• Proceedings of the IEEK Conference
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• 대한전자공학회 2002년도 ITC-CSCC -2
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• pp.1256-1259
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• 2002

In this paper, we have proposed license plate recognition system for multi-national vehicle license plate using geometric features along with hybrid and seven segment pattern vectors. In the proposed system, we suggested to find horizontal and vertical relation after going through preparation process with inputted real-time license plate image of Korea and Japan, and then to classify license plate with using characteristic and geometric information of license plates. It classifies the extracted license plate images into letters and numbers, such as local name, local number, classification character and license consecutive numbers, and recognize license plate of Korea and Japan by applying hybrid and seven segments pattern vectors to classified letter and number region. License plate extraction step of the proposed system uses width and length information along with relative rate of Korean and Japanese license plate. Moreover, it exactly segmentation by letters with using each letter and number position information within license plate region, and recognizes Korean and Japanese license plates by applying hybrid and seven segment pattern vectors, containing characteristics related to letter size and movement within segmented letter area. As the result of testing the proposed system in real experiment, it recognized regardless of external lighting conditions as well as classifying license plates by nations, Korea and Japan. We have developed a system, recognizing regardless of inputted structural character of vehicle licenses and external environment.

• Journal of the Institute of Electronics Engineers of Korea TE
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• 제39권4호
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• pp.379-388
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• 2002

In this paper, the real-time algorithm is proposed for automatic detection of the face and facial features. In the face region, we extracted eyes, nose, mouth and so forth. There are two methods to extract them; one is the method of using the location information of them, other is the method of using Gaussian second derivatives filters. This system have high speed and accuracy because the facial feature extraction is processed only by detected face region, not by whole image. There are some kinds of good experimental result for the proposed algorithm; high face detection rate of 95%, high speed of lower than 1sec. the reduction of illumination effect, and the compensation of face tilt.

• Journal of Broadcast Engineering
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• 제18권4호
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• pp.535-542
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• 2013

In this paper, we proposed a advanced hardware engine architecture for high speed and high detection rate image recognitions. We adopted the HOG-LBP feature extraction algorithm and more parallelized architecture in order to achieve higher detection rate and high throughput. As a simulation result, the designed engine which can search about 90 frames per second detects 97.7% of pedestrians when false positive per window is $10^{-4}$.

• Current Optics and Photonics
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• 제1권6호
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• pp.587-597
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• 2017

The extraction of the valid measurement region from the interference fringe pattern is a significant step when measuring gear tooth flank form deviation with grazing incidence interferometry, which will affect the measurement accuracy. In order to overcome the drawback of the conventionally used method in which the object image pattern must be captured, an improved segmentation approach is proposed in this paper. The interference fringe patterns feature, which is smoothed by the nonlinear diffusion, would be extracted by the structure tensor first. And then they are incorporated into the vector-valued Chan-Vese model to extract the valid measurement region. This method is verified in a variety of interference fringe patterns, and the segmentation results show its feasibility and accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권3호
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• pp.1390-1403
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• 2016

Facial expression recognition (FER) plays a very significant role in computer vision, pattern recognition, and image processing applications such as human computer interaction as it provides sufficient information about emotions of people. For video-based facial expression recognition, depth cameras can be better candidates over RGB cameras as a person's face cannot be easily recognized from distance-based depth videos hence depth cameras also resolve some privacy issues that can arise using RGB faces. A good FER system is very much reliant on the extraction of robust features as well as recognition engine. In this work, an efficient novel approach is proposed to recognize some facial expressions from time-sequential depth videos. First of all, efficient Local Binary Pattern (LBP) features are obtained from the time-sequential depth faces that are further classified by Generalized Discriminant Analysis (GDA) to make the features more robust and finally, the LBP-GDA features are fed into Hidden Markov Models (HMMs) to train and recognize different facial expressions successfully. The depth information-based proposed facial expression recognition approach is compared to the conventional approaches such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Linear Discriminant Analysis (LDA) where the proposed one outperforms others by obtaining better recognition rates.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권3호
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• pp.1189-1204
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• 2018

Detecting and capturing 3D human structures from the intensity-based image sequences is an inherently arguable problem, which attracted attention of several researchers especially in real-time activity recognition (Real-AR). These Real-AR systems have been significantly enhanced by using depth intensity sensors that gives maximum information, in spite of the fact that conventional Real-AR systems are using RGB video sensors. This study proposed a depth-based routine-logging Real-AR system to identify the daily human activity routines and to make these surroundings an intelligent living space. Our real-time routine-logging Real-AR system is categorized into two categories. The data collection with the use of a depth camera, feature extraction based on joint information and training/recognition of each activity. In-addition, the recognition mechanism locates, and pinpoints the learned activities and induces routine-logs. The evaluation applied on the depth datasets (self-annotated and MSRAction3D datasets) demonstrated that proposed system can achieve better recognition rates and robust as compare to state-of-the-art methods. Our Real-AR should be feasibly accessible and permanently used in behavior monitoring applications, humanoid-robot systems and e-medical therapy systems.

