- 标题
- 摘要
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[IEEE 2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) - Chennai (2018.3.22-2018.3.24)] 2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) - Automatic Segmentation of Exudates in Retinal Images
摘要： This paper presents a new technique for segmentation of exudates in fundus images. This technique is based on Discrete Wavelet Transform (DWT) and histogram based thresholding procedure. In this work, Optic Disc (OD) is eliminated using DWT from original green component image prior segmentation of exudates. This step aids to avoid the misclassification of exudates region. Histogram based threshold calculation procedure is introduced for segmentation of bright regions in green component image. Hard exudates are obtained after masking the OD region in segmented bright regions of the green component image. This technique was evaluated on images from DIARETDB0 and DIARETDB1 databases. The average sensitivity, specificity and accuracy achieved by proposed method are 0.7890, 0.9972 and 0.9964 respectively. Comparison with existing methods offered in the literature shows that the performance of proposed approach is significant.
关键词： Optic Disc,Retinal image,Segmentation,Exudates,Discrete Wavelet Transform
更新于2025-09-23 15:22:29
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Hybrid technique for the detection of suspicious lesions in digital mammograms
摘要： This paper presents an efficient system for the detection of suspicious lesions in mammograms. The proposed detection system consists of three steps. In the first step, an efficient pre-processing technique is developed using Top-Hat morphological filter and NL means filter. In the second step, threshold selection procedure is developed using a combination of Fuzzy C-means (FCM), gradient magnitude (GM), and intensity contrast (IC). Finally, computed threshold is used to extract the suspicious lesions in mammograms. The Free Response Operating Characteristics (FROC) curve is used to assess the performance of the proposed system. Proposed system achieved the sensitivity of 93.8% at the rate of 0.51 false positives per image.
关键词： breast cancer,segmentation,computer-aided diagnosis,fuzzy C-means,mammograms
更新于2025-09-23 15:22:29
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Unsupervised Segmentation of Spectral Images with a Spatialized Gaussian Mixture Model and Model Selection
摘要： In this article, we describe a novel unsupervised spectral image segmentation algorithm. This algorithm extends the classical Gaussian Mixture Model-based unsupervised classification technique by incorporating a spatial flavor into the model: the spectra are modelized by a mixture of K classes, each with a Gaussian distribution, whose mixing proportions depend on the position. Using a piecewise constant structure for those mixing proportions, we are able to construct a penalized maximum likelihood procedure that estimates the optimal partition as well as all the other parameters, including the number of classes. We provide a theoretical guarantee for this estimation, even when the generating model is not within the tested set, and describe an efficient implementation. Finally, we conduct some numerical experiments of unsupervised segmentation from a real dataset.
关键词： Spectral images,Gaussian Mixture Model,Model selection,Spatial information,Unsupervised segmentation
更新于2025-09-23 15:22:29
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Image-segmentation algorithm based on wavelet and data-driven neutrosophic fuzzy clustering
摘要： Aim to that Neutrosophic C-mean clustering segmentation does not consider the membership distribution of every sample point to different classes. Herein, an image-segmentation algorithm based on wavelet and data-driven neutrosophic fuzzy clustering