• Title/Summary/Keyword: Image processing algorithms

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Fruit Grading Algorithms of Multi-purpose Fruit Grader Using Black at White Image Processing System (흑백영상처리장치를 이용한 다목적 과실선별기의 등급판정 알고리즘 개발)

  • 노상하;이종환;황인근
    • Journal of Biosystems Engineering
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    • v.20 no.1
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    • pp.95-103
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    • 1995
  • A series of study has been conducted to develop a multi-purpose fruit grader using a black & white image processing system equipped with a 550 nm interference filter. A device and high performance algorithms were developed for sizing and color grading of Fuji apple in the previous study. In this study an emphasis was put on finding correlations between weights of several kinds of fruits and their area fractions(AF), and on compensating the blurring effect upon sizing and color grading by conveying speed of fruit. Also, the effect of orientation and direction of fruit on conveyor during image forming was analyzed to identify any difficulty (or utilizing an automatic fruit feeder. The results are summarized as follows. 1. The correlation coefficients(r) between the weights of fruits and their image sizes were 0.984~0.996 for apples, 0.983~0.990 for peachs, 0.995 for tomato, 0.986 for sweet persimmon and 0.970~0.993 for pears. 2. It was possible to grade fruits by color with the area weighted mean gray values(AWMGV) based on the mean gray valves of direct image and the compensated values of reflected image of a fruit, and also possible to sort fruits by size with AF. Accuracies in sizing and color grading ranged over 81.0% ~95.0% and 82.0% ~89.7% respectively as compared with results from sizing by electronic weight scale and grading by expert. 3. The blurring effect on the sizing and color grading depending on conveying speed was identified and regression equations were derived. 4. It was found that errors in sizing and coloring grading due to the change in direction and orientation of Fuji apple on the conveyor were not significant as far as the stem end of apple keeping upward.

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High accuracy measurement of the position and orientation of SMD VR by Computer Vision (비젼을 이용한 SMD 부품의 위치 및 자세 계측)

  • 김병엽;송재용;한창수;박종현;이영민
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 1995.04b
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    • pp.371-376
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    • 1995
  • Computer vision is applicated to measure the position and orientation of the SMD on 8mm Camcoder PCB and advanced image processing algorithms for high accuracy and real time processing are proposed. Illumination conditions are optimized for the best image formation and a set of LEDs is used as economic illuminator, which is regarded as a summation of many point sources. Conctete optical system is constructed and the performance of the proposed algorithm is verified by several experiments.

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Blocking artefact noise reduction using block division (블록 나눔을 사용한 블로킹 아티팩트 잡음 감소)

  • Cha, Seong Won;Shin, Jae Ho
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.4 no.1
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    • pp.47-53
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    • 2008
  • Blocking artefact noise is necessarily happened in compressed images using block-coded algorithms such as JPEC compressing algorithm. This noise is more recognizable especially in highly compressed images. In this paper, an algorithm is presented for reduction of blocking artefact noise using block division. Furthermore, we also mention about the median filter which is often used in image processing.

Automatic Extraction and Measurement of Visual Features of Mushroom (Lentinus edodes L.) (표고 외관 특징점의 자동 추출 및 측정)

  • Hwang, Heon;Lee, Yong-Guk
    • Journal of Bio-Environment Control
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    • v.1 no.1
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    • pp.37-51
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    • 1992
  • Quantizing and extracting visual features of mushroom(Lentinus edodes L.) are crucial to the sorting and grading automation, the growth state measurement, and the dried performance indexing. A computer image processing system was utilized for the extraction and measurement of visual features of front and back sides of the mushroom. The image processing system is composed of the IBM PC compatible 386DK, ITEX PCVISION Plus frame grabber, B/W CCD camera, VGA color graphic monitor, and image output RGB monitor. In this paper, an automatic thresholding algorithm was developed to yield the segmented binary image representing skin states of the front and back sides. An eight directional Freeman's chain coding was modified to solve the edge disconnectivity by gradually expanding the mask size of 3$\times$3 to 9$\times$9. A real scaled geometric quantity of the object was directly extracted from the 8-directional chain element. The external shape of the mushroom was analyzed and converted to the quantitative feature patterns. Efficient algorithms for the extraction of the selected feature patterns and the recognition of the front and back side were developed. The developed algorithms were coded in a menu driven way using MS_C language Ver.6.0, PC VISION PLUS library fuctions, and VGA graphic functions.

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Optimization of Max-Plus based Neural Networks using Genetic Algorithms (유전 알고리즘을 이용한 Max-Plus 기반의 뉴럴 네트워크 최적화)

  • Han, Chang-Wook
    • Journal of the Institute of Convergence Signal Processing
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    • v.14 no.1
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    • pp.57-61
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    • 2013
  • A hybrid genetic algorithm based learning method for the morphological neural networks (MNN) is proposed. The morphological neural networks are based on max-plus algebra, therefore, it is difficult to optimize the coefficients of MNN by the learning method with derivative operations. In order to solve the difficulty, a hybrid genetic algorithm based learning method to optimize the coefficients of MNN is used. Through the image compression/reconstruction experiment using test images extracted from standard image database(SIDBA), it is confirmed that the quality of the reconstructed images obtained by the proposed method is better than that obtained by the conventional neural networks.

