Otsu Thresholding Algorithm Research Articles

Document image binarisation based on low‐rank modelling

Segmentation from document images with a complex background is presented. The background component is estimated by low-rank modelling. With the estimated background component, the original image is further transformed into a compensated image which is easily segmented with Otsu's thresholding algorithm to obtain a good binarisation result. Experiments on the Bickley diary and the DIBCO 2013 datasets validate the feasibility of the proposed method.

Automatic detection of exudates in retinal images based on threshold moving average models

Since exudate diagnostic procedures require the attention of an expert ophthalmologist as well as regular monitoring of the disease, the workload of expert ophthalmologists will eventually exceed the current screening capabilities. Retinal imaging technology is a current practice screening capability providing a great potential solution. In this paper, a fast and robust automatic detection of exudates based on moving average histogram models of the fuzzy image was applied, and then the better histogram was derived. After segmentation of the exudate candidates, the true exudates were pruned based on Sobel edge detector and automatic Otsu's thresholding algorithm that resulted in the accurate location of the exudates in digital retinal images. To compare the performance of exudate detection methods we have constructed a large database of digital retinal images. The method was trained on a set of 200 retinal images, and tested on a completely independent set of 1220 retinal images. Results show that the exudate detection method performs overall best sensitivity, specificity, and accuracy of 90.42%, 94.60%, and 93.69%, respectively.

Infrared Target Detection Based on Temporal-Spatial Domain Fusion

In order to improve the accuracy and stability of infrared target detection, a novel moving target detection approach based on temporal-spatial domain fusion is presented. A multi-level spatial-temporal median filter is utilized to extract the background frame, with which the background clutters are suppressed by using the background subtraction technique. Then a local weighted operator is applied to enhance the targets. Lastly, the otsu thresholding algorithm is utilized to detect the targets. Experimental results demonstrate that the proposed approach is capable of detecting infrared moving targets effectively for F1 measurement up to 92.8%.

Image Threshold Segmentation Algorithm Based on Histogram Statistical Property

The traditional Otsu threshold algorithm is not a good method for processing the real images because of complex shape and unbalanced distribution. To solve this problem, the paper uses the thinking of Otsu’s method for reference, introduces a threshold segmentation algorithm based on histogram statistical property. In addition, the paper draws a comparison between the new algorithm and Otsu’s algorithm. Experimental results show that the new algorithm can get better segmentation effect than that of Otsu’s method when the gray-level distribution of the background follows normal distribution approximatively, and the target region is less than the background region.

Automatic method of fruit object extraction under complex agricultural background for vision system of fruit picking robot

Fruit picking robot is required for agricultural automation for fruit harvest, and vision system is the important and crucial composition of a robot system. An automatic extraction method of fruit object under complex agricultural background for vision system in fruit picking robot is presented in this study. The method is based on an improving Otsu threshold algorithm using a new feature in OHTA color space. Color features are extracted in OHTA color space and then used as an input for the Otsu threshold algorithm which calculates the segmentation threshold value automatically. Four kinds of fruit images are selected to validate the automatic extraction method. The fruit objects are automatically extracted with this method and the outputs are presented in binary images. Numerous of experiments show that the automatic extraction method can extract mature fruit from complex agricultural background and the extraction accuracy is more than 95%. The results indicate an effective fruit object extraction method for vision system of fruit picking robot.

Adaptive Minimum Error Thresholding Algorithm

A robust minimum error thresholding method is proposed to combine the three-dimensional (3D) minimum error thresholding scheme based on 2D method with the principle of rebuilding and dimension reduction of the 3D histogram. Considering the global behavior of this approach and its ability to process even illumination images only, a water flow model is used to estimate the background of uneven illumination images for improving adaptability of the proposed method. Then, the difference image between the original image and background can be readily obtained to reduce the interference of uneven illumination during the binarization process. To improve execution performance of the segmentation procedure, gamma correction is employed to enhance image in addition to a global segmentation using robust minimum error thresholding algorithm. Subsequently, image segmentation tests are carried out with even and uneven illumination, and then comparison on misclassification error and time expenditure are performed between the proposed method and other approaches, i.e., 1D/2D minimum error thresholding, Otsu thresholding algorithm based on 3D histogram rebuilding and dimensionality reduction, adaptive gray wave transformation thresholding scheme, as well as a modified FCM method. The results show that the proposed approach yields better thresholding performance than those methods.

