• Title/Summary/Keyword: thresholding method

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An Image Segmentation based on Chamfer Algorithm (Chamfer 알고리듬에 기초한 영상분리 기법)

  • Kim, Hak-Kyeong;Jeong, Nam-Soo;Lee, Myung-Suk;Kim, Sang-Bong
    • Proceedings of the KSME Conference
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    • 2001.06b
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    • pp.670-675
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    • 2001
  • This paper is to propose image segmentation method based on chamfer algorithm. First, we get original image from CCD camera and transform it into gray image. Second, we extract maximum gray value of background and reconstruct and eliminate the background using surface fitting method and bilinear interpolation. Third, we subtract the reconstructed background from gray image to remove noises in gray image. Fourth, we transform the subtracted image into binary image using Otsu's optimal thresholding method. Fifth, we use morphological filters such as areaopen, opening, filling filter etc. to remove noises and isolated points. Sixth, we use chamfer distance or Euclidean distance to this filtered image. Finally, we use watershed algorithm and count microorganisms in image by labeling. To prove the effectiveness, we apply the proposed algorithm to one of Ammonia-oxidizing bacteria, Acinetobacter sp. It is shown that both Euclidean algorithm and chamfer algorithm show over-segmentation. But Chamfer algorithm shows less over-segmentation than Euclidean algorithm.

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Adaptive Thresholding Method Using Zone Searching Based on Representative Points for Improving the Performance of LCD Defect Detection (LCD 결함 검출 성능 개선을 위한 대표점 기반의 영역 탐색을 이용한 적응적 이진화 기법)

  • Kim, Jin-Uk;Ko, Yun-Ho;Lee, Si-Woong
    • The Journal of the Korea Contents Association
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    • v.16 no.7
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    • pp.689-699
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    • 2016
  • As the demand for LCD increases, the importance of inspection equipment for improving the efficiency of LCD production is continuously emphasized. The pattern inspection apparatus is one that detects minute defects of pattern quickly using optical equipment such as line scan camera. This pattern inspection apparatus makes a decision on whether a pixel is a defect or not using a single threshold value in order to meet constraint of real time inspection. However, a method that uses an adaptive thresholding scheme with different threshold values according to characteristics of each region in a pattern can greatly improve the performance of defect detection. To apply this adaptive thresholding scheme it has to be known that a certain pixel to be inspected belongs to which region. Therefore, this paper proposes a region matching algorithm that recognizes the region of each pixel to be inspected. The proposed algorithm is based on the pattern matching scheme with the consideration of real time constraint of machine vision and implemented through GPGPU in order to be applied to a practical system. Simulation results show that the proposed method not only satisfies the requirement for processing time of practical system but also improves the performance of defect detection.

Real Time Face Detection in Video Using Progressive Thresholding (순차 임계 설정법을 이용한 비디오에서의 실시간 얼굴검출)

  • Ye Soo-Young;Lee Seon-Bong;Kum Dae-Hyun;Kim Hyo-Sung;Nam Ki-Gon
    • Journal of the Institute of Convergence Signal Processing
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    • v.7 no.3
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    • pp.95-101
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    • 2006
  • A face detection plays an important role in face recognition, video surveillance, and human computer interaction. In this paper, we propose a progressive threshold method to detect human faces in real time. The consecutive face images are acquired from camera and transformed into YCbCr color space images. The skin color of the input images are separated using a skin color filter in the YCbCr color space and some candidated face areas are decided by connected component analysis. The intensity equalization is performed to avoid the effect of many circumstances and an arbitrary threshold value is applied to get binary images. The eye area can be detected because the area is clearly distinguished from others in the binary image progressive threshold method searches for an optimal eye area by progressively increasing threshold from low values. After progressive thresholding, the eye area is normalized and verified by back propagation algorithm to finalize the face detection.

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A Fast and Robust License Plate Detection Algorithm Based on Two-stage Cascade AdaBoost

  • Sarker, Md. Mostafa Kamal;Yoon, Sook;Park, Dong Sun
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.8 no.10
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    • pp.3490-3507
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    • 2014
  • License plate detection (LPD) is one of the most important aspects of an automatic license plate recognition system. Although there have been some successful license plate recognition (LPR) methods in past decades, it is still a challenging problem because of the diversity of plate formats and outdoor illumination conditions in image acquisition. Because the accurate detection of license plates under different conditions directly affects overall recognition system accuracy, different methods have been developed for LPD systems. In this paper, we propose a license plate detection method that is rapid and robust against variation, especially variations in illumination conditions. Taking the aspects of accuracy and speed into consideration, the proposed system consists of two stages. For each stage, Haar-like features are used to compute and select features from license plate images and a cascade classifier based on the concatenation of classifiers where each classifier is trained by an AdaBoost algorithm is used to classify parts of an image within a search window as either license plate or non-license plate. And it is followed by connected component analysis (CCA) for eliminating false positives. The two stages use different image preprocessing blocks: image preprocessing without adaptive thresholding for the first stage and image preprocessing with adaptive thresholding for the second stage. The method is faster and more accurate than most existing methods used in LPD. Experimental results demonstrate that the LPD rate is 98.38% and the average computational time is 54.64 ms.

