression in the hippocampal dentate gyrus, and increased CREB phosphorylation in the dentate gyrus. These data suggest that BME improves novel object recognition by increasing the cell proliferation and neuroblast differentiation in the dentate gyrus, and this may be closely related to elevated levels of BDNF and CREB phosphorylation in the dentate gyrus.

• Journal of the Korea Computer Graphics Society
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• v.20 no.3
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• pp.1-8
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• 2014

We propose a method to compose a foreground object into a background image, where the foreground object is a part (or a region) of an image taken by a front-facing camera and the background image is a whole image taken by a back-facing camera in a smart phone at the same time. Recent high-end cell-phones have two cameras and provide users with preview video before taking photos. We extract the foreground object that is moving along with the front-facing camera using the optical flow during the preview. We compose the extracted foreground object into a background image using a simple image composition technique. For better-looking result in the composed image, we apply a border smoothing technique using a weighted-border mask to blend transparency from background to foreground. Since constructing and grouping pixel-level dense optical flow are quite slow even in high-end cell-phones, we compute a mask to extract the foreground object in low-resolution image, which reduces the computational cost greatly. Experimental result shows the effectiveness of our extraction and composition techniques, with much less computational time in extracting the foreground object and better composition quality compared with Poisson image editing technique which is widely used in image composition. The proposed method can improve limitedly the color bleeding artifacts observed in Poisson image editing using weighted-border blending.

• The KIPS Transactions:PartD
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• v.14D no.1 s.111
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• pp.67-82
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• 2007

Due to the classes in object-orientation, which are too detailed and specific, their reusability can be decreased. Components, considered to be more coarse-grained compared to objects, help maintain software complexity effectively and facilitate software reuse. Furthermore, component technology becomes essential by the appearance of the new frameworks, such as MDA, SOA, etc. Consequently, it is necessary to reengineer an existing object-oriented system into a component-based system suitable to those new environments. In this paper, we propose a model-based quantitative reengineering methodology to identify components from object-oriented systems. We expand system model and process, which are defined in our prior work, more formally and precisely. A system model, constructed from object-oriented system, is used to extract and refine components in quantitative ways. We develop a supporting tool and show effectiveness of the methodology through applying it to an existing object-oriented system.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.6
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• pp.712-717
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• 2003

A novel multi-snake is proposed for efficient extraction of multi-object contours from a natural scene. An NTGST(noise-tolerant generalized symmetry transform) is used as a context-free attention operator to detect and locate multiple objects from a complex background and then the snake points are automatically initialized nearby the contour of each detected object using symmetry map of the NTGST before multiple snakes are introduced. These procedures solve the knotty subjects of automatic snake initialization and simultaneous extraction of multi-object contours in conventional snake algorithms. Because the snake points are initialized nearby the actual contour of each object, as close as possible, contours with high convexity and/or concavity can be easily extracted. The experimental results show that the proposed method can efficiently extract multi-object contours from a noisy and complex background of natural scenes.

• Korean Journal of Applied Biomechanics
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• v.17 no.1
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• pp.81-90
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• 2007

The effort identifying positioning information of the moving object in real time has been a issue not only in sport biomechanics but also other academic areas. In order to solve this issue, this study tried to track the movement of a pitched ball that might provide an easier prediction because of a clear focus and simple movement of the object. Machine learning has been leading the research of extracting information from continuous images such as object tracking. Though the rule-based methods in artificial intelligence prevailed for decades, it has evolved into the methods of statistical approach that finds the maximum a posterior location in the image. The development of machine learning, accompanied by the development of recording technology and computational power of computer, made it possible to extract the trajectory of pitched baseball from recorded images. We present a method of baseball tracking, based on object tracking methods in machine learning. We introduce three state-of-the-art researches regarding the object tracking and show how we can combine these researches to yield a novel engine that finds trajectory from continuous pitching images. The first research is about mean shift method which finds the mode of a supposed continuous distribution from a set of data. The second research is about the research that explains how we can find the mode and object region effectively when we are given the previous image's location of object and the region. The third is about the research of representing data into features that we can deal with. From those features, we can establish a distribution to generate a set of data for mean shift. In this paper, we combine three works to track baseball's location in the continuous image frames. From the information of locations from two sets of images, we can reconstruct the real 3-D trajectory of pitched ball. We show how this works in real pitching images.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.3
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• pp.670-678
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• 2014

This paper presents a method to extract objects in radiographic images where all the allowable combinations of segmented regions are compared to a target object using Fourier descriptor. In the object extraction for usual images, a main problem is occlusion. In radiographic images, there is an advantage that the shape of an object is not occluded by other objects. It is because radiographic images represent the amount of radiation penetrated through objects. Considering the property of no occlusion in radiographic images, the shape based descriptors can be very effective to find objects. After all, the proposed object extraction method consists of three steps of segmenting regions, finding all the combinations of the segmented regions, and matching the combinations to the shape of the target object. In finding the combinations, we reduce a lot of computations to remove unnecessary combinations before matching. In matching, we employ Fourier descriptor so that the proposed method is rotation and shift invariant. Additionally, shape normalization is adopted to be scale invariant. By experiments, we verify that the proposed method works well in extracting objects.

• Journal of KIISE:Software and Applications
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• v.27 no.6
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• pp.595-606
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• 2000

Software metrics evaluate the development process, measure the software development effort, and control the software quality effectively. Moreover in a current status to emphasize reusability, it is necessary to study of cohesion and coupling that plays an important role in evaluating reusability. Object oriented methodology to use the concept like encapsulation, inheritance, and polymorphism demands metrics that are different from existing procedural methodology, so a study for object oriented metrics is in progress at the present time. In this paper, we propose cohesion and coupling metrics for object oriented program, evaluate the proposed metrics by using the complexity properties proposed by Weyuker and Briand, and extract cohesion and coupling from C++ code.