• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2011.07a
• /
• pp.437-438
• /
• 2011

Spectral segmentation is a major trend in image segmentation. Specially, constrained spectral segmentation, inspired by the user-given inputs, remains its challenging task. Since it makes use of the spectrum of the affinity matrix of a given image, its overall quality depends mainly on how to design the graphical model. In this work, we propose a sparse, multi-layer graphical model, where the pixels and the over-segmented regions are the graph nodes. Here, the graph affinities are computed by using the must-link and cannot-link constraints as well as the likelihoods that each node has a specific label. They are then used to simultaneously cluster all pixels and regions into visually coherent groups across all layers in a single multi-layer framework of Normalized Cuts. Although we incorporate only the adjacent connections in the multi-layer graph, the foreground object can be efficiently extracted in the spectral framework. The experimental results demonstrate the relevance of our algorithm as compared to existing popular algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.17 no.3
• /
• pp.712-717
• /
• 2013

In this paper, we propose a performance-enhanced object recognition by using nonlinear 3D correlator based on pixel restoration. In the proposed method, elemental images of the 3D target that are partially occluded by a foreground object are picked up and transformed into sub-images. By using the block-matching algorithm, the occluded target regions of each sub-image are estimated and removed. After that, the missing pixels in each sub-image are reestablished by using the pixel-restoration method. Finally, through the nonlinear cross-correlations between the reconstructed reference and the target plane images, the improved object recognition can be performed. To show the feasibility of the proposed method, some preliminary experiments are carried out and results are presented by comparing the conventional method.

• Journal of Broadcast Engineering
• /
• v.4 no.2
• /
• pp.176-186
• /
• 1999

In order to produce an image that lends realism to audience in the virtual studio system. it is important to synchronize precisely between foreground objects and background image provided by computer graphics. In this paper, we propose a method of camera parameter extraction for the synchronization by tracking the pose of TV camera. We derive an equation for extracting camera parameters from inverse perspective equations for tracking the pose of the camera and 3-D transformation between base coordinates and estimated coordinates. We show the validity of the proposed method in terms of the accuracy ratio between the parameters computed from the equation and the real parameters that applied to a TV camera.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2011.10a
• /
• pp.157-160
• /
• 2011

In this paper, We proposed the method of recognition and analysis of human activites using Motion vector in real-time surveillance system. We employs subtraction image techniques to detect blob(human) in the foreground. When MPEG-4 video recording EPZS(Enhanced Predicted Zonal Search) is detected the values of motion vectors were used. In this paper, the activities of human recognize and classified such as meta-classes like this {Active, Inactive}, {Moving, Non-moving}, {Walking, Running}. Each step was separated using a step-by-step threshold values. We created approximately 150 conditions for the simulation. As a result, We showed a high success rate about 86~98% to distinguish each steps in simulation image.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.53 no.2
• /
• pp.53-61
• /
• 2016

Extracting focused object with saliency map is still remaining as one of the most highly tasked research area around computer vision for it is hard to estimate. Through this paper, we propose enhanced object extraction method based on multi-channel saliency map which could be done automatically without machine learning. Proposed Method shows a higher accuracy than Itti method using SLIC, Euclidean, and LBP algorithm as for object extraction. Experiments result shows that our approach is possible to be used for automatic object extraction without any previous training procedure through focusing on the main object from the image instead of estimating the whole image from background to foreground.

• Journal of the Korea Society of Computer and Information
• /
• v.23 no.11
• /
• pp.51-57
• /
• 2018

In this paper, we studied about the extraction of the parameter and implementation of speechreading system to recognize the Korean 8 vowel. Face features are detected by amplifying, reducing the image value and making a comparison between the image value which is represented for various value in various color space. The eyes position, the nose position, the inner boundary of lip, the outer boundary of upper lip and the outer line of the tooth is found to the feature and using the analysis the area of inner lip, the hight and width of inner lip, the outer line length of the tooth rate about a inner mouth area and the distance between the nose and outer boundary of upper lip are used for the parameter. 2400 data are gathered and analyzed. Based on this analysis, the neural net is constructed and the recognition experiments are performed. In the experiment, 5 normal persons were sampled. The observational error between samples was corrected using normalization method. The experiment show very encouraging result about the usefulness of the parameter.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2007.04a
• /
• pp.315-319
• /
• 2007

This paper presents a algorithm, what is able to recognize 4 different continuous human motion using a single stationary camera as input. For the first step, we acquire images from a camera. To enhance the image, we perform preprocessing which deals with removing noise using median filter, thresholding. And then morphological operations are performed to remove which small blobs and eliminates small holes. At the forth step, blobs are analysed to extracts for foreground region. Then, motions are predicted from these images by using optical flow technique, and the predicted motion data are refined by comparing our cardboard models so as to judge behavior pattern.

• Journal of Institute of Control, Robotics and Systems
• /
• v.16 no.8
• /
• pp.766-770
• /
• 2010

In this paper, we present an effective covariance tracking algorithm based on adaptive size changing of tracking window. Recent researches have advocated the use of a covariance matrix of object image features for tracking objects instead of the conventional histogram object models used in popular algorithms. But, according to the general covariance tracking algorithm, it can not deal with the scale changes of the moving objects. The scale of the moving object often changes in various tracking environment and the tracking window(or object kernel) has to be adapted accordingly. In addition, the covariance matrix of moving objects should be adaptively updated considering of the tracking window size. We provide a solution to this problem by segmenting the moving object from the background pixels of the tracking window. Therefore, we can improve the tracking performance of the covariance tracking method. Our several simulations prove the effectiveness of the proposed method.

• The Bulletin of The Korean Astronomical Society
• /
• v.35 no.1
• /
• pp.70.1-70.1
• /
• 2010

We present the far-ultraviolet (FUV) emission line morphologies in the whole region of the supernova remnant G65.3+5.7 using the FIMS/SPEAR data. The morphologies of the C IV ${\lambda}{\lambda}1548$, 1551, $H2{\lambda}1608 $, He II ${\lambda}1640$, and O III] ${\lambda}{\lambda}1661$, 1666 lines appear to correlate clearly with the optical emission line images or the ROSAT X-ray (0.11-0.284 keV) image obtained in some of the previous studies. We found that a foreground dust cloud, resonant scattering, and incompleteness of radiative shocks have made the definite differences between the morphologies of the above FUV emission lines. We also present the FUV spectra and line intensities from a few sub-regions.

• The Bulletin of The Korean Astronomical Society
• /
• v.43 no.1
• /
• pp.67.1-67.1
• /
• 2018

We present high-resolution near infrared (IR) spectra of two candidate planetary nebulae (PNe) that were serendipitously found toward the Galactic center (GC). Our spectra obtained using GNIRS on Gemini North reveal strong Br ♑ and He I recombination lines. In one of the targets, we confidently detect Pa ♌ emission. Based on Br ♑ and Pa ♌ lines, we estimate a foreground reddening to be Av=27 mag, which confidently puts this object at the GC distance. Along with the presence of highly excited emission lines such as [S IV], [Ne III], [Ne V], and [O IV] detected in the mid-IR spectra from the Spitzer Space Telescope, and the extended emission in the Pa ♋ narrow-band image from the Hubble Space Telescope, this makes it the first spectroscopically confirmed PN in the GC.