• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.229-232
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• 2009

Shot change detection is an important technique for effective management of video data, so detection scheme requires adaptive detection techniques to be used actually in various video. In this paper, we propose an adaptive shot change detection algorithm using the mean of feature value on variable reference blocks. Our algorithm determines shot change detection by defining adaptive threshold values with the feature value extracted from video frames and comparing the feature value and the threshold value. We obtained better detection ratio than the conventional methods maximally by 15% in the experiment with the same test sequence. We also had good detection ratio for other several methods of feature extraction and could see real-time operation of shot change detection in the hardware platform with low performance was possible by implementing it in TVUS model of HOMECAST Company. Thus, our algorithm in the paper can be useful in PMP or other portable players.

• ETRI Journal
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• 제41권5호
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• pp.684-695
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• 2019

In a cloud environment, performance degradation, or even downtime, of virtual machines (VMs) usually appears gradually along with anomalous states of VMs. To better characterize the state of a VM, all possible performance metrics are collected. For such high-dimensional datasets, this article proposes a feature extraction algorithm based on unsupervised fuzzy linear discriminant analysis with kernel (UFKLDA). By introducing the kernel method, UFKLDA can not only effectively deal with non-Gaussian datasets but also implement nonlinear feature extraction. Two sets of experiments were undertaken. In discriminability experiments, this article introduces quantitative criteria to measure discriminability among all classes of samples. The results show that UFKLDA improves discriminability compared with other popular feature extraction algorithms. In detection accuracy experiments, this article computes accuracy measures of an anomaly detection algorithm (i.e., C-SVM) on the original performance metrics and extracted features. The results show that anomaly detection with features extracted by UFKLDA improves the accuracy of detection in terms of sensitivity and specificity.

• Journal of Information Processing Systems
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• 제19권1호
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• pp.109-117
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• 2023

Defect detection is one of the key factors in fabric quality control. To improve the speed and accuracy of denim fabric defect detection, this paper proposes a defect detection algorithm based on cascading feature extraction architecture. Firstly, this paper extracts these weight parameters of the pre-trained VGG16 model on the large dataset ImageNet and uses its portability to train the defect detection classifier and the defect recognition classifier respectively. Secondly, retraining and adjusting partial weight parameters of the convolution layer were retrained and adjusted from of these two training models on the high-definition fabric defect dataset. The last step is merging these two models to get the defect detection algorithm based on cascading architecture. Then there are two comparative experiments between this improved defect detection algorithm and other feature extraction methods, such as VGG16, ResNet-50, and Xception. The results of experiments show that the defect detection accuracy of this defect detection algorithm can reach 94.3% and the speed is also increased by 1-3 percentage points.

• 인터넷정보학회논문지
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• 제7권3호
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• pp.143-153
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• 2006

얼굴 특징 검출은 HCI, 얼굴 인식, 얼굴 추적, 표정 인식 및 이미지 데이터 검색등과 같은 응용분야의 근간 기술이다. 실시간 환경에서 얼굴 특징 검출을 처리하기 위해서는 검출 알고리즘의 속도가 중요한 관건으로 작용하고 있다. 또한 빛의 변화, 대상의 위치, 각도, 복잡한 배경등과 같은 요인들은 얼굴 특징 검출 알고리즘의 검출율을 낮추는데 영향을 미치므로 이를 개선한 방법이 필요하다. 본 연구에서는 검출율과 검출 속도를 동시에 개선한 알고리즘을 제안한다. 제안 알고리즘은 얼굴 이미지에 빛 보상 알고리즘인 CLAHE를 이용하여 빛의 변화에 강건하도록 이미지를 개선한 다음 얼굴 피부 영역을 검출한다. 검출한 피부 영역에서 얼굴 특징 포인트를 추출하기 위해 얼굴 특징의 외형기반 기하학적 성질을 이용한다. 제안 알고리즘은 얼굴 특징 검출의 정확도를 높일 뿐 아니라 빠른 검출 속도를 보임으로써 얼굴 추적, 인식 등과 같은 실시간 응용분야에 적용할 수 있다.

• 대한임베디드공학회논문지
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• 제12권6호
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• pp.395-403
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• 2017

Image processing for object detection and identification has been studied for supply chain management application with various approaches. Among them, feature pointed detection algorithm is used to track an object or to recognize a position in automated supply chain systems and a depth image based feature point detection is recently highlighted in the application. The result of feature point detection is easily influenced by image noise. Also, the depth image has noise itself and it also affects to the accuracy of the detection results. In order to solve these problems, we propose a novel hybrid filtering mechanism for depth image based feature point detection, it shows better performance compared with conventional hybrid filtering mechanism.

