• Title/Summary/Keyword: computer vision systems

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Histogram Modification based on Additive Term and Gamma Correction for Image Contrast Enhancement (영상의 대비 개선을 위한 추가 항과 감마 보정에 기반한 히스토그램 변형 기법)

  • Kim, Jong-Ho
    • The Journal of the Korea institute of electronic communication sciences
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    • v.13 no.5
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    • pp.1117-1124
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    • 2018
  • Contrast enhancement plays an important role in various computer vision systems, since their usability can be improved with visibility enhancement of the images affected by weather and lighting conditions. This paper introduces a histogram modification algorithm that reflects the properties of original images in order to eliminate the saturation effect and washed-out of image details due to the over-enhancement. Our method modifies the original histogram so that an additive term fill histogram pits and the gamma correction suppresses histogram spikes. The parameters for the additive term and gamma correction are adjusted automatically according to statistical properties of the images. Experimental results for various low contrast and hazy images demonstrate that the proposed contrast enhancement improves visibility and reduces haze components effectively, while preserving the characteristics of original images, than the conventional methods.

A Method of Adative Background Image Generation for Object Tracking (객체 추적을 위한 적응적 배경영상 생성 방법)

  • Jee, Jeong-Gyu;Lee, Kwang-Hyoung;Kim, Yong-Gyun;Oh, Hae-Seok
    • The KIPS Transactions:PartB
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    • v.10B no.3
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    • pp.329-338
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    • 2003
  • Object tracking in a real time image is one of Interesting subjects in computer vision and many practical application fields past couple of years. But sometimes existing systems cannot find object by recognize background noise as object. This paper proposes a method of object detection and tracking using adaptive background image in real time. To detect object which does not influenced by illumination and remove noise in background image, this system generates adaptive background image by real time background image updating. This system detects object using the difference between background image and input image from camera. After setting up MBR(minimum bounding rectangle) using the internal point of detected object, the system tracks object through this MBR. In addition, this paper evaluates the test result about performance of proposed method as compared with existing tracking algorithm.

Posture Recognition for a Bi-directional Participatory TV Program based on Face Color Region and Motion Map (시청자 참여형 양방향 TV 방송을 위한 얼굴색 영역 및 모션맵 기반 포스처 인식)

  • Hwang, Sunhee;Lim, Kwangyong;Lee, Suwoong;Yoo, Hoyoung;Byun, Hyeran
    • KIISE Transactions on Computing Practices
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    • v.21 no.8
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    • pp.549-554
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    • 2015
  • As intuitive hardware interfaces continue to be developed, it has become more important to recognize the posture of the user. An efficient alternative to adding expensive sensors is to implement computer vision systems. This paper proposes a method to recognize a user's postured in a live broadcast bi-directional participatory TV program. The proposed method first estimates the position of the user's hands by generation a facial color map for the user and a motion map. The posture is then recognized by computing the relative position of the face and the hands. This method exhibited 90% accuracy in an experiment to recognize three defined postures during the live broadcast bi-directional participatory TV program, even when the input images contained a complex background.

Unsupervised Motion Pattern Mining for Crowded Scenes Analysis

  • Wang, Chongjing;Zhao, Xu;Zou, Yi;Liu, Yuncai
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.6 no.12
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    • pp.3315-3337
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    • 2012
  • Crowded scenes analysis is a challenging topic in computer vision field. How to detect diverse motion patterns in crowded scenarios from videos is the critical yet hard part of this problem. In this paper, we propose a novel approach to mining motion patterns by utilizing motion information during both long-term period and short interval simultaneously. To capture long-term motions effectively, we introduce Motion History Image (MHI) representation to access to the global perspective about the crowd motion. The combination of MHI and optical flow, which is used to get instant motion information, gives rise to discriminative spatial-temporal motion features. Benefitting from the robustness and efficiency of the novel motion representation, the following motion pattern mining is implemented in a completely unsupervised way. The motion vectors are clustered hierarchically through automatic hierarchical clustering algorithm building on the basis of graphic model. This method overcomes the instability of optical flow in dealing with time continuity in crowded scenes. The results of clustering reveal the situations of motion pattern distribution in current crowded videos. To validate the performance of the proposed approach, we conduct experimental evaluations on some challenging videos including vehicles and pedestrians. The reliable detection results demonstrate the effectiveness of our approach.

Extraction of Workers and Heavy Equipment and Muliti-Object Tracking using Surveillance System in Construction Sites (건설 현장 CCTV 영상을 이용한 작업자와 중장비 추출 및 다중 객체 추적)

  • Cho, Young-Woon;Kang, Kyung-Su;Son, Bo-Sik;Ryu, Han-Guk
    • Journal of the Korea Institute of Building Construction
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    • v.21 no.5
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    • pp.397-408
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    • 2021
  • The construction industry has the highest occupational accidents/injuries and has experienced the most fatalities among entire industries. Korean government installed surveillance camera systems at construction sites to reduce occupational accident rates. Construction safety managers are monitoring potential hazards at the sites through surveillance system; however, the human capability of monitoring surveillance system with their own eyes has critical issues. A long-time monitoring surveillance system causes high physical fatigue and has limitations in grasping all accidents in real-time. Therefore, this study aims to build a deep learning-based safety monitoring system that can obtain information on the recognition, location, identification of workers and heavy equipment in the construction sites by applying multiple object tracking with instance segmentation. To evaluate the system's performance, we utilized the Microsoft common objects in context and the multiple object tracking challenge metrics. These results prove that it is optimal for efficiently automating monitoring surveillance system task at construction sites.