An Effective Binarization Method for Character Image (문자 영상을 위한 효율적인 이진화 방법)

  • Kim, Do-Hyeon;Jung, Ho-Young;Cho, Hoon;Cha, Eui-Young
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.10 no.10
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    • pp.1877-1884
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    • 2006
  • Image binarization is an important preprocessing to identify objects of interest by dividing pixels into background and objects. Usually binarization methods are classified into global and local thresholding approaches. In this paper, we propose an efficient and adaptive binarization method for the character segmentation by combining both advantages of the global and the local thresholding methods. Experimental results with the korean character images present that the proposed method binarizes character image faster and better than other local binarization methods.

A Real-time Detection Method for the Driving Direction Points of a Low Speed Processor (저 사양 프로세서를 위한 실시간 주행 방향점 검출 기법)

  • Hong, Yeonggi;Park, Jungkil;Lee, Sungmin;Park, Jaebyung
    • Journal of Institute of Control, Robotics and Systems
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    • v.20 no.9
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    • pp.950-956
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    • 2014
  • In this paper, the real-time detection method of a DDP (Driving Direction Point) is proposed for an unmanned vehicle to safely follow the center of the road. Since the DDP is defined as a center point between two lanes, the lane is first detected using a web camera. For robust detection of the lane, the binary thresholding and the labeling methods are applied to the color camera image as image preprocessing. From the preprocessed image, the lane is detected, taking the intrinsic characteristics of the lane such as width into consideration. If both lanes are detected, the DDP can be directly obtained from the preprocessed image. However, if one lane is detected, the DDP is obtained from the inverse perspective image to guarantee reliability. To verify the proposed method, several experiments to detect the DDPs are carried out using a 4 wheeled vehicle ERP-42 with a web camera.

Simple Fuzzy Rule Based Edge Detection

  • Verma, O.P.;Jain, Veni;Gumber, Rajni
    • Journal of Information Processing Systems
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    • v.9 no.4
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    • pp.575-591
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    • 2013
  • Most of the edge detection methods available in literature are gradient based, which further apply thresholding, to find the final edge map in an image. In this paper, we propose a novel method that is based on fuzzy logic for edge detection in gray images without using the gradient and thresholding. Fuzzy logic is a mathematical logic that attempts to solve problems by assigning values to an imprecise spectrum of data in order to arrive at the most accurate conclusion possible. Here, the fuzzy logic is used to conclude whether a pixel is an edge pixel or not. The proposed technique begins by fuzzifying the gray values of a pixel into two fuzzy variables, namely the black and the white. Fuzzy rules are defined to find the edge pixels in the fuzzified image. The resultant edge map may contain some extraneous edges, which are further removed from the edge map by separately examining the intermediate intensity range pixels. Finally, the edge map is improved by finding some left out edge pixels by defining a new membership function for the pixels that have their entire 8-neighbourhood pixels classified as white. We have compared our proposed method with some of the existing standard edge detector operators that are available in the literature on image processing. The quantitative analysis of the proposed method is given in terms of entropy value.

Inspection of Automotive Oil-Seals Using Artificial Neural Network and Vision System (인공신경망과 비전 시스템을 이용한 자동차용 오일씰의 검사)

  • 노병국;김기대
    • Journal of the Korean Society for Precision Engineering
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    • v.21 no.8
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    • pp.83-88
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    • 2004
  • The Classification of defected oil-seals using a vision system with the artificial neural network is presented. The artificial neural network fur classification consists of 27 input nodes, 10 hidden nodes, and one output node. The selection of the number of the input nodes is based on an observation that the difference among the defected, non-defected, and smeared oil-seals is greatly pronounced in the 26 step gray-scale level thresholding. The number of the hidden nodes is chosen as a result of a trade-off between accuracy and computing time. The back-propagation algorithm is used for teaching the network. The proposed network is capable of successfully classifying the defected from the smeared oil-seals which tend to be classified as the defected ones using the binary thresholding. It is envisaged that the proposed method improves the reliability and productivity of the automotive vision inspection system.

A Study on Visual Saliency Detection in Infrared Images Using Boolean Map Approach

  • Truong, Mai Thanh Nhat;Kim, Sanghoon
    • Journal of Information Processing Systems
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    • v.16 no.5
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    • pp.1183-1195
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    • 2020
  • Visual saliency detection is an essential task because it is an important part of various vision-based applications. There are many techniques for saliency detection in color images. However, the number of methods for saliency detection in infrared images is limited. In this paper, we introduce a simple approach for saliency detection in infrared images based on the thresholding technique. The input image is thresholded into several Boolean maps, and an initial saliency map is calculated as a weighted sum of the created Boolean maps. The initial map is further refined by using thresholding, morphology operation, and a Gaussian filter to produce the final, high-quality saliency map. The experiment showed that the proposed method has high performance when applied to real-life data.

Objective Measurement of Water Repellency of Fabric Using Image Analysis (I) - Methodology of Image Processing -

  • Jeong Young Jin;Jang Jinho
    • Fibers and Polymers
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    • v.6 no.2
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    • pp.162-168
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    • 2005
  • A methodology for the objective evaluation of water repellency is studied using image analysis of the sprayed pattern on woven fabrics according to a standard spray test (AATCC Test Method 22-2001). The wet area ratio obtained from the spray standard test ranking is found to be exponentially related with its water repellency rating. Mean filtering is used to remove the effect of weave texture and the transmitted light through interyarn spaces. The ring frame of the instrument and wet region are recognized using Otsu thresholding technique. And Hough transform and outline operation are used to obtain the size and position of the ring frame. The objective assessment of the water repellency using image processing can reduce unnecessary confusion in the subjective determination of the water repellency.