• Journal of information and communication convergence engineering
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• v.10 no.4
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• pp.378-383
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• 2012

This paper presents the implementation of an automatic road sign recognizer for an intelligent transport system. In this system, lists of road signs are processed with actions such as line segmentation, single sign segmentation, and storing an artificial sign in the database. The process of taking the video stream and extracting the road sign and storing in the database is called the road sign recognition. This paper presents a study on recognizing traffic sign patterns using a segmentation technique for the efficiency and the speed of the system. The image is converted from one scale to another scale such as RGB to grayscale or grayscale to binary. The images are pre-processed with several image processing techniques, such as threshold techniques, Gaussian filters, Canny edge detection, and the contour technique.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1997.06a
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• pp.19-24
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• 1997

This paper describes an image segmentation technique for the object-oriented coding at very low bit rates. By noting that, in the object-oriented coding technique, each objects are represented by 3 parameters, namely, shape, motion, and color informations, we propose a segmentation technique, in which the 3 parameters are fully exploited. To achieve this goal, starting with the color space conversion and the noise reduction, the input image is divided into many small regions by the K-menas algorithm on the O-K-S color space. Then, each regions are merged, according to the shape and motion information. In simultations, it is shown that the proposed technique segments the input image into relevant objects, according to the shape and motion as well as the colors. In addition, in order to evaluate the performance of the proposed technique, we introduce the notion of the interesting regions, and provide the results of encoding the image with emphasizing the interesting regions.

• Journal of the Semiconductor & Display Technology
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• v.18 no.3
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• pp.1-6
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• 2019

Foreground/background segmentation in video sequences is often one of the first tasks in machine vision applications, making it a critical part of the system. In this paper, we present an improved sample-based technique that provides robust background image as well as segmentation mask. The conventional multiple interval sampling (MIS) algorithm have suffer from the unbalance of computation time per frame and the rapid change of confidence factor of background pixel. To balance the computation amount, a random-based pixel update scheme is proposed and a spatial and temporal smoothing technique is adopted to increase reliability of the confidence factor. The proposed method allows the sampling queue to have more dispersed data in time and space, and provides more continuous and reliable confidence factor. Experimental results revealed that our method works well to estimate stable background image and the foreground mask.

• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.372-378
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• 2015

Background subtraction is the first processing stage in video surveillance. It is a general term for a process which aims to separate foreground objects from a background. The goal is to construct and maintain a statistical representation of the scene that the camera sees. The output of background subtraction will be an input to a higher-level process. Background subtraction under dynamic environment in the video sequences is one such complex task. It is an important research topic in image analysis and computer vision domains. This work deals background modeling based on modified adaptive Gaussian mixture model (GMM) with three temporal differencing (TTD) method in dynamic environment. The results of background subtraction on several sequences in various testing environments show that the proposed method is efficient and robust for the dynamic environment and achieves good accuracy.

• Journal of the Korean Society for information Management
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• v.34 no.1
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• pp.219-239
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• 2017

As IT technology develops rapidly and the personal dissemination of smart devices increases, video material is especially used as a medium of information transmission among audiovisual materials. Video as an information service content has become an indispensable element, and it has been used in various ways such as unidirectional delivery through TV, interactive service through the Internet, and audiovisual library borrowing. Especially, in the Internet environment, the information provider tries to reduce the effort and cost for the processing of the provided information in view of the video service through the smart device. In addition, users want to utilize only the desired parts because of the burden on excessive network usage, time and space constraints. Therefore, it is necessary to enhance the usability of the video by automatically classifying, summarizing, and indexing similar parts of the contents. In this paper, we propose a method of automatically segmenting the shots that make up videos by analyzing the contents and characteristics of language education videos and indexing the detailed contents information of the linguistic videos by combining visual features. The accuracy of the semantic based shot segmentation is high, and it can be effectively applied to the summary service of language education videos.