• The Journal of the Korea institute of electronic communication sciences
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• 제11권7호
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• pp.685-692
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• 2016

In this paper, we not only take advantage of the SIFT features in banknote recognition, which has robustness to illumination changes, geometric rotation as well as scale changes, but also propose the hierarchical banknote recognition algorithm, which comprised of feature vector extraction from the frame grabbed image of the banknotes, and matching to the prepared data base of multinational banknotes by ANN algorithm. The images of banknote under the developed UV, IR and white illumination are used so as to extract the SIFT features peculiar to each banknotes. These SIFT features are used in recognition of the nationality as well as face value. We confirmed successful function of the proposed algorithm by applying the proposed algorithm to the banknotes of Korean and USD as well as EURO.

• Journal of the Korea Computer Industry Society
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• 제3권7호
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• pp.901-914
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• 2002

In this paper, We propose an efficient automatic caption detection and location method, caption recognition using FE-MCBP(Feature Extraction based Multichained BackPropagation) neural network for content based retrieval of video. Frames are selected at fixed time interval from video and key frames are selected by gray scale histogram method. for each key frames, segmentation is performed and caption lines are detected using line scan method. lastly each characters are separated. This research improves speed and efficiency by color segmentation using local maximum analysis method before line scanning. Caption detection is a first stage of multimedia database organization and detected captions are used as input of text recognition system. Recognized captions can be searched by content based retrieval method.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 한국방송공학회 2009년도 IWAIT
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• pp.794-798
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• 2009

We introduce an implementation of plug-ins on PLUTO. These plug-ins discriminate inflammatory nodules from other types of nodules in chest X-ray CT images. The PLUTO is a common platform for computer-aided diagnosis systems on Microsoft Windows series and it is easy to add new functions as plug-ins. We coded two plug-ins. One of the them calculates features based on medical knowledge. The other plug-in calculates parameters to classify the type of nodules, and it also classifies nodules into inflammatory nodules and others using SVM. These plug-ins are coded using MIST library which is produced at Nagoya University, Japan. In our previous study, the MIST library was parallelized, so that we can utilize a number of CPUs to calculate features and SVM learning/classifying depending on the amount of computation. Using these plug-ins, it became easy to extract features to discriminate inflammatory nodules from other types of nodules and to change parameters for feature extraction and SVM learning/classifying with GUI interface. The accuracy of the classifying result is 100% with 78 solid nodules which contains 43 inflammatory nodules and 35 other type of nodules.

• Proceedings of the Korean Information Science Society Conference
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• 한국정보과학회 1998년도 가을 학술발표논문집 Vol.25 No.2 (2)
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• pp.429-431
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• 1998

본 논문에서는 의료 영상 중에서 성대 운동의 불규칙적인 움직임을 판단하여 자동으로 진단 파라미터를 구하는 비디오스트로보키모그래피(Videostrobokymography) 시스템에서 관심 영역을 추출하는 방법을 소개하고자 한다. CCD카메라에 의해 촬영된 영상은 비디오 테이프에 저장된 후 이미지 캡쳐 보드에서 그레이 이미지(gray-level)로 변환되어 저장된다. 입력된 영상은 움직이는 영상을 촬영한 것이므로 관심 영역의 위치가 각 프레임마다 다르다. 또한 실제로 입력된 성대영상들이 점진적인 농도 변화를 보이기 때문에 에지에 의해 영역을 추출하는 일반적인 영역 추출방법은 사용하기 어렵다. 본 논문에서는 두 번의 단계를 통하여 관심 영역을 추출하고 있다. 첫 번째는 입력된 영상에서 노이즈를 제거한 후 각 프레임에서 영상의 최소 에너지를 구한다. 두 번째로 농도 변화 값을 특징 값으로 이용하는 분할-합병 알고리즘(Split-merge Algorithm)을 적용하여 관심 영역을 추출하였다. 제안한 알고리즘을 19명의 성대 영상에 적용하여 분석한 결과 성대의 관심 영역을 추출할 수 있었다. 그리고, 영상의 에너지 값을 이용하는 스네이크 알고리즘(Snake Algorithm)에 적용하여 비교해본 결과 본 연구에서 제안하는 스네이크 알고리즘보다 좋은 성능을 보임을 확인할 수 있었다. 본 연구에서 제안하는 관심 영역 추출 방법은 동적인 변화를 보이는 영상에서 관심 영역을 추출할 수 있을 뿐 아니라 계산 량이 적어 200x280크기의 이미지를 초당 약 40프레임에 대한 관심 영역을 추출할 수 있는 장점이 있다.