• Title/Summary/Keyword: Multi-level Otsu Thresholding

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A Multi-thresholding Approach Improved with Otsu's Method (Otsu의 방법을 개선한 멀티 스래쉬홀딩 방법)

  • Li Zhe-Xue;Kim Sang-Woon
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.43 no.5 s.311
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    • pp.29-37
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    • 2006
  • Thresholding is a fundamental approach to segmentation that utilizes a significant degree of pixel popularity or intensity. Otsu's thresholding employed the normalized histogram as a discrete probability density function. Also it utilized a criterion that minimizes the between-class variance of pixel intensity to choose a threshold value for segmentation. However, the Otsu's method has a disadvantage of repeatedly searching optimal thresholds for the entire range. In this paper, a simple but fast multi-level thresholding approach is proposed by means of extending the Otsu's method. Rather than invoke the Otsu's method for the entire gray range, we advocate that the gray-level range of an image be first divided into smaller sub-ranges, and that the multi-level thresholds be achieved by iteratively invoking this dividing process. Initially, in the proposed method, the gray range of the object image is divided into 2 classes with a threshold value. Here, the threshold value for segmentation is selected by invoking the Otsu's method for the entire range. Following this, the two classes are divided into 4 classes again by applying the Otsu's method to each of the divided sub-ranges. This process is repeatedly performed until the required number of thresholds is obtained. Our experimental results for three benchmark images and fifty faces show a possibility that the proposed method could be used efficiently for pattern matching and face recognition.

Obtaining Object by Using Optimal Threshold for Saliency Map Thresholding (Saliency Map을 이용한 최적 임계값 기반의 객체 추출)

  • Hai, Nguyen Cao Truong;Kim, Do-Yeon;Park, Hyuk-Ro
    • The Journal of the Korea Contents Association
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    • v.11 no.6
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    • pp.18-25
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    • 2011
  • Salient object attracts more and more attention from researchers due to its important role in many fields of multimedia processing like tracking, segmentation, adaptive compression, and content-base image retrieval. Usually, a saliency map is binarized into black and white map, which is considered as the binary mask of the salient object in the image. Still, the threshold is heuristically chosen or parametrically controlled. This paper suggests using the global optimal threshold to perform saliency map thresholding. This work also considers the usage of multi-level optimal thresholds and the local adaptive thresholds in the experiments. These experimental results show that using global optimal threshold method is better than parametric controlled or local adaptive threshold method.

A Fast Thresholding Method For Pattern Matching (패턴매칭을 위한 고속 스레쉬홀딩법)

  • Li, Zhe-Xue;Kim, Sang-Woon
    • Proceedings of the KIEE Conference
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    • 2006.04a
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    • pp.126-128
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    • 2006
  • For pattern matching, an object image should be segmented and analyzed for the first time. Thresholding is a fundamental approach to segmentation that utilizes a significant degree of pixel popularity or intensity. Otsu's thresholding is one of the most veil-known methods proposed in the literature. However, the method has a disadvantage of repeatedly searching the optimal thresholds for the entire region. To overcome this problem, a number of methods have been proposed. In this paper, we propose a simple and fast thresholding method of finding multi-level threshold values by extending the Otsu's method. Our experimental results for the benchmak images show a possibility that the proposed method could be used efficiently for pattern matching.

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Multi-Level Thresholding based on Non-Parametric Approaches for Fast Segmentation

  • Cho, Sung Ho;Duy, Hoang Thai;Han, Jae Woong;Hwang, Heon
    • Journal of Biosystems Engineering
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    • v.38 no.2
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    • pp.149-162
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    • 2013
  • Purpose: In image segmentation via thresholding, Otsu and Kapur methods have been widely used because of their effectiveness and robustness. However, computational complexity of these methods grows exponentially as the number of thresholds increases due to the exhaustive search characteristics. Methods: Particle swarm optimization (PSO) and genetic algorithms (GAs) can accelerate the computation. Both methods, however, also have some drawbacks including slow convergence and ease of being trapped in a local optimum instead of a global optimum. To overcome these difficulties, we proposed two new multi-level thresholding methods based on Bacteria Foraging PSO (BFPSO) and real-coded GA algorithms for fast segmentation. Results: The results from BFPSO and real-coded GA methods were compared with each other and also compared with the results obtained from the Otsu and Kapur methods. Conclusions: The proposed methods were computationally efficient and showed the excellent accuracy and stability. Results of the proposed methods were demonstrated using four real images.

