• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.10
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• pp.248-255
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• 2014

In this paper, we propose a pothole detection method in asphalt pavement using various features. Segmentation, candidate, and decision steps of pothole detection are processed according to the values which are derived from feature characteristics. Segmentation step, we use histogram and closing operation of morphology filter which extracts dark regions for pothole detection. Candidate step, we extract candidate regions of pothole using various features such as size, compactness, etc. Finally, decision step, candidate regions are decided whether pothole or not using comparison of pothole and background's features. Experimental results show that our proposed pothole detection method has better results than existing methods and good performance in discrimination of pothole and similar patterns.

• The Transactions of the Korea Information Processing Society
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• v.6 no.6
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• pp.1686-1692
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• 1999

This paper describes the feature area-based vehicle plate recognition system(VPRS). For the extraction of vehicle plate in a vehicle image, we used the method which extracts vehicle plate area from a s vehicle image using intensity variation. For the extraction of the feature area containing character from the extracted vehicle plate, we used the histogram-based approach and the relative location information of individual characters in the extracted vehicle plate. The extracted feature area is used as the input vector of ART2 neural network. The proposed method simplifies the existing complex preprocessing the solves the problem of distortion and noise in the binarization process. In the difficult cases of character extraction by binarization process of previous method, our method efficiently extracts characters regions and recognizes it.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06c
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• pp.492-497
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• 2008

최근 모바일 장치의 사용 영역 확대와 더불어 기기장치 내의 다양한 영상 데이터에 대한 효율적인 관리와 검색에 관한 기술 연구가 요구되고 있다. 그러나 모바일 장치의 낮은 CPU성능과 한정적인 메모리를 극복하기 위해 저 용량 그리고 고속의 검색 엔진 개발이 요구된다. 이 문제를 해결하기 위하여, 본 논문에서는 RGB 색상 공간에서 HSV 색상 공간 상의 36개의 특징 값으로 변환하는 검색 테이블 방법을 제안한다. 제안하는 방법에 의해, 입력 영상은 검색 테이블에 기반하여 빠르게 색상과 위치에 대한 두개의 특징 히스토그램으로 변환된다. 여기서, 특징추출에 필요한 연산은 본 논문의 실험 결과에서 보는 바와 같이 매우 낮다. 제안하는 방법을 이용하여, 우리는 영상, 색상 그리고 블랍에 의한 질의가 가능한 모바일 기반 영상 검색 시스템을 구현하였다. 본 논문에서 제시하는 실험결과는 제안하는 방법이 충분히 모바일에서 운용 가능한 가볍고 빠른 방법임을 알 수 있다.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.3
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• pp.135-138
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• 2014

This paper devices a facial expression recognition method robust to overfitting using 2D-DCT and EHMM algorithm. In particular, this paper achieves enhanced recognition performance by setting up a large window size for 2D-DCT feature extraction and extracting the observation vectors of EHMM. The experimental results on the CK facial expression database and the JAFFE facial expression database showed that the facial expression recognition accuracy was improved according as window size is large. Also, the proposed method revealed the recognition accuracy of 87.79% and showed enhanced recognition performance ranging from 46.01% to 50.05% in comparison to previous approaches based on histogram feature, when CK database is employed for training and JAFFE database is used to test the recognition accuracy.

• The Transactions of the Korea Information Processing Society
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• v.6 no.7
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• pp.1825-1831
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• 1999

In this paper, to reduce misrecognized characters, we propose the new method that extract features from character to apply to the character recognition using features from character to apply to the character recognition using fractal dimensions and attractors. Firstly, to reduce the load of recognizer we classify the characters. For the classified character, we extract the features for Box-counting dimensions. Natural Measures, Information dimensions then recognize characters. With histogram, we generate attractors and calculate dimensions from attractors. Then we recognize characters with dimensions of characters and attractors. An experimental result that the overall recognition rates for the training data and testing data are 96.03% and 91.74% respectively. This result shows the effectiveness of proposed method.

