• Journal of Broadcast Engineering
• /
• v.2 no.2
• /
• pp.146-155
• /
• 1997

The new video coding standard Iv1PEG-4 is enabling content-based functionalities. It requires a prior decomposition of sequences into video object planes (VOP's) so that each VOP represents moving objets. This paper addresses an image segmentation method for separating moving objects from still background (non-moving area) in video sequences using a statistical hypothesis test. In the proposed method. three consecutive image frames are exploited and a hypothesis testing is performed by comparing two means from two consecutive difference images. which results in a T-test. This hypothesis test yields a change detection mask that indicates moving areas (foreground) and non-moving areas (background), Moreover. an effective method for extracting

• Journal of the Institute of Convergence Signal Processing
• /
• v.2 no.3
• /
• pp.86-92
• /
• 2001

Position controls are very important in semiconductor manufacturing devices, machine tools, precision measuring instruments, etc. to measure the distance of movement of moving objects in minute units and the accuracy of measurement for the moving distance in these devices affect the performance of the whole devices. Therefore, in those precision instruments, a sensing device that can measure the distance of movement with high-precision resolution is required. Thus an optical encoder that has such advantages as easy digital interface, economical price, and a resolution similar to that of laser interferometers can be used. In this paper, a interferometer-type linear scale with easy digital interface and high-resolution has been set up and measured the distance of movement based on the diffraction principle. Interference signals produced in this optical setup of the linear scale have beers digitalized through fabricated photodetectors and designed signal processing circuits. A resolution of 0.5${\mu}{\textrm}{m}$ is acquired from the experimental interferometer-type linear scale without for the movement of scales any additional dividing circuits. It is shown that from this experiment a high-resolution distance measurement device can be designed by a simple optical setup.

• Proceedings of the KSR Conference
• /
• 2011.10a
• /
• pp.1966-1972
• /
• 2011

Optical flow estimation based on multi constraint approaches is frequently used for recognition of moving objects. This paper proposed the method to monitor passenger boarding using image processing when a train is operated based on Automatic Train Operation(ATO). The movement of passenger can be detected to compare two images, one is a basic image and another is immediately captured by CCTV. Optical Flow helps to find the movement of passenger when two images are compared. The movement of passenger is one of important informations for ATO system because it needs to decide door status.

• Journal of Broadcast Engineering
• /
• v.19 no.2
• /
• pp.166-173
• /
• 2014

Recently, human action recognition have been developed for various broadcasting and video process. Since a video can consist of various scenes, keypoint approaches have been more attracted than template based methods for real application. Keypoint approahces tried to find regions having motion in video, and made 3-dimensional patches. Then, descriptors using histograms were computed from the patches, and a classifier based on machine learning method was applied to detect actions in video. However, a single classifier was difficult to handle various human actions. In order to improve this problem, approaches using multi classifiers were used to detect and to recognize objects. Thus, we propose a new human action recognition using decision-level fusion with support vector machine and sparse representation. The proposed method extracted descriptors based on keypoint approach from a video, and acquired results from each classifier for human action recognition. Then, we applied weights which were acquired by training stage to fuse each results from two classifiers. The experiment results in this paper show better result than a previous fusion method.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.12 no.6
• /
• pp.78-86
• /
• 2013

In this paper, we propose design of a high-performance vehicle imaging information system for an efficient vehicle imaging stabilization. The proposed system was designed the algorithm by divided as motion estimation and motion compensation. The motion estimation were configured as local motion vector estimation and irregular local motion vector detection, global motion vector estimation. The motion compensation was corrected for the four directions for compensate to the shake of vehicle video image using estimate GMV. The designed algorithm were designed the motion compensation technology chip by applied to IP for vehicle imaging stabilization. In this paper, the experimental results of the proposed vehicle imaging information system were proved to the effectiveness by compared with other methods, because imaging stabilization of moving vehicle was not used of memory by processing real-time. Also, it could be obtained to reduction effect of calculation time by arithmetic operation through to block matching.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.40 no.12
• /
• pp.2363-2371
• /
• 2015

