• Proceedings of the Korea Information Processing Society Conference
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• 2007.11a
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• pp.34-37
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• 2007

Face recognition is still a challenging problem in pattern recognition field which is affected by different factors such as facial expression, illumination, pose etc. The facial feature such as eyes, nose, and mouth constitute a complete face. Mouth feature of face is under the undesirable effect of facial expression as many factors contribute the low performance. We proposed a new approach for face recognition under facial expression applying two cascaded classifiers to improve recognition rate. All facial expression images are treated by general purpose classifier at first stage. All rejected images (applying threshold) are used for adaptation using GA for improvement in recognition rate. We apply Gabor Wavelet as a general classifier and Gabor wavelet with Genetic Algorithm for adaptation under expression variance to solve this issue. We have designed, implemented and demonstrated our proposed approach addressing this issue. FERET face image dataset have been chosen for training and testing and we have achieved a very good success.

• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1343-1358
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• 2020

In the stagewise arithmetic orthogonal matching pursuit algorithm, the weak threshold used in sparsity estimation is determined via maximum iterations. Different maximum iterations correspond to different thresholds and affect the performance of the algorithm. To solve this problem, we propose an improved variable weak threshold based on the stagewise arithmetic orthogonal matching pursuit algorithm. Our proposed algorithm uses the residual error value to control the weak threshold. When the residual value decreases, the threshold value continuously increases, so that the atoms contained in the atomic set are closer to the real sparsity value, making it possible to improve the reconstruction accuracy. In addition, we improved the generalized Jaccard coefficient in order to replace the inner product method that is used in the stagewise arithmetic orthogonal matching pursuit algorithm. Our proposed algorithm uses the covariance to replace the joint expectation for two variables based on the generalized Jaccard coefficient. The improved generalized Jaccard coefficient can be used to generate a more accurate calculation of the correlation between the measurement matrixes. In addition, the residual is more accurate, which can reduce the possibility of selecting the wrong atoms. We demonstrate using simulations that the proposed algorithm produces a better reconstruction result in the reconstruction of a one-dimensional signal and two-dimensional image signal.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.205-208
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• 2007

Due to the development and wide use of digital multimedia broadcasting (DMB) and Wireless Broadband Internet (WiBro), the digital contents industry using images has been progressed. Therefore, the image processing has been applied in a variety of fields and in order to transmit and conserve accurate information, the degradation phenomenon for images should be removed. As a representative cause of the degradation phenonenon, noise has become known and Gaussian noise occurs in the process of transmission. Diverse researches for Gaussian noise removal have been implemented and a great number of algorithms have been proposed until now. In this paper, for mage restoration an algorithm using the adaptive threshold value is proposed in Gaussian noise environment and the threshold value is established by using the histogram of edge image. And from simulation results, the noise removal performance of the proposed method is proven using mean square error (MSE) and peak signal to noise ratio (PSNR).

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.10
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• pp.955-961
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• 2010

In this paper, we describe a study of a vehicle detection method based on a Boosting Classifier which uses Histogram of Oriented Gradient (HOG) features and Image Segmentation techniques. An input image is segmented by means of a split and merge algorithm. Then, the two largest segmented regions are removed in order to reduce the search region and speed up processing time. The HOG features are then calculated for each pixel in the search region. In order to detect the vehicle region we used the AdaBoost (adaptive boost) method, which is well known for classifying samples with two classes. To evaluate the performance of the proposed method, 537 training images were used to train and learn the classifier, followed by 500 non-training images to provide the recognition rate. From these experiments we were able to detect the proper image 98.34% of the time for the 500 non-training images. In conclusion, the proposed method can be used for detecting the location of a vehicle in an intelligent vehicle control system.

• Journal of the Korea Computer Industry Society
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• v.3 no.9
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• pp.1145-1156
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• 2002

A method to measure the speed and distance of moving object is proposed using the stereo vision system. One of the most important factors for measuring the speed and distance of moving object is the accuracy of object tracking. Accordingly, the background image algorithm is adopted to track the rapidly moving object and the local opening operator algorithm is used to remove the shadow and noise of object. The extraction efficiency of moving object is improved by using the adaptive threshold algorithm independent to variation of brightness. Since the left and right central points are compensated, the more exact speed and distance of object can be measured. Using the background image algorithm and local opening operator algorithm, the computational processes are reduced and it is possible to achieve the real-time processing of the speed and distance of moving object. The simulation results show that background image algorithm can track the moving object more rapidly than any other algorithm. The application of adaptive threshold algorithm improved the extraction efficiency of the target by reducing the candidate areas. Since the central point of the target is compensated by using the binocular parallax, the error of measurement for the speed and distance of moving object is reduced. The error rate of measurement for the distance from the stereo camera to moving object and for the speed of moving object are 2.68% and 3.32%, respectively.

