• Journal of KIISE:Software and Applications
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• v.36 no.10
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• pp.816-824
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• 2009

In the face space, face images due to illumination and pose variations have a nonlinear distribution. Active Appearance Models (AAM) based on the linear model have limits to the nonlinear distribution of face images. In this paper, we assume that a few clusters of face images are given; we build local AAMs according to the clusters of face images, and then select a proper AAM model during the fitting phase. To solve the problem of updating fitting parameters among the models due to the model changing, we propose to build in advance relationships among the clusters in the parameter space from the training images. In addition, we suggest a gradual model changing to reduce improper model selections due to serious fitting failures. In our experiment, we apply the proposed model to Yale Face Database B and compare it with the previous method. The proposed method demonstrated successful fitting results with strongly illuminated face images of deep shadows.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.6
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• pp.127-136
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• 2010

In multi-view video, illumination disharmony between neighboring views can occur on account of different location of each camera and imperfect camera calibration, and so on. Such discrepancy can be the cause of the performance decrease of multi-view video coding by mismatch of inter-view prediction which refer to the pictures obtained from the neighboring views at the same time. In this paper, we propose an efficient histogram-based prefiltering algorithm to compensate mismatches between the luminance and chrominance components in multi-view video for improving its coding efficiency. To compensate illumination variation efficiently, all camera frames of a multi-view sequence are adjusted to a predefined reference through the histogram matching. A Cosited filter that is used for chroma subsampling in many video encoding schemes is applied to each color component prior to histogram matching to improve its performance. The histogram matching is carried out in the RGB color space after color space converting from YCbCr color space. The effective color conversion skill that has respect to direction of edge and range of pixel value in an image is employed in the process. Experimental results show that the compression ratio for the proposed algorithm is improved comparing with other methods.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.07a
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• pp.80-82
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• 2010

본 논문에서는 깊이 정보를 이용하여 영상을 색상 분포가 각각 다른 객체 영상으로 분리하고 개별적으로 히스토그램 매칭 기법을 적용하는 조명 보상 기법을 제안한다. 서로 위치가 다른 다시점 카메라의 경우, 다시점 비디오 부호화(multi-view video coding)의 성능을 저하시키는 인접 시점 영상 간 조명 불일치 현상이 발생한다. 이러한 조명 불일치를 보상하기 위한 히스토그램 매칭(histogram matching)을 이용한 전처리 기법이 제안되었다. 모든 시점의 다시점 영상 히스토그램은 정해진 참조 시점 영상의 히스토그램으로 매칭되어 조명 불일치와 다시점 비디오 부호화의 성능을 개선할 수 있다. 하지만 일반적인 영상은 색상 분포와 깊이 정보가 상호 독립적인 객체들로 구성되어 있다. 또한 다시점 비디오는 시점에 따라 획득된 영상 간에 동일 객체의 위치와 깊이가 서로 달라 정해진 참조 시점의 히스토그램으로 매칭하는 기존의 방법은 적합하지 않다. 본 논문에서는 주어진 영상 내에서 깊이 정보를 이용하여 객체를 먼저 분리하고, 객체 영상별로 히스토그램 매칭 기법을 적용하여 색상 보상을 수행하는 새로운 기법을 제안한다. 실험을 통해 제안하는 객체 단위의 조명 보상 기법이 향상된 다시점 비디오 부호화 효율을 보이는 것을 확인하였다.

• Journal of Biomedical Engineering Research
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• v.35 no.1
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• pp.1-7
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• 2014

Among semi-quantitative or fully quantitative lateral flow assay readers, an image sensor-based instrument has been widely used because of its simple setup, cheap sensor price, and compact equipment size. For all previous approaches, monochrome CCD or CMOS cameras were used for lateral flow assay imaging in which the overall intensities of all colors were taken into consideration to estimate the analyte content, although the analyte related color information is only limited to a narrow wavelength range. In the present work, we introduced a color CCD camera as a sensor and a color decomposition method to improve the sensitivity of the quantitative biosensor system which utilizes the lateral flow assay successfully. The proposed setup and image processing method were applied to achieve the quantification of imitatively dispensed particles on the surface of a porous membrane first, and the measurement result was then compared with that using a monochrome CCD. The compensation method was proposed in different illumination conditions. Eventually, the color decomposition method was introduced to the commercially available lateral flow immunochromatographic assay for the diagnosis of myocardial infarction. The measurement sensitivity utilizing the color image sensor is significantly improved since the slopes of the linear curve fit are enhanced from 0.0026 to 0.0040 and from 0.0802 to 0.1141 for myoglobin and creatine kinase (CK)-MB detection, respectively.