In this paper, we propose a new feature matching algorithm which is robust to the viewpoint change by using the FAST(Features from Accelerated Segment Test) feature detector and the SIFT(Scale Invariant Feature Transform) feature descriptor. The original FAST algorithm unnecessarily results in many feature points along the edges in the image. To solve this problem, we apply the principal curvatures for refining it. We use the SIFT descriptor to describe the extracted feature points and calculate the homography matrix through the RANSAC(RANdom SAmple Consensus) with the matching pairs obtained from the two different viewpoint images. To make feature matching robust to the viewpoint change, we classify the matching pairs by calculating the Euclidean distance between the transformed coordinates by the homography transformation with feature points in the reference image and the coordinates of the feature points in the different viewpoint image. Through the experimental results, it is shown that the proposed algorithm has better performance than the conventional feature matching algorithms even though it has much less computational load.

• Journal of Broadcast Engineering
• /
• v.22 no.6
• /
• pp.724-733
• /
• 2017

Conventional methods of automatically recording Go game positions do not properly consider obstacles (hand or object) on a Go board during the Go game. If the Go board is blocked by obstacles, the position of a Go stone may not be correctly recognized, or the sequences of moves may be stored differently from the actual one. In the proposed algorithm, only the complete Go board image without obstacles is stored as a background image and the obstacle is recognized by comparing the background image with the current input image. To eliminate the phenomenon that the shadow is mistaken as obstacles, this paper proposes the new obstacle detection method based on the gradient image instead of the simple differential image. When there is no obstacle on the Go board, the background image is updated. Finally, the successive background images are compared to recognize the position and type of the Go stone. Experimental results show that the proposed algorithm has more than 95% recognition rate in general illumination environment.

• The KIPS Transactions:PartB
• /
• v.9B no.3
• /
• pp.327-336
• /
• 2002

Texture analysis is an important technique in many image understanding areas, such as perception of surface, object, shape and depth. But the previous works are intend to the issue of only texture segment, that is not capable of acquiring recognition information. No unsupervised method is basased on the recognition of texture in image. we propose a novel approach for efficient texture image analysis that uses unsupervised learning schemes for the texture recognition. The self-organization neural network for multiple texture image identification is based on block-based clustering and merging. The texture features used are the angle and magnitude in orientation-field that might be different from the sample textures. In order to show the performance of the proposed system, After we have attempted to build a various texture images. The final segmentation is achieved by using efficient edge detection algorithm applying to block-based dilation. The experimental results show that the performance of the system Is very successful.

• Journal of Korea Multimedia Society
• /
• v.12 no.4
• /
• pp.473-482
• /
• 2009

Reconstructed 3D data from computer vision includes necessarily a noise or an error. When these data goes through a mesh process, the different 3D mesh data from original shape comes to make by a noise or an error. This paper proposed the method that smooths a noise effectively by noise analysis in reconstructed 3D data. Because the proposed method is smooths a noise using the area ratio of the mesh, the pre-processing of unusable mesh is necessary in 3D mesh data. This study detects a peak noise and Gaussian noise using the ratio of 3D volume and 2D area of mesh and smooths the noise with respect of its characteristics. The experimental results using synthetic and real data demonstrated the efficacy and performance of proposed algorithm.

• The Journal of the Korea Contents Association
• /
• v.11 no.4
• /
• pp.67-73
• /
• 2011

The image retrieval generally shows the same or similar images to a query image as a result. In the case of rotated image, however, its performance tends to be debased significantly. We propose a method to ensure a reliable image retrieval of rotated images as follows; First, to obtain feature points of query/DB images by Harris Corner Detector; and then, utilizing the feature points, to find the object's axis and query/DB images into rotation invariant images with Principal Components Analysis algorithm. We have experimented with 6,000 natural images which are 256 pixels in diameter. They are 1,000 Wang's images and their rotated images by $30^{\circ}$, $45^{\circ}$, $90^{\circ}$, $135^{\circ}$ and $180^{\circ}$. The simulation results show that the proposed method retrieves rotated images more effectively than the conventional method.