• Journal of Korean Society of Transportation
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• v.28 no.3
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• pp.169-183
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• 2010

Image-based traffic information collection systems have entered widespread adoption and use in many countries since these systems are not only capable of replacing existing loop-based detectors which have limitations in management and administration, but are also capable of providing and managing a wide variety of traffic related information. In addition, these systems are expanding rapidly in terms of purpose and scope of use. Currently, the utilization of image processing technology in the field of traffic accident management is limited to installing surveillance cameras on locations where traffic accidents are expected to occur and digitalizing of recorded data. Accurately recording the sequence of situations around a traffic accident in a freeway and then objectively and clearly analyzing how such accident occurred is more urgent and important than anything else in resolving a traffic accident. Therefore, in this research, existing technologies, this freeway attribute, velocity changes, volume changes, occupancy changes reflect judge the primary. Furthermore, We pointed out by many past researches while presenting and implementing an active and environmentally adaptive methodology capable of effectively reducing false detection situations which frequently occur even with the Gaussian Mixture model analytical method which has been considered the best among well-known environmental obstacle reduction methods. Therefore, in this way, the accident was the final decision. Also, environmental factors occur frequently, and with the index finger situations, effectively reducing that can actively and environmentally adaptive techniques through accident final judgment. This implementation of the evaluate performance of the experiment road of 12 incidents in simulated and the jang-hang IC's real-time accident experiment. As a result, the do well detection 93.33%, false alarm 6.7% as showed high reliability.

• The Transactions of the Korea Information Processing Society
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• v.6 no.2
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• pp.534-542
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• 1999

A sell-designed rate control strategy can improve overall picture quality for video transmission over a constant bit rate channel and the rate control method is not a normative part of MPEG-video standard, the performance of MPEG video codec can be quite different depends on how to implement the rate control scheme. The rate control scheme proposed in MPEG show good results when scene changes is not occurred. But it has weakness that it does not properly handle scene-changed pictures. Therefore picture quality after scene change is deteriorated, and possibility of overflow occurrence becomes high. In this paper, a new method for detection of scene change occurrence using local variance and a new determination scheme for adaptive quantization parameter, mqunt, which can consider local characteristic of an image by using previously computed the local variance from the scene change detection part are proposed. IN addition, and adaptive rate control scheme which can handles scene changed picture very efficiently by scene-changed picture is proposed. Computer simulations are performed to verify the performance of the proposed algorithm. The suggested detection algorithm precisely detected scene change. And the proposed rate control scheme shows better rate control performance as compared with that of the conventional MPEG scheme.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.4B
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• pp.353-364
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• 2002

In this paper, a new onto-digital stereo object-tracking system using hierarchical digital algorithms and optical BPEJTC is proposed. This proposed system can adaptively track a moving target by controlling the convergence of stereo camera. firstly, the target is detected through the background matching of the sequential input images by using optical BPEJTC and then the target area is segmented by using the target projection mask which is composed by hierarchical digital processing of image subtraction, logical operation and morphological filtering. Secondly, the location's coordinate of the moving target object for each of the sequential input frames can be extracted through carrying out optical BPEJTC between the reference image of the target region mask and the stereo input image. Finally, the convergence and pan/tilt of stereo camera can be sequentially controlled by using these target coordinate values and the target can be kept in tracking. Also, a possibility of real-time implementation of the adaptive stereo object tracking system is suggested through optically implementing the proposed target extraction and convergence control algorithms.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.10
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• pp.108-117
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• 2015

If typical contrast enhancement algorithms for natural images are applied to X-ray images, they may cause artifacts such as overshooting or produce unnatural visual quality because they do not consider inherent characteristics of X-ray images. In order to overcome such problems, we propose a locally adaptive block-based contrast enhancement algorithm for X-ray images. After we derive a weighted cumulative distribution function for each block, we apply it to each block for contrast enhancement. Then, we obtain images that are removed from block effect by adopting block-based overlapping. In post-processing, we obtain the final image by emphasizing high frequency components. Experimental results show that the proposed block-based contrast enhancement algorithm provides at maximum 5-times higher visual quality than the exiting algorithm in terms of quantitative contrast metric.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.4C
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• pp.277-282
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• 2012

In this paper, we present a real-time eye contact system for realistic telepresence using a Kinect depth camera. In order to generate the eye contact image, we capture a pair of color and depth video. Then, the foreground single user is separated from the background. Since the raw depth data includes several types of noises, we perform a joint bilateral filtering method. We apply the discontinuity-adaptive depth filter to the filtered depth map to reduce the disocclusion area. From the color image and the preprocessed depth map, we construct a user mesh model at the virtual viewpoint. The entire system is implemented through GPU-based parallel programming for real-time processing. Experimental results have shown that the proposed eye contact system is efficient in realizing eye contact, providing the realistic telepresence.