• The Journal of the Korea Contents Association
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• v.3 no.2
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• pp.24-30
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• 2003

Pre-existing authentication system such as token based method, knowledge-based and hybrid method have problems such as loss and wiretapping. for this, this paper describes the biometric authentication system which have the excellent convenience and security. In particular, a new biometric system by human face biometric features which have the non-enforcement and non-touch measurement is proposed. Firstly, facial features are extracted by Y- histogram and tilted face images we corrected by coordinate transformation and scaling has done for achieving independent of the camera positions. Secondly, feature vectors are extracted such as distance and intersection angles and similarities we measured by fuzzy relation matrix. finally, the effectiveness of this paper is demonstrated by experiments.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.96-99
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• 2008

본 논문은 의료 영상을 효과적으로 분류하고 검색 하기 위한 새로운 알고리즘을 제안한다. 의료 영상 중 X-Ray 영상은 어두운 배경에 반해 밝은 전경을 갖고 있기 때문에, 전경의 두드러진 부분에서만 시각 기술자로 추출한다. 우선, 색 구조 기술자(H-CSD)에서 해리스 코너 검출기로 검출한 관심 포인트들에서 색상 특징을 추출하고, 경계선 히스토그램 기술자에서 영상의 전역 및 지역적 질감 특징을 추출한다. 추출된 특징 벡터는 멀티클래스 SVM 에 적용되어 각 영상을 위한 멤버십 스코어를 얻는다. 이후, H-CSD와 EHD 에 대한 SVM 의 멤버십 스코어를 앙상블 결합하여 하나의 특징 벡터로 생성하고, K-nearest Neighborhood 방법을 이용하여 상위-K 개의 영상을 검색을 하도록 하였다. imageCLEFmed2007 을 이용한 실험 결과에서 다른 전역적 속성 또는 분류 기반 검색 방법에 비교하여 보다 개선된 검색 성능을 나타냄을 확인하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.471-476
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• 2016

In this paper, recognition system for continuous human action is explained by using motion history image and histogram of oriented gradient with spotter model based on depth information, and the spotter model which performs action spotting is proposed to improve recognition performance in the recognition system. The steps of this system are composed of pre-processing, human action and spotter modeling and continuous human action recognition. In pre-processing process, Depth-MHI-HOG is used to extract space-time template-based features after image segmentation, and human action and spotter modeling generates sequence by using the extracted feature. Human action models which are appropriate for each of defined action and a proposed spotter model are created by using these generated sequences and the hidden markov model. Continuous human action recognition performs action spotting to segment meaningful action and meaningless action by the spotter model in continuous action sequence, and continuously recognizes human action comparing probability values of model for meaningful action sequence. Experimental results demonstrate that the proposed model efficiently improves recognition performance in continuous action recognition system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.3
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• pp.613-620
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• 2010

Additive noise and channel distortion strongly degrade the performance of speaker verification systems, as it introduces distortion of the features of speech. This distortion causes a mismatch between the training and recognition conditions such that acoustic models trained with clean speech do not model noisy and channel distorted speech accurately. This paper presents a phase-related feature extraction method in order to improve the robustness of the speaker verification systems. The instantaneous frequency is computed from the phase of speech signals and features from the histogram of the instantaneous frequency are obtained. Experimental results show that the proposed technique offers significant improvements over the standard techniques in both clean and adverse testing environments.

• Journal of the Korea Computer Industry Society
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• v.3 no.12
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• pp.1765-1774
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• 2002

A face recognition is currently the field which many research have been processed actively. But many problems must be solved the previous problem. First, We must recognize the face of the object taking a location various lighting change and change of the camera into account. In this paper, we proposed that new method to fund feature within fast and correct computation time after scanning PC camera and ID card picture. It converted RGB color space to YUV. A face skin color extracts which equalize a histogram of Y ingredient without the luminance. After, the method use V' ingredient which transformes V ingredient of YUV and then find the face feature. The reult of the experiment shows getting correct input face image from ID Card picture and PC camera.