Adaptive V1-MT model for motion perception

  • Li, Shuai;Fan, Xiaoguang;Xu, Yuelei;Huang, Jinke
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.1
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    • pp.371-384
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    • 2019
  • Motion perception has been tremendously improved in neuroscience and computer vision. The baseline motion perception model is mediated by the dorsal visual pathway involving the cortex areas the primary visual cortex (V1) and the middle temporal (V5 or MT) visual area. However, few works have been done on the extension of neural models to improve the efficacy and robustness of motion perception of real sequences. To overcome shortcomings in situations, such as varying illumination and large displacement, an adaptive V1-MT motion perception (Ad-V1MTMP) algorithm enriched to deal with real sequences is proposed and analyzed. First, the total variation semi-norm model based on Gabor functions (TV-Gabor) for structure-texture decomposition is performed to manage the illumination and color changes. And then, we study the impact of image local context, which is processed in extra-striate visual areas II (V2), on spatial motion integration by MT neurons, and propose a V1-V2 method to extract the image contrast information at a given location. Furthermore, we take feedback inputs from V2 into account during the polling stage. To use the algorithm on natural scenes, finally, multi-scale approach has been used to handle the frequency range, and adaptive pyramidal decomposition and decomposed spatio-temporal filters have been used to diminish computational cost. Theoretical analysis and experimental results suggest the new Ad-V1MTMP algorithm which mimics human primary motion pathway has universal, effective and robust performance.

Hole Identification Method Based on Template Matching for the Ear-Pins Insertion Automation System (이어핀 삽입 자동화 시스템을 위한 템플릿 매칭 기반 삽입 위치 판별 방법)

  • Baek, Jonghwan;Lee, Jaeyoul;Jung, Myungsoo;Jang, Minwoo;Shin, Dongho;Seo, Kapho;Hong, Sungho
    • KIPS Transactions on Computer and Communication Systems
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    • v.10 no.1
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    • pp.7-14
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    • 2021
  • In jewelry industry, the proportion of labor costs is high. Also, the production time and quality of products are highly varied depending on the workers' capabilities. Therefore, there is a demand from the jewelry industry for automation. The ear pin insertion automation system is the robot automatically inserts the ear pins into the silicone mold, and this automated system require accurate and fast hole detection method. In this paper, we propose optimal binarization method and a template matching method that can be applied in the ear pin insertion automation system. Through the performance test, it was shown that the applied method has an accuracy of 98.5% and 0.5 seconds faster processing speed than the Otsu binarization method. So, this automation system can contribute to cost reduction, work time reduction, and productivity improvement.

Unmanned Enforcement System for Illegal Parking and Stopping Vehicle using Adaptive Gaussian Mixture Model (적응적 가우시안 혼합 모델을 이용한 불법주정차 무인단속시스템)

  • Youm, Sungkwan;Shin, Seong-Yoon;Shin, Kwang-Seong;Pak, Sang-Hyon
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.25 no.3
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    • pp.396-402
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    • 2021
  • As the world is trying to establish smart city, unmanned vehicle control systems are being widely used. This paper writes about an unmanned parking control system that uses an adaptive background image modeling method, suggesting the method of updating the background image, modeled with an adaptive Gaussian mixture model, in both global and local way according to the moving object. Specifically, this paper focuses on suggesting two methods; a method of minimizing the influence of a moving object on a background image and a method of accurately updating the background image by quickly removing afterimages of moving objects within the area of interest to be monitored. In this paper, through the implementation of the unmanned vehicle control system, we proved that the proposed system can quickly and accurately distinguish both moving and static objects such as vehicles from the background image.

CG/VR Image Super-Resolution Using Balanced Attention Mechanism (Balanced Attention Mechanism을 활용한 CG/VR 영상의 초해상화)

  • Kim, Sowon;Park, Hanhoon
    • Journal of the Institute of Convergence Signal Processing
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    • v.22 no.4
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    • pp.156-163
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    • 2021
  • Attention mechanisms have been used in deep learning-based computer vision systems, including single image super-resolution (SISR) networks. However, existing SISR networks with attention mechanism focused on real image super-resolution, so it is hard to know whether they are available for CG or VR images. In this paper, we attempt to apply a recent attention module, called balanced attention mechanism (BAM) module, to 12 state-of-the-art SISR networks, and then check whether the BAM module can achieve performance improvement in CG or VR image super-resolution. In our experiments, it has been confirmed that the performance improvement in CG or VR image super-resolution is limited and depends on data characteristics, size, and network type.

STAR-24K: A Public Dataset for Space Common Target Detection

  • Zhang, Chaoyan;Guo, Baolong;Liao, Nannan;Zhong, Qiuyun;Liu, Hengyan;Li, Cheng;Gong, Jianglei
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.16 no.2
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    • pp.365-380
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    • 2022
  • The target detection algorithm based on supervised learning is the current mainstream algorithm for target detection. A high-quality dataset is the prerequisite for the target detection algorithm to obtain good detection performance. The larger the number and quality of the dataset, the stronger the generalization ability of the model, that is, the dataset determines the upper limit of the model learning. The convolutional neural network optimizes the network parameters in a strong supervision method. The error is calculated by comparing the predicted frame with the manually labeled real frame, and then the error is passed into the network for continuous optimization. Strongly supervised learning mainly relies on a large number of images as models for continuous learning, so the number and quality of images directly affect the results of learning. This paper proposes a dataset STAR-24K (meaning a dataset for Space TArget Recognition with more than 24,000 images) for detecting common targets in space. Since there is currently no publicly available dataset for space target detection, we extracted some pictures from a series of channels such as pictures and videos released by the official websites of NASA (National Aeronautics and Space Administration) and ESA (The European Space Agency) and expanded them to 24,451 pictures. We evaluate popular object detection algorithms to build a benchmark. Our STAR-24K dataset is publicly available at https://github.com/Zzz-zcy/STAR-24K.