Computer Aided Diagnosis System for Detection of Lung Cancer in CT Scan Images

The automated Computer Aided Diagnosing (CAD) system is proposed in this paper for detection of lung cancer form the analysis of computed tomography images. In recent years the image processing mechanisms are used widely in several medical areas for improving earlier detection and treatment stages, in which the time factor is very important to discover the disease in the patient as possible as fast, especially in various cancer types such as the lung cancer, breast cancer. Lung cancer is the second most commonly diagnosed cancer in the United States, and it is the leading cancer related death in the world, with the current fatality rate exceeding that of the next three most common cancers (breast, prostate, and colorectal) combined. In this research, we considered the problem of developing an automated system for detecting the presence of pulmonary nodules in the lung CT .The essence of developing a system like that needed to focus on detecting nodules in their early stages, which are the very small nodules that are likely to be overlooked by the radiologists. This paper involves cancer detection system based on texture features extracted from the slice of DICOM Lung CT images for the identification of cancerous nodules. In developing this system we passed the available lung CT images and its database in basic three stages to achieve more accuracy in our experimental results: Firstly A pre-processing stage involving some image enhancement techniques helps to solve the problem. We preprocess the images (by contrast enhancement, thresholding, filtering, and blob analysis) obtained after scanning the Lung CT Images and secondly separate the suspected nodule areas (SNA) from the image by a segmentation process by using thresholding segmentation mechanism by Otsu thresholding algorithm and region growing techniques. Finally we relied on Texture features which help us to make a comparison between cancerous and non cancerous images. For accurate detection of cancerous nodules, we need to differentiate the cancerous nodules from the noncancerous. We developed an artificial neural network to differentiate them. We trained the neural network by the backpropagation algorithm and tested it with different images from a database of the DICOM CT Lung images of NIH/NCI Lung Image Database Consortium (LIDC) dataset.

영상 분할을 위한 Context Fuzzy c-Means 알고리즘을 이용한 공간 분할

영상 분할 (Image Segmentation)은 패턴 인식, 환경 인식, 문서 분석을 위한 영상 처리 과정에서 가장 기본적인 단계이다. 영상 분할 방법들 중 Otsu의 영상의 정규화된 히스토그램의 분포 정보를 이용하여 클래스 간의 분산을 최대화 시키는 임계치값을 결정하는 자동 임계치값 선정방법이 가장 잘 알려진 방법이다. Otsu의 방법은 영상의 전 영역에 대한 히스토그램을 분석함으로써 영상의 부분적인 특성을 반영하여 임계치값을 결정하기는 어렵다. 본 논문에서는 이 어려움 해소하기 위하여 Context Fuzzy c-Means 알고리즘을 이용하여 영상을 여러 개의 부분 영역으로 나누고, 정의된 부 영역에 영상 분할 기법을 적용함으로써 부 영역들에 적합한 여러 개의 임계치값을 계산함으로써 영상 분할 성능을 개선하고자 하였다. Image segmentation is the basic step in the field of the image processing for pattern recognition, environment recognition, and context analysis. The Otsu's automatic threshold selection, which determines the optimal threshold value to maximize the between class scatter using the distribution information of the normalized histogram of a image, is the famous method among the various image segmentation methods. For the automatic threshold selection proposed by Otsu, it is difficult to determine the optimal threshold value by considering the sub-region characteristic of the image because the Otsu's algorithm analyzes the global histogram of a image. In this paper, to alleviate this difficulty of Otsu's image segmentation algorithm and to improve image segmentation capability, the original image is divided into several sub-images by using context fuzzy c-means algorithm. The proposed fuzzy Otsu threshold algorithm is applied to the divided sub-images and the several threshold values are obtained.

Stereo audio source separation based on time–frequency masking and multilevel thresholding

Source separation and up-mixing in real commercial music recordings is a challenging problem. In the last few years, some algorithms have provided interesting results, but the problem remains unsolved. In this paper we describe a method for separating the sources present in a two channel mixture based on the panning coefficients used in the stereo mixdown. The sources are separated by estimating time-frequency masks using the multilevel extension of the Otsu thresholding algorithm used in image segmentation. A refinement step is also carried out for extraction and reassignment of inter-source residuals. Examples of application and performance evaluation are also discussed.

IC 패키지 마킹검사를 위한 적응적 다단계 이진화와 정합단위의 동적 선택

머신비전을 이용한 IC 패키지 마킹검사 시스템은 입력영상으로부터 검사할 요소들의 위치를 식별하고, 추출된 요소들을 학습된 표준 패턴과 비교하여 마킹의 불량 여부를 판단한다. 본 논문에서는 검사 대상 IC 패키지의 위치 판별, 마킹문자 추출, 핀원딤플 검출과 같은 일련의 작업들에 적합한 적응적 다단계 이진화 방법과 마킹문자의 국소적인 오류검출은 물론 잡영에 강건한 정합단위의 동적 선택 방법을 제안한다. 제안하는 이진화 방법은 이진화 대상 영역과 명도 값의 범위를 제한하여 Otsu의 이진화 알고리즘을 적용함으로써 특정 응용에 적응적인 이진화가 가능하다. 정합단위의 동적 선택 방법은 문자추출 및 배치분석에 대한 결과에 따라 정합단위를 선택한다. 그러므로 문자추출 및 배치분석 과정에서 발생하는 예기치 못한 부적절한 상황에서도 가능한 범위내에서 최소의 정합단위를 선택할 수 있다. 제안된 방법을 구현하여 8종의 IC 패키지, 총 280개의 영상에 대하여 실험한 결과, IC 패키지와 핀원딤플의 검출율은 100%였으며, 마킹상태에 대한 판정은 98.8%의 정확도를 나타내어 제안된 방법이 효과적임을 확인할 수 있었다. 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.