• Proceedings of the Korean Society of Computer Information Conference
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• 2016.07a
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• pp.293-294
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• 2016

스마트폰, 블랙박스, CCTV 등을 통해 다양하고 방대한 영상 데이터가 발생하고 있다. 그중에서 사람의 얼굴 영상을 통해 개인을 인식 및 식별하고 감정 상태를 분석하려는 다양한 연구가 진행되고 있다. 본 논문에서는 디지털영상처리 분야에서 널리 사용되고 있는 SIFT알고리즘을 이용하여, 얼굴영상에 대한 특징점을 추출하고 이를 기반으로 성별, 나이 및 기초적인 감정 상태를 분류할 수 있는 시스템을 제안한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.1-6
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• 2005

In this paper, we propose an emotion recognition method using the facial images and speech signals. Six basic emotions including happiness, sadness, anger, surprise, fear and dislike are investigated. Facia] expression recognition is performed by using the multi-resolution analysis based on the discrete wavelet. Here, we obtain the feature vectors through the ICA(Independent Component Analysis). On the other hand, the emotion recognition from the speech signal method has a structure of performing the recognition algorithm independently for each wavelet subband and the final recognition is obtained from the multi-decision making scheme. After merging the facial and speech emotion recognition results, we obtained better performance than previous ones.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.7
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• pp.2496-2511
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• 2014

Face recognition is considered a complex biometrics in the field of image processing mainly due to the constraints imposed by variation in the appearance of facial images. These variations in appearance are affected by differences in expressions and/or occlusions (sunglasses, scarf etc.). This paper discusses incremental Kernel Fisher Discriminate Analysis on sub-classes for dealing with partial occlusions and variant expressions. This framework focuses on the division of classes into fixed size sub-classes for effective feature extraction. For this purpose, it modifies the traditional Linear Discriminant Analysis into incremental approach in the kernel space. Experiments are performed on AR, ORL, Yale B and MIT-CBCL face databases. The results show a significant improvement in face recognition.

• Journal of Ocean Engineering and Technology
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• v.27 no.3
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• pp.22-28
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• 2013

This paper suggests a navigation information display system that is based on augmented reality technology. A navigator always has to confirm the information from marine electronic navigation devices and then compare it with the view of targets outside the windows. This "head down" posture causes discomfort and sometimes near accidents such as collisions or missing objects, because he or she cannot keep an eye on the front view of the windows. Augmented reality can display both virtual and real information in a single display. Therefore, we attempted to adapt AR technology to assist navigators. To analyze the outside view of the bridge window, various computer image processing techniques are required because the sea surface has many noises that disturb computer image processing for object detection, such as waves, wakes, light reflection, and so on. In this study, we investigated an analysis module to extract navigational information from images that are captured by a CCTV camera, and we validated our prototype.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2002.05a
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• pp.193-199
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• 2002

Analyzing working conditions with shape characteristics of wear debris in a lubricated machine, it can be effect on diagnosis of hydraulic machining system. And it can be recognized that results are processed threshold images of wear debris. But, in order to predict and estimate a working condition of lubricated machine, it is need to analysis a shape characteristic of wear debris and to identify. Therefor, If shape characteristics of wear debris are identified by computer image analysis and the neural network, it is possible to find the cause and effect of wear condition. In this stud)r, wear debris in the lubricant oil are extracted by membrane filter $(0.45{\mu}m)$, and the quantitative value of shape characteristic of wear debris are calculated by the digital image processing. This morphological information are studied and identified by tile artificial neural network. The purpose of this study is to apply morphological characteristic of wear debris to prediction and estimation of working condition in hydraulic machining systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.4
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• pp.1424-1440
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• 2015

It is a challenging problem to search the intended images from a large number of candidates. Content based image retrieval (CBIR) is the most promising way to tackle this problem, where the most important topic is to measure the similarity of images so as to cover the variance of shape, color, pose, illumination etc. While previous works made significant progresses, their adaption ability to dataset is not fully explored. In this paper, we propose a similarity learning method on the basis of probabilistic generative model, i.e., probabilistic latent semantic analysis (PLSA). It first derives Fisher kernel, a function over the parameters and variables, based on PLSA. Then, the parameters are determined through simultaneously maximizing the log likelihood function of PLSA and the retrieval performance over the training dataset. The main advantages of this work are twofold: (1) deriving similarity measure based on PLSA which fully exploits the data distribution and Bayes inference; (2) learning model parameters by maximizing the fitting of model to data and the retrieval performance simultaneously. The proposed method (PLSA-FK) is empirically evaluated over three datasets, and the results exhibit promising performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.6
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• pp.2302-2316
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• 2015

We propose an efficient method of extracting targets within a region of interest in non-homogeneous infrared images by using a principal component analysis (PCA) plane and adaptive Gaussian kernel. Existing approaches for extracting targets have been limited to using only the intensity values of the pixels in a target region. However, it is difficult to extract the target regions effectively because the intensity values of the target region are mixed with the background intensity values. To overcome this problem, we propose a novel PCA based approach consisting of three steps. In the first step, we apply a PCA technique minimizing the total least-square errors of an IR image. In the second step, we generate a binary image that consists of pixels with higher values than the plane, and then calculate the second derivative of the sum of the square errors (SDSSE). In the final step, an iteration is performed until the convergence criteria is met, including the SDSSE, angle and labeling value. Therefore, a Gaussian kernel is weighted in addition to the PCA plane with the non-removed data from the previous step. Experimental results show that the proposed method achieves better segmentation performance than the existing method.

• Journal of the Korea Computer Graphics Society
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• v.24 no.4
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• pp.1-9
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• 2018

We propose an emotional training framework that can determine the initial symptom of schizophrenia by using emotional analysis method through facial expression change. We use Emotion API in Microsoft to obtain facial expressions and emotion values at the present time. We analyzed these values and recognized subtle facial expressions that change with time. The emotion states were classified according to the peak analysis-based variance method in order to measure the emotions appearing in facial expressions according to time. The proposed method analyzes the lack of emotional recognition and expressive ability by using characteristics that are different from the emotional state changes classified according to the six basic emotions proposed by Ekman. As a result, the analyzed values are integrated into the image color transfer framework so that users can easily recognize and train their own emotional changes.

• Proceedings of the IEEK Conference
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• summer
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• pp.417-420
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• 2004

Caenorhabditis (C) elegans is often used in genetic analysis in neuroscience because its simple organism; an adult hermaphrodite contains only 302 neuron. So the worm is often used to study of cancer, alzheimer disease, aging, etc. To analysis mutant type of the worm, an experienced observer was able to subjectively before, but requirements for objective analysis are now increasing. For this reason, we use automated tracking systems to extract global movement coordinate of the worm. In this paper, we extract features, which are related on reversal and movement of the worm. Using these features, we quantitatively analysis 6 type mutant by movement and reversal characteristic.

• Journal of the Korea Society of Computer and Information
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• v.25 no.2
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• pp.49-58
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• 2020

In this study, we solve an online multi-object problem which finds object states (i.e. locations and sizes) while conserving their identifications in online-provided images and detections. We handle this problem based on a tracking-by-detection approach by linking (or associating) detections between frames. For more accurate online association, we propose novel online appearance learning with discrete fourier transform and partial least square analysis (PLS). We first transform each object image into a Fourier image in order to extract meaningful features on a frequency domain. We then learn PLS subspaces which can discriminate frequency features of different objects. In addition, we incorporate the proposed appearance learning into the recent confidence-based association method, and extensively compare our methods with the state-of-the-art methods on MOT benchmark challenge datasets.