• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.169-177
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• 2014

It is essential to support the image and video analysis technology such as motion detection since the DVR and NVR storage were adopted in the real time visual surveillance system. Especially the network camera would be popular as a video input device. The traditional CCTV that supports analog video data get be replaced by the network camera. In this paper, we present the design and implementation of video surveillance system that provides the real time motion detection by the video storage server. The mobile application also has been implemented in order to provides the retrieval functionality of image analysis results. We develop the video analysis server with open source library OpenCV and implement the daemon process for video input processing and real-time image analysis in our video surveillance system.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.14 no.11
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• pp.1156-1160
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• 2003

Many time-frequency analysis techniques have been used for motion compensated ISAR(Inverse Synthetic Aperture Radar) imaging. In this work, a novel time-frequency(T-F) analysis called evolutionary adaptive wavelet transform (EAWT) and evolutionary adaptive joint time-frequency(EAJTF) procedure are used for the motion compensated ISAR image. To show the validity of our algorism, we use simulated MIG-25 and Boeing 727(B-727) ISAR data. From the constructed ISAR image using EAWT and EAJTF, we show that our algorithm can obtain a clear motion compensated ISAR image such as other time-frequency analysis techniques.

• 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.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.6
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• pp.44-56
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• 2008

Previous hardware devices to capture human motion have many limitations; expensive equipment, complexity of manipulation or constraints of human motion. In order to overcome these problems, real-time motion capture algorithms based on computer vision have been actively proposed. This paper presents an efficient analysis method of multiple view images for real-time motion capture. First, we detect the skin color regions of human being, and then correct the image coordinates of the regions by using camera calibration and epipolar geometry. Finally, we track the human body part and capture human motion using kalman filter. Experimental results show that the proposed algorithm can estimate a precise position of the human body.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1408-1411
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• 2002

In this paper, we propose a method that converts motion vectors on MPEG coded domain as a uniform set, independent of the frame type and the direction of prediction, and directly utilizes these normalized motion vectors for understanding video contents. This frame-type-independent motion vectors are utilized as feature information for image retrieval or moving object tracking on compressed domain. By simulation, we evaluate the effectiveness of the proposed method and compare its performance to the conventional method.

• Proceedings of the Korea Contents Association Conference
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• 2003.11a
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• pp.181-184
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• 2003

In the past, the image processing system is independently implemented and has a limit in its application to a degree of simple display. The scope of present image processing system is diversely extended in its application owing to the development of image processing It chips. In this paper, we implement the image processing system operated independently without PC by converting analogue image signals into digital signals. In the proposed image processing system, we extract the motion parameters from analogue image signals and generate the virtual movement to Simulator. Also we analysis the image control algorithm and operate Simulator by extracting motion parameters.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.47-51
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• 2005

Using satellite images, an optimal solution to water motion has been presented in this study. Since temperature patterns are suitable tracers in water motion, Sea Surface Temperature (SST) images of Caspian Sea taken by MODIS sensor on board Terra satellite have been used in this study. Two daily SST images with 24 hours time interval are used as input data. Computation of templates correspondence between pairs of images is crucial within motion algorithms using non-rigid body objects. Image matching methods have been applied to estimate water body motion within the two SST images. The least squares matching technique, as a flexible technique for most data matching problems, offers an optimal spatial solution for the motion estimation. The algorithm allows for simultaneous local radiometric correction and local geometrical image orientation estimation. Actually, the correspondence between the two image templates is modeled both geometrically and radiometrically. Geometric component of the model includes six geometric transformation parameters and radiometric component of the model includes two radiometric transformation parameters. Using the algorithm, the parameters are automatically corrected, optimized and assessed iteratively by the least squares algorithm. The method used in this study, has presented more efficient and robust solution compared to the traditional motion estimation schemes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.1
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• pp.84-95
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• 2009

The ubiquitous smart home is the home of the future, which exploits context information from both the human and the home environment, providing an automatic home service for the human. Human location and motion are the most important contexts in the ubiquitous smart home. In this paper, we present a real-time human tracker that predicts human location and motion for the ubiquitous smart home. The system uses four network cameras for real-time human tracking. This paper explains the architecture of the real-time human tracker, and proposes an algorithm for predicting human location and motion. To detect human location, three kinds of images are used: $IMAGE_1$ - empty room image, $IMAGE_2$ - image of furniture and home appliances, $IMAGE_3$ - image of $IMAGE_2$ and the human. The real-time human tracker decides which specific furniture or home appliance the human is associated with, via analysis of three images, and predicts human motion using a support vector machine (SVM). The performance experiment of the human's location, which uses three images, lasted an average of 0.037 seconds. The SVM feature of human motion recognition is decided from the pixel number by the array line of the moving object. We evaluated each motion 1,000 times. The average accuracy of all types of motion was 86.5%.

• The Korean Journal of Nuclear Medicine
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• v.24 no.2
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• pp.244-253
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• 1990

Sixty-four patients with paradoxical ventricular wall motion noticed both in angiocardiography or 2-dimensional echocardiography were assessed by ECG gated blood pool scan (GBPS). Endless cine loop image, phase and amplitude images and paradox image obtained by visual inspection of each cardiac beat or Fourier transformation of acquired raw data were investigated to determine the incremental value of GBPS with these processing methods for identification of paradoxical ventricular wall motion. The results were as follows: 1) Paradoxical wall motions were observed on interventricular septum in 34 cases, left ventricular free wall in 26 and right ventricular wall in 24. Underlying heart diseases were ischemic (23 cases) valvular(9), congenital heart disease (12), cardiomyopathy (5). pericardial effusion(5), post cardiac surgery(3), col pulmonale (2), endocarditis(1) and right ventricular tumor(1). 2) Left ventricular ejection fractions of patients with paradoxical left ventricular wall motion were significantly lower than those with paradoxical septal motion(p<0.005). 3) The sensitivity of each processing methods for detecting paradoxical wall motion was 76.9% by phase analysis, 74.6% by endless cine loop mapping and 68.4% by paradox image manipultion respectively. Paradoxial motions visualized only in phase, paradox or both images were appeared as hypokinesia or akinesia in cine loop image. 4) All events could be identified by at least one of above three processing methods, however only 34 cases (48.4%) showed the paradoxical molies in all of the three images. By these findings, we concluded that simultaneous inspection of all above three processing methods-endless cine loop, phase analysis and paradox image-is necessary for accurate identification and assessment of paradoxical ventricular wall motion when performing GBPS.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.429-431
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• 2002

Metabolic analysis of biological tissues, the interventional radiology in MRT (Magnetic Resonance Treatment) and for clinical diagnoses, representation of 4-Dimensional (4D) structural information (x,y,z,t) of biological tissues is required. This paper discusses image representation techniques for those 4D MR Images. We have proposed an image reconstruction method for ultra-fast 3D MRI. It is based on image interpolation and prediction of un-acquired pictorial data in both of the real and the k-space (the acquisition domain in MRI). A 4D MR image is reconstructed from only two 3D MR images and acquired a few echo signals that are optimized by prediction of the tissue motion. This prediction can be done by the phase of acquired echo signal is proportioned to the tissue motion. On the other hand, reconstructed 4D MR images are represented as a 3D-movie by using computer graphics techniques. Rendered tissue surfaces and/or ROIs are displayed on a CRT monitor. It is represented in an arbitrary plane and/or rendered surface with their motion. As examples of the proposed representation techniques, the finger and the lung motion of healthy volunteers are demonstrated.