• Title/Summary/Keyword: 이미지 히스토그램

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Recognition of Car Plate using Gray Brightness Variation, HSI Information and Enhanced ART2 Algorithm (명암도 변화 및 HSI 정보와 개선된 ART2 알고리즘을 이용한 차량 번호판 인식)

  • 김광백;김영주
    • Journal of the Korean Institute of Intelligent Systems
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    • v.11 no.5
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    • pp.379-387
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    • 2001
  • We proposed an enhanced extraction method of vehicle plate, in which both the brightness variation of gray and the Hue value of HSI color model were used. For the extraction of the vehicle plate from a vehicle image, first of all, candidate regions for the vehicle plate were extracted from the image by using the property of brightness variation of the image. A real place region was determined among candidate regions by the density of pixels with the Hue value of green and white. For- extracting the feature area containing characters from the extracted vehicle plate, we used the histogram-based approach of individual characters. And we proposed and applied for the recognition of characters the enhanced ART2 algorithm which support the dynamical establishment of the vigilance threshold with the genera]iced union operator of Yager. In addition, we propose an enhanced SOSL algorithm which is integrated both enhanced ART2 and supervised learning methods. The performance evaluation was performed using 100's real vehicle images and the evaluation results demonstrated that the extraction rates of tole proposed extraction method were improved, compared with that of previous methods based un brightness variation, RGB and HSI individually . Furthermore, the recognition rates of the proposed algorithms were improved much more than that of the conventional ART2 and BP algorithms.

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Implementation of a Video Retrieval System Using Annotation and Comparison Area Learning of Key-Frames (키 프레임의 주석과 비교 영역 학습을 이용한 비디오 검색 시스템의 구현)

  • Lee Keun-Wang;Kim Hee-Sook;Lee Jong-Hee
    • Journal of Korea Multimedia Society
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    • v.8 no.2
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    • pp.269-278
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    • 2005
  • In order to process video data effectively, it is required that the content information of video data is loaded in database and semantics-based retrieval method can be available for various queries of users. In this paper, we propose a video retrieval system which support semantics retrieval of various users for massive video data by user's keywords and comparison area learning based on automatic agent. By user's fundamental query and selection of image for key frame that extracted from query, the agent gives the detail shape for annotation of extracted key frame. Also, key frame selected by user becomes a query image and searches the most similar key frame through color histogram comparison and comparison area learning method that proposed. From experiment, the designed and implemented system showed high precision ratio in performance assessment more than 93 percents.

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A Driver's Condition Warning System using Eye Aspect Ratio (눈 영상비를 이용한 운전자 상태 경고 시스템)

  • Shin, Moon-Chang;Lee, Won-Young
    • The Journal of the Korea institute of electronic communication sciences
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    • v.15 no.2
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    • pp.349-356
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    • 2020
  • This paper introduces the implementation of a driver's condition warning system using eye aspect ratio to prevent a car accident. The proposed driver's condition warning system using eye aspect ratio consists of a camera, that is required to detect eyes, the Raspberrypie that processes information on eyes from the camera, buzzer and vibrator, that are required to warn the driver. In order to detect and recognize driver's eyes, the histogram of oriented gradients and face landmark estimation based on deep-learning are used. Initially the system calculates the eye aspect ratio of the driver from 6 coordinates around the eye and then gets each eye aspect ratio values when the eyes are opened and closed. These two different eye aspect ratio values are used to calculate the threshold value that is necessary to determine the eye state. Because the threshold value is adaptively determined according to the driver's eye aspect ratio, the system can use the optimal threshold value to determine the driver's condition. In addition, the system synthesizes an input image from the gray-scaled and LAB model images to operate in low lighting conditions.

A Study on Rotational Alignment Algorithm for Improving Character Recognition (문자 인식 향상을 위한 회전 정렬 알고리즘에 관한 연구)

  • Jin, Go-Whan
    • Journal of the Korea Convergence Society
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    • v.10 no.11
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    • pp.79-84
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    • 2019
  • Video image based technology is being used in various fields with continuous development. The demand for vision system technology that analyzes and discriminates image objects acquired through cameras is rapidly increasing. Image processing is one of the core technologies of vision systems, and is used for defect inspection in the semiconductor manufacturing field, object recognition inspection such as the number of tire surfaces and symbols. In addition, research into license plate recognition is ongoing, and it is necessary to recognize objects quickly and accurately. In this paper, propose a recognition model through the rotational alignment of objects after checking the angle value of the tilt of the object in the input video image for the recognition of inclined objects such as numbers or symbols marked on the surface. The proposed model can perform object recognition of the rotationally sorted image after extracting the object region and calculating the angle of the object based on the contour algorithm. The proposed model extracts the object region based on the contour algorithm, calculates the angle of the object, and then performs object recognition on the rotationally aligned image. In future research, it is necessary to study template matching through machine learning.

Retinex-based Logarithm Transformation Method for Color Image Enhancement (컬러 이미지 화질 개선을 위한 Retinex 기반의 로그변환 기법)

  • Kim, Donghyung
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.19 no.5
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    • pp.9-16
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    • 2018
  • Images with lower illumination from the light source or with dark regions due to shadows, etc., can improve subjective image quality by using retinex-based image enhancement schemes. The retinex theory is a method that recognizes the relative lightness of a scene, rather than recognizing the brightness of the scene. The way the human visual system recognizes a scene in a specific position can be in one of several methods: single-scale retinex, multi-scale retinex, and multi-scale retinex with color restoration (MSRCR). The proposed method is based on the MSRCR method, which includes a color restoration step, which consists of three phases. In the first phase, the existing MSRCR method is applied. In the second phase, the dynamic range of the MSRCR output is adjusted according to its histogram. In the last phase, the proposed method transforms the retinex output value into the display dynamic range using a logarithm transformation function considering human visual system characteristics. Experimental results show that the proposed algorithm effectively increases the subjective image quality, not only in dark images but also in images including both bright and dark areas. Especially in a low lightness image, the proposed algorithm showed higher performance improvement than the conventional approaches.