• 한국멀티미디어학회논문지
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• 제14권8호
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• pp.992-999
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• 2011

This paper proposed a human body posture recognition program based on haar-like feature and hand activity detection. Its distinguishing features are the combination of face detection and motion detection. Firstly, the program uses the haar-like feature face detection to receive the location of human face. The haar-like feature is provided with the advantages of speed. It means the less amount of calculation the haar-like feature can exclude a large number of interference, and it can discriminate human face more accurately, and achieve the face position. Then the program uses the frame subtraction to achieve the position of human body motion. This method is provided with good performance of the motion detection. Afterwards, the program recognises the human body motion by calculating the relationship of the face position with the position of human body motion contour. By the test, we know that the recognition rate of this algorithm is more than 92%. The results show that, this algorithm can achieve the result quickly, and guarantee the exactitude of the result.

• Journal of Information Processing Systems
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• 제12권3호
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• pp.502-510
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• 2016

Considering video copy transform diversity, a multi-feature video copy detection algorithm based on a Speeded-Up Robust Features (SURF) local descriptor is proposed in this paper. Video copy coarse detection is done by an ordinal measure (OM) algorithm after the video is preprocessed. If the matching result is greater than the specified threshold, the video copy fine detection is done based on a SURF descriptor and a box filter is used to extract integral video. In order to improve video copy detection speed, the Hessian matrix trace of the SURF descriptor is used to pre-match, and dimension reduction is done to the traditional SURF feature vector for video matching. Our experimental results indicate that video copy detection precision and recall are greatly improved compared with traditional algorithms, and that our proposed multiple features algorithm has good robustness and discrimination accuracy, as it demonstrated that video detection speed was also improved.

• 한국통신학회논문지
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• 제38A권6호
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• pp.486-491
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• 2013

본 논문은 객체검출(object detection)에 사용되는 분류기의 학습을 위한 빠르고 효율적인 Haar-like feature 선택 알고리듬을 제안한다. 기존 AdaBoost를 이용한 Haar-like feature 선택 알고리듬은 학습 샘플들에 대한 피쳐의 에러만을 고려하여 형태적으로 유사하거나 중복되는 피쳐가 선택되는 경우가 많았다. 제안하는 알고리듬은 피쳐의 형태와 피쳐간의 거리로부터 피쳐의 유사도를 계산하고 이미 선택된 피쳐와 유사도가 큰 피쳐들을 피쳐 세트에서 제거하여 빠르고 효율적인 피쳐 선택이 이루어지도록 하였다. FERET 얼굴 데이터베이스를 사용하여 제안된 알고리듬을 사용하여 학습시킨 분류기와 기존 알고리듬을 사용한 분류기의 성능을 비교하였다. 실험 결과 제안한 피쳐 선택 방법을 사용하여 학습시킨 분류기가 기존 방법을 사용한 분류기보다 향상된 성능을 보였으며, 동일한 성능을 갖도록 학습시켰을 경우 분류기의 피쳐 수가 20% 감소하였다.

• 한국멀티미디어학회논문지
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• 제9권10호
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• pp.1266-1275
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• 2006

본 논문은 AdaBoost(Adaptive Boosting)알고리즘을 이용한 실시간 얼굴 검출 및 추적에 패한 기법을 제안한다. 얼굴 검출은 8종류의 간단한 웨이블릿 특징 모형을 이용한다. 각각의 특징들은 $20{\times}20$의 훈련 영상에서 다양한 크기와 위치로 배치되어 초기의 특징 집합을 구성한다. 초기의 특징 집합과 훈련 영상은 AdaBoost알고리즘의 입력으로 사용된다. AdaBoost알고리즘의 기본원리는 약한 분류기를 선형적으로 결합하여 최종적으로는 계층적 구조를 갖는 강한 분류기론 생성하는 것이다. 본 논문에서는 AdaBoost알고리즘에서 훈련 영상과 초기의 특징 집합 간에 이루어지는 반복적 계산량을 줄이기 위해 SAT(Summed-Area Table) 기법을 이용하였다. 얼굴 추적은 Pan-Tilt카메라를 통해 동적으로 가시 영역을 확장해 가면서 검출된 영역의 위치와 크기정보를 이용하여 실시간으로 이루어진다. 검출된 얼굴 영역의 중심을 전체 영상의 중심으로 이동하는 방법을 사용하였다. 실험결과 92.5%의 얼굴 검출율과 평균 12프레임의 얼굴 추적속도를 얻었다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.2373-2378
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• 2005

An emotion detection algorithm using frontal facial image is presented in this paper. The algorithm is composed of three main stages: image processing stage and facial feature extraction stage, and emotion detection stage. In image processing stage, the face region and facial component is extracted by using fuzzy color filter, virtual face model, and histogram analysis method. The features for emotion detection are extracted from facial component in facial feature extraction stage. In emotion detection stage, the fuzzy classifier is adopted to recognize emotion from extracted features. It is shown by experiment results that the proposed algorithm can detect emotion well.