Car detection area segmentation using deep learning system

  • Dong-Jin Kwon;Sang-hoon Lee
    • International journal of advanced smart convergence
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    • v.12 no.4
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    • pp.182-189
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    • 2023
  • A recently research, object detection and segmentation have emerged as crucial technologies widely utilized in various fields such as autonomous driving systems, surveillance and image editing. This paper proposes a program that utilizes the QT framework to perform real-time object detection and precise instance segmentation by integrating YOLO(You Only Look Once) and Mask R CNN. This system provides users with a diverse image editing environment, offering features such as selecting specific modes, drawing masks, inspecting detailed image information and employing various image processing techniques, including those based on deep learning. The program advantage the efficiency of YOLO to enable fast and accurate object detection, providing information about bounding boxes. Additionally, it performs precise segmentation using the functionalities of Mask R CNN, allowing users to accurately distinguish and edit objects within images. The QT interface ensures an intuitive and user-friendly environment for program control and enhancing accessibility. Through experiments and evaluations, our proposed system has been demonstrated to be effective in various scenarios. This program provides convenience and powerful image processing and editing capabilities to both beginners and experts, smoothly integrating computer vision technology. This paper contributes to the growth of the computer vision application field and showing the potential to integrate various image processing algorithms on a user-friendly platform

Study on Robust Driving for Autonomous Vehicle in Real-Time (자율주행차량의 실시간 강건한 주행을 위한 연구)

  • 이대은;김정훈;김영배
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 2004.10a
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    • pp.908-911
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    • 2004
  • In this paper, we describe a robust image processing algorithm to recognize the road lane in real-time. For the real-time processing, a detection area is decided by a lane segment of a previous frame and edges are detected on the basis of the lane width. For the robust driving, the global threshold with the Otsu algorithm is used to get a binary image in a frame. Therefore, reliable edges are obtained from the algorithms suggested in this paper in a short time. Lastly, the lane segment is found by hough transform. We made a RC(Radio Control) car equipped with a vision system and verified these algorithms using the RC Car.

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Two-Dimensional Hidden Markov Mesh Chain Algorithms for Image Dcoding (이차원 영상해석을 위한 은닉 마프코프 메쉬 체인 알고리즘)

  • Sin, Bong-Gi
    • The Transactions of the Korea Information Processing Society
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    • v.7 no.6
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    • pp.1852-1860
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    • 2000
  • Distinct from the Markov random field or pseudo 2D HMM models for image analysis, this paper proposes a new model of 2D hidden Markov mesh chain(HMMM) model which subsumes the definitions of and the assumptions underlying the conventional HMM. The proposed model is a new theoretical realization of 2D HMM with the causality of top-down and left-right progression and the complete lattice constraint. These two conditions enable an efficient mesh decoding for model estimation and a recursive maximum likelihood estimation of model parameters. Those algorithms are developed in theoretical perspective and, in particular, the training algorithm, it is proved, attains the optimal set of parameters.

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The Study on Removing Random-valued Impulse Noise

  • Yinyu, Gao;Kim, Nam-Ho
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2011.05a
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    • pp.333-335
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    • 2011
  • In the transmitting process of image processing system, images always be corrupted by impulse noise, especially random-valued impulse noise. So removing the random-valued impulse noise is very important, but it is also one of the most difficult case in image processing. The most famous method is the standard median filter, but at edge, the filter has a special feature which has a tendency to decrease the preserve. As a result, we proposed a filter that detection random-valued impulse noise firstly, next to use efficient method to remove the noise and preserve the details. And through the simulation, we compared with the algorithms and indicated that proposed method significant improvement over many other existing algorithms.

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Automatic Determination of Constraint Parameter for Improving Homography Matrix Calculation in RANSAC Algorithm

  • Chandra, Devy;Lee, Kee-Sung;Jo, Geun-Sik
    • Annual Conference of KIPS
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    • 2014.04a
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    • pp.830-833
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    • 2014
  • This paper proposes dynamic constraint parameter to filter out degenerate configurations (i.e. set of collinear or adjacent features) in RANSAC algorithm. We define five different groups of image based on the feature distribution pattern. We apply the same linear and distance constraints for every image, but we use different constraint parameter for every group, which will affect the filtering result. An evaluation is done by comparing the proposed dynamic CS-RANSAC algorithm with the classic RANSAC and regular CS-RANSAC algorithms in the calculation of a homography matrix. The experimental results show that dynamic CS-RANSAC algorithm provides the lowest error rate compared to the other two algorithms.