Green Chroma Keying for Robot Performances in Public Places (공공장소에서 로봇 공연용 그린 크로마키 합성)

  • Hwang, Heesoo
    • Journal of the Korea Convergence Society
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    • v.8 no.7
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    • pp.7-13
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    • 2017
  • Robot performances in public places are conducted for the purpose of promoting robot technology and inducing interest in events, exhibitions, and streets instead of dedicated stages. This paper extracts robot images in real time from a robot operation in front of a green chroma key cloth, and synthesizes them on various stage images. A simple and robust method for extracting a foreground robot from a chroma key background without a user's preset is proposed. After increasing the color difference between the background and the foreground, this method automatically removes the background based on the histogram of the difference information, thereby eliminating the need for a user's preset. The simulation shows 98.8% of foreground extraction rate and experimental results demonstrate that the robots can effectively be extracted from the background.

Phase Segmentation of PVA Fiber-Reinforced Cementitious Composites Using U-net Deep Learning Approach (U-net 딥러닝 기법을 활용한 PVA 섬유 보강 시멘트 복합체의 섬유 분리)

  • Jeewoo Suh;Tong-Seok Han
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.36 no.5
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    • pp.323-330
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    • 2023
  • The development of an analysis model that reflects the microstructure characteristics of polyvinyl alcohol (PVA) fiber-reinforced cementitious composites, which have a highly complex microstructure, enables synergy between efficient material design and real experiments. PVA fiber orientations are an important factor that influences the mechanical behavior of PVA fiber-reinforced cementitious composites. Owing to the difficulty in distinguishing the gray level value obtained from micro-CT images of PVA fibers from adjacent phases, fiber segmentation is time-consuming work. In this study, a micro-CT test with a voxel size of 0.65 ㎛3 was performed to investigate the three-dimensional distribution of fibers. To segment the fibers and generate training data, histogram, morphology, and gradient-based phase-segmentation methods were used. A U-net model was proposed to segment fibers from micro-CT images of PVA fiber-reinforced cementitious composites. Data augmentation was applied to increase the accuracy of the training, using a total of 1024 images as training data. The performance of the model was evaluated using accuracy, precision, recall, and F1 score. The trained model achieved a high fiber segmentation performance and efficiency, and the approach can be applied to other specimens as well.

Documentation of Printed Hangul Images of the Selected Area by Finger Movement (손가락 이동에 의해 선택된 영역의 인쇄체 한글 영상 문서화)

  • Beak, Seung-Bok
    • Journal of the Korean Institute of Intelligent Systems
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    • v.12 no.4
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    • pp.306-310
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    • 2002
  • In this paper, we realized a system that converts the Korean alphabet (Hangul) images, which are in any domain that is formed by the finger movement on the Hangul document, to the editable characters and then outputs them to the word editor. The domain of hand is separated from the sphere of document in the pre-process step of image. The centroid point of hand is drawn by the maximum circular movement method. After the system recognizes the hand with the circular pattern vector algorithm, finds out the position of finger by the distance spectrum and then draws out the sphere of selected character image by the finger movement to divide the characters into character units by applying the histogram between the Hangul characters. We standardized the characters of various sizes. We used the circular pattern vector algorithm that grafts on the fuzzy inference to divert the character images of the domain, which user wants, to the editable characters by comparing the characteristic vectors between the standard pattern character and the inputted character and by recognizing the character.

Integrating Color, Texture and Edge Features for Content-Based Image Retrieval (내용기반 이미지 검색을 위한 색상, 텍스쳐, 에지 기능의 통합)

  • Ma Ming;Park Dong-Won
    • Science of Emotion and Sensibility
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    • v.7 no.4
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    • pp.57-65
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    • 2004
  • In this paper, we present a hybrid approach which incorporates color, texture and shape in content-based image retrieval. Colors in each image are clustered into a small number of representative colors. The feature descriptor consists of the representative colors and their percentages in the image. A similarity measure similar to the cumulative color histogram distance measure is defined for this descriptor. The co-occurrence matrix as a statistical method is used for texture analysis. An optimal set of five statistical functions are extracted from the co-occurrence matrix of each image, in order to render the feature vector for eachimage maximally informative. The edge information captured within edge histograms is extracted after a pre-processing phase that performs color transformation, quantization, and filtering. The features where thus extracted and stored within feature vectors and were later compared with an intersection-based method. The content-based retrieval system is tested to be effective in terms of retrieval and scalability through experimental results and precision-recall analysis.

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Development of The Flexible User-Friendly Real-Time Machine Vision Inspection System (사용자 중심의 유연한 실시간 머신비전 검사시스템 개발)

  • Cho, In-Sung;Lee, Ji-Hong;Oh, Sang-Jin
    • Journal of the Institute of Electronics Engineers of Korea SC
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    • v.45 no.3
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    • pp.42-50
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    • 2008
  • We developed a visual inspection system for detecting defective products. Most existing inspection systems are designed to be dedicated to one product, which makes operator spend extra money and time to adopt other products. In this work, we propose a flexible visual inspection system that can inspect various products without any additional major job at a low-cost. The developed system contained image processing algorithm libraries and user-friendly graphic interface for adaptable image-based inspection system. We can find a proper threshold value using the proposed algorithm which uses correlation coefficient between a non-defective product and existing sample images of defective product. And We tested the performance of the proposed algorithm using Otsu's method. The proposed system is applied to a automated inspection line for cellular phone.