• Journal of Korea Multimedia Society
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• v.14 no.10
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• pp.1252-1261
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• 2011

In this paper, we propose the comparison method of pattern similarity for video segmentation algorithm. The shot boundary type is categorized as 2 types, abrupt change and gradual change. The representative examples of gradual change are dissolve, fade-in, fade-out or wipe transition. The proposed method consider the problem to detect shot boundary as 2-class problem. We concentrated if the shot boundary event happens or not. It is essential to define similarity between frames for shot boundary detection. We proposed 2 similarity measures, within similarity and between similarity. The within similarity is defined by feature comparison between frames belong to same shot. The between similarity is defined by feature comparison between frames belong to different scene. Finally we calculated the statistical patterns comparison between the within similarity and between similarity. Because this measure is robust to flash light or object movement, our proposed algorithm make contribution towards reducing false positive rate. We employed color histogram and mean of sub-block on frame image as frame feature. We performed the experimental evaluation with video dataset including set of TREC-2001 and TREC-2002. The proposed algorithm shows the performance, 91.84% recall and 86.43% precision in experimental circumstance.

• Journal of KIISE:Software and Applications
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• v.29 no.1_2
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• pp.114-125
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• 2002

In this paper, we are proposing a hierarchical segmentation method that first segments the video data into units of shots by detecting cut and dissolve, and then decides types of camera operations or object movements in each shot. In our previous work[1], each picture group is divided into one of the three detailed categories, Shot(in case of scene change), Move(in case of camera operation or object movement) and Static(in case of almost no change between images), by analysing DC(Direct Current) component of I(Intra) frame. In this process, we have designed two-stage hierarchical neural network with inputs of various multiple features combined. Then, the system detects the accurate shot position, types of camera operations or object movements by searching P(Predicted), B(Bi-directional) frames of the current picture group selectively and hierarchically. Also, the statistical distributions of macro block types in P or B frames are used for the accurate detection of cut position, and another neural network with inputs of macro block types and motion vectors method can reduce the processing time by using only DC coefficients of I frames without decoding and by searching P, B frames selectively and hierarchically. The proposed method classified the picture groups in the accuracy of 93.9-100.0% and the cuts in the accuracy of 96.1-100.0% with three different together is used to detect dissolve, types of camera operations and object movements. The proposed types of video data. Also, it classified the types of camera movements or object movements in the accuracy of 90.13% and 89.28% with two different types of video data.

• ETRI Journal
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• v.35 no.6
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• pp.1105-1114
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• 2013

Owing to the large number of video programs available, a method for accessing preferred videos efficiently through personalized video summaries and clips is needed. The automatic recognition of user states when viewing a video is essential for extracting meaningful video segments. Although there have been many studies on emotion recognition using various user responses, electroencephalogram (EEG)-based research on preference recognition of videos is at its very early stages. This paper proposes classification models based on linear and nonlinear classifiers using EEG features of band power (BP) values and asymmetry scores for four preference classes. As a result, the quadratic-discriminant-analysis-based model using BP features achieves a classification accuracy of 97.39% (${\pm}0.73%$), and the models based on the other nonlinear classifiers using the BP features achieve an accuracy of over 96%, which is superior to that of previous work only for binary preference classification. The result proves that the proposed approach is sufficient for employment in personalized video segmentation with high accuracy and classification power.

• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.1004-1012
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• 2020

We present a novel content-preserving seam estimation algorithm for real-time high-resolution video stitching. Seam estimation is one of the fundamental steps in image/video stitching. It is to minimize visual artifacts in the transition areas between images. Typical seam estimation algorithms are based on optimization methods that demand intensive computations and large memory. The algorithms, however, often fail to avoid objects and results in cropped or duplicated objects. They also lack temporal consistency and induce flickering between frames. Hence, we propose an efficient and temporarily-consistent seam estimation algorithm that utilizes a straight line. The proposed method also uses convolutional neural network-based instance segmentation to locate seam at out-of-objects. Experimental results demonstrate that the proposed method produces visually plausible stitched videos with minimal visual artifacts in real-time.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.4
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• pp.121-126
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• 2009

Video poxy server which is located near clients can store the frequently requested video data in storage space in order to minimize initial latency and network traffic significantly. However, due to the limited storage space in video proxy server, an appropriate deletion algorithm is needed to remove the old video data which is not serviced for a long time. Thus, we propose an efficient video data deletion algorithm for video proxy server. The proposed deletion algorithm removes the video which has the lowest request possibility based on the user access patterns. In our algorithm, we arrange the videos which are stored in video proxy server according to the requested time sequence and then, select the video which has the oldest requested time. The selected video is partially removed in order to free up storage space in video poky server. The simulation results show that the proposed algorithm performs better than other algorithms in terms of the block hit rate and the number of block deletion.