Automatic Thresholding Selection for Image Segmentation Based on Genetic Algorithm (유전자알고리즘을 이용한 영상분할 문턱값의 자동선정에 관한 연구)

  • Lee, Byung-Ryong;Truong, Quoc Bao;Pham, Van Huy;Kim, Hyoung-Seok
    • Journal of Institute of Control, Robotics and Systems
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    • v.17 no.6
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    • pp.587-595
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    • 2011
  • In this paper, we focus on the issue of automatic selection for multi-level threshold, and we greatly improve the efficiency of Otsu's method for image segmentation based on genetic algorithm. We have investigated and evaluated the performance of the Otsu and Valley-emphasis threshold methods. Based on this observation we propose a method for automatic threshold method that segments an image into more than two regions with high performance and processing in real-time. Our paper introduced new peak detection, combines with evolution algorithm using MAGA (Modified Adaptive Genetic Algorithm) and HCA (Hill Climbing Algorithm), to find the best threshold automatically, accurately, and quickly. The experimental results show that the proposed evolutionary algorithm achieves a satisfactory segmentation effect and that the processing time can be greatly reduced when the number of thresholds increases.

A Multi-Layer Perceptron for Color Index based Vegetation Segmentation (색상지수 기반의 식물분할을 위한 다층퍼셉트론 신경망)

  • Lee, Moon-Kyu
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.43 no.1
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    • pp.16-25
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    • 2020
  • Vegetation segmentation in a field color image is a process of distinguishing vegetation objects of interests like crops and weeds from a background of soil and/or other residues. The performance of the process is crucial in automatic precision agriculture which includes weed control and crop status monitoring. To facilitate the segmentation, color indices have predominantly been used to transform the color image into its gray-scale image. A thresholding technique like the Otsu method is then applied to distinguish vegetation parts from the background. An obvious demerit of the thresholding based segmentation will be that classification of each pixel into vegetation or background is carried out solely by using the color feature of the pixel itself without taking into account color features of its neighboring pixels. This paper presents a new pixel-based segmentation method which employs a multi-layer perceptron neural network to classify the gray-scale image into vegetation and nonvegetation pixels. The input data of the neural network for each pixel are 2-dimensional gray-level values surrounding the pixel. To generate a gray-scale image from a raw RGB color image, a well-known color index called Excess Green minus Excess Red Index was used. Experimental results using 80 field images of 4 vegetation species demonstrate the superiority of the neural network to existing threshold-based segmentation methods in terms of accuracy, precision, recall, and harmonic mean.

An Adaptive Multi-Level Thresholding and Dynamic Matching Unit Selection for IC Package Marking Inspection (IC 패키지 마킹검사를 위한 적응적 다단계 이진화와 정합단위의 동적 선택)

  • Kim, Min-Ki
    • The KIPS Transactions:PartB
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    • v.9B no.2
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    • pp.245-254
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    • 2002
  • IC package marking inspection system using machine vision locates and identifies the target elements from input image, and decides the quality of marking by comparing the extracted target elements with the standard patterns. This paper proposes an adaptive multi-level thresholding (AMLT) method which is suitable for a series of operations such as locating the target IC package, extracting the characters, and detecting the Pinl dimple. It also proposes a dynamic matching unit selection (DMUS) method which is robust to noises as well as effective to catch out the local marking errors. The main idea of the AMLT method is to restrict the inputs of Otsu's thresholding algorithm within a specified area and a partial range of gray values. Doing so, it can adapt to the specific domain. The DMUS method dynamically selects the matching unit according to the result of character extraction and layout analysis. Therefore, in spite of the various erroneous situation occurred in the process of character extraction and layout analysis, it can select minimal matching unit in any environment. In an experiment with 280 IC package images of eight types, the correct extracting rate of IC package and Pinl dimple was 100% and the correct decision rate of marking quality was 98.8%. This result shows that the proposed methods are effective to IC package marking inspection.