• The Journal of Korea Robotics Society
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• v.14 no.2
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• pp.139-149
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• 2019

In this paper, we present auto-annotation tool and synthetic dataset using 3D CAD model for deep learning based object detection. To be used as training data for deep learning methods, class, segmentation, bounding-box, contour, and pose annotations of the object are needed. We propose an automated annotation tool and synthetic image generation. Our resulting synthetic dataset reflects occlusion between objects and applicable for both underwater and in-air environments. To verify our synthetic dataset, we use MASK R-CNN as a state-of-the-art method among object detection model using deep learning. For experiment, we make the experimental environment reflecting the actual underwater environment. We show that object detection model trained via our dataset show significantly accurate results and robustness for the underwater environment. Lastly, we verify that our synthetic dataset is suitable for deep learning model for the underwater environments.

• Korean Journal of Cognitive Science
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• v.27 no.3
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• pp.355-376
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• 2016

We used holes defined by color similarity (Experiment 1) and binocular disparity (Experiment 2) to study how the inner contour of an object (i.e., boundary of a hole in it) is encoded in visual working memory. Many studies in VWM have shown that an object's boundary properties can be integrated with its surface properties via their shared spatial location, yielding an object-based encoding benefit. However, encoding of the hole contours has rarely been tested. We presented objects (squares or circles) containing a bar under a change detection paradigm, and relevant features to be remembered were the color of objects and the orientation of bars (or holes). If the contour of a hole belongs to the surrounding object rather than to the hole itself, the object-based feature binding hypothesis predicts that the shape of it can be integrated with color of an outer object, via their shared spatial location. Thus, in the hole display, change detection performance was expected to better than in the conjunction display where orientation and color features to be remembered were assigned to different parts of a conjunction object, and comparable to that in a single bar display where both orientation and color were assigned into a single bar. However, the results revealed that performance in the hole display did not differ from that in the conjunction display. This suggests that the shape of holes is not automatically encoded together with the surface properties of the outer object via object-based feature binding, but encoded independently from the surrounding object.

• Journal of Broadcast Engineering
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• v.17 no.4
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• pp.611-624
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• 2012

'Gesture' is the most intuitive means of communication except the voice. Therefore, there are many researches for method that controls computer using gesture input to replace the keyboard or mouse. In these researches, the method of user detection and main body parts estimation is one of the very important process. in this paper, we propose user objects detection and main body parts estimation method on inaccurate depth information for pose estimation. we present user detection method using 2D and 3D depth information, so this method robust to changes in lighting and noise and 2D signal processing 1D signals, so mainly suitable for real-time and using the previous object information, so more accurate and robust. Also, we present main body parts estimation method using 2D contour information, 3D depth information, and tracking. The result of an experiment, proposed user detection method is more robust than only using 2D information method and exactly detect object on inaccurate depth information. Also, proposed main body parts estimation method overcome the disadvantage that can't detect main body parts in occlusion area only using 2D contour information and sensitive to changes in illumination or environment using color information.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.325-332
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• 2003

This study was to develop a system for grading apples by their color using statistical image processing. T-test was used to detect edges in apple images and the chain code method was used for contour coding. The histogram and mean gray level of each RGB channel in a ring-shaped region was used to compare apple colors to reference apple color.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.672-675
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• 2000

본 논문에서는 차량의 뒷바퀴 윤곽을 이용하여 근거리 전방차량을 인식하는 방법에 대해 설명한다. 차량 앞범퍼 중앙에 장착된 흑백 CCD 카메라에서 입력되는 영상을 분석하면 전방차량의 뒷바퀴 바깥쪽 윤곽이 수직성분을 띄며 도로면과 명확하게 구분됨을 알 수 있다. 이 특징을 이용하여 일정거리 이내에 존재하는 전방차량을 탐지할 수 있다 또한 탐지된 차량까지의 대략적인 거리를 계산할 수 있고, 브레이크등의 점등 여부를 판단할 수 있다. 이 방법은 차량간 간격이 좁고 저속으로 주행하는 도심지 도로에서 가다서다를 반복하는 Stop-and-Go 차량을 구현하는데 적용될 수 있을 것이다.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.7
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• pp.578-589
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• 1991