• Journal of Korea Multimedia Society
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• v.13 no.2
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• pp.225-236
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• 2010

This study proposes a method for the recognition of emergency situation according to the bimodal information of camera image sensor and gravity sensor. This method can recognize emergency condition by mutual cooperation and compensation between sensors even when one of the sensors malfunction, the user does not carry gravity sensor, or in the place like bathroom where it is hard to acquire camera images. This paper implemented HMM(Hidden Markov Model) based learning and recognition algorithm to recognize actions such as walking, sitting on floor, sitting at sofa, lying and fainting motions. Recognition rate was enhanced when image feature vectors and gravity feature vectors are combined in learning and recognition process. Also, this method maintains high recognition rate by detecting moving object through adaptive background model even in various illumination changes.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.4
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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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.6
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• pp.797-803
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• 2014

In this paper, 3D face recognition system is designed by using polynomial based on RBFNNs. In case of 2D face recognition, the recognition performance reduced by the external environmental factors such as illumination and facial pose. In order to compensate for these shortcomings of 2D face recognition, 3D face recognition. In the preprocessing part, according to the change of each position angle the obtained 3D face image shapes are changed into front image shapes through pose compensation. the depth data of face image shape by using Multiple Point Signature is extracted. Overall face depth information is obtained by using two or more reference points. The direct use of the extracted data an high-dimensional data leads to the deterioration of learning speed as well as recognition performance. We exploit principle component analysis(PCA) algorithm to conduct the dimension reduction of high-dimensional data. Parameter optimization is carried out with the aid of PSO for effective training and recognition. The proposed pattern classifier is experimented with and evaluated by using dataset obtained in IC & CI Lab.

• Journal of Broadcast Engineering
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• v.21 no.4
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• pp.493-505
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• 2016

Among modern computer vision and video surveillance systems, the pedestrian counting system is a one of important systems in terms of security, scheduling and advertising. In the field of, pedestrian counting remains a variety of challenges such as changes in illumination, partial occlusion, overlap and people detection. During pedestrian counting process, the biggest problem is occlusion effect in crowded environment. Occlusion and overlap must be resolved for accurate people counting. In this paper, we propose a novel pedestrian counting system which improves existing pedestrian tracking method. Unlike existing pedestrian tracking method, proposed method shows that average filter tracking method can improve tracking performance. Also proposed method improves tracking performance through frame compensation and outlier removal. At the same time, we keep various information of tracking objects. The proposed method improves counting accuracy and reduces error rate about S6 dataset and S7 dataset. Also our system provides real time detection at the rate of 80 fps.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1224-1242
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• 2018

Aiming at motion analysis and compensation, it is essential to conduct motion detection with images. However, motion detection and tracking from low-altitude images obtained from an unmanned aerial system may pose many challenges due to degraded image quality caused by platform motion, image instability and illumination fluctuation. This research tackles these challenges by proposing a modified joint transform correlation algorithm which includes two preprocessing strategies. In spatial domain, a modified fuzzy edge detection method is proposed for preprocessing the input images. In frequency domain, to eliminate the disturbance of self-correlation items, the cross-correlation items are extracted from joint power spectrum output plane. The effectiveness and accuracy of the algorithm has been tested and evaluated by both simulation and real datasets in this research. The simulation experiments show that the proposed approach can derive satisfactory peaks of cross-correlation and achieve detection accuracy of displacement vectors with no more than 0.03pixel for image pairs with displacement smaller than 20pixels, when addition of image motion blurring in the range of 0~10pixel and 0.002variance of additive Gaussian noise. Moreover,this paper proposes quantitative analysis approach using tri-image pairs from real datasets and the experimental results show that detection accuracy can be achieved with sub-pixel level even if the sampling frequency can only attain 50 frames per second.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.411-412
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• 2009

최근 시각 기반 인터페이스를 위하여, 손 동작 인식 기술 개발의 필요성이 증가하고 있다. 이러한 손 동작 인식 기술에서 손 모양 인식은 중요한 부분이며, 이는 손 영역 검출의 결과에 많은 영향을 받는다. 기존의 많은 손 동작 인식 기술들은 사람의 피부색이 갖는 컬러 특징을 이용하여 손 영역을 검출하였다. 그러나, 이러한 컬러 정보는 배경 및 조도 변화에 매우 민감하다. 이러한 문제를 해결하기 위해 본 논문에서는, 색 온도 보정 과정을 손 영역 검출에 적용함으로써 배경 및 조도 변화에 강인한 손 모양 인식 시스템을 제안한다. 제안한 방법이 배경 및 조도 변화에 강인함을 보이기 위해, 조명의 밝기 수준을 조절하며, 다양한 색을 배경으로 찍은 손 영상을 입력으로 손 모양 인식 성능을 실험하였다. 기존의 피부색을 이용한 손 영역 검출과의 비교 실험 결과를 통해, 제안한 방법이 배경 및 조도 변화에 강인한 손 모양 인식 성능을 가짐을 확인하였다.