In this paper, there is proposed a novel concept of Incremintal Circle Transform which can describe the boundary contour of a two-dimensional object without object without occlusions. And a pattern recognition algorithm to determine the posture of an object is developed with the aid of line integral and similarity transform. Also, It is confirmed via experiments that the algorithm can find the posture of an object in a very fast manner independent of the starting point for boundary coding and the position of the object.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.472-474
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• 2002

본 논문에서는 복잡한 영상에서 얼굴 영역의 윤곽선을 검출하는 방법을 제안하였다. 이를 위하여 얼굴의 칼라 정보에 기반한 액티브 컨투어 모델을 이용하였다. 얼굴의 칼라 정보는 색채칼라 공간(chromatic color space)에서 2D-Gaussian모델로 나타내어지는 스킨 칼로 모델로 표현 되었다. 실험결과 제안된 방법은 복잡한 영상뿐 아니라 잡음이 많은 영상에서 하나 또는 여러 개의 얼굴 영역을 추출할 수 있었다.

• Journal of the Semiconductor & Display Technology
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• v.15 no.2
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• pp.61-65
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• 2016

Facial feature detection is a fundamental function in the field of computer vision such as security, bio-metrics, 3D modeling, and face recognition. There are many algorithms for the function, active shape model is one of the most popular local texture models. This paper addresses issues related to face detection, and implements an efficient extraction algorithm for extracting the facial feature points to use on iOS platform. In this paper, we extend the original ASM algorithm to improve its performance by four modifications. First, to detect a face and to initialize the shape model, we apply a face detection API provided from iOS CoreImage framework. Second, we construct a weighted local structure model for landmarks to utilize the edge points of the face contour. Third, we build a modified model definition and fitting more landmarks than the classical ASM. And last, we extend and build two-dimensional profile model for detecting faces within input images. The proposed algorithm is evaluated on experimental test set containing over 500 face images, and found to successfully extract facial feature points, clearly outperforming the original ASM.

• Journal of the Korea Society of Computer and Information
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• v.8 no.2
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• pp.31-36
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• 2003

This paper proposes an acceleration method of PCA(Principal Component Analysis) based feature detection. The feature detection method makes decision whether the target feature is included in a given image, and if included, calculates the position and extent of the target feature. The position and scale of the target feature or face is not known previously, all the possible locations should be tested for various scales to detect the target. This is a search Problem in huge search space. This Paper proposes a fast face and feature detection method by reducing the search space using the multi-stage prediction map and contour Prediction map. A Proposed method compared to the existing whole search way, and it was able to reduce a computational complexity below 10％ by experiment.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.11
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• pp.1445-1454
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• 1995

In this study, we have developed a prototype of the automatic defects detection system for capsule inspection using the computer vision techniques. The subjects for inspection are empty hard capsules of various sizes which are made of gelatine. To inspect both sides of a capsule, 2-stage recognition is performed. Features we have used are various lengths of a capsule, area, linearity, symmetricity, head curvature and so on. Decision making is performed based on average value which is computed from 20 good capsules in training and permission bounds in factories. Most of time-consuming process for feature extraction is computed by hardware to meet the inspection speed of more than 20 capsules/sec. The main logic for control and arithmetic computation is implemented using EPLD for the sake of easy change of design and reduction in time for developement. As a result of experiment, defects on size or contour of binary images are detected over 95%. Because of dead zone in imaging system, detection ratio of defects on surface, such as bad joint, chip, speck, etc, is lower than the former case. In this case, detection ratio is 50-85%. Defects such as collet pinch and mashed cap/body seldom appear in binary image, and detection ratio is very low. So we have to process the gray-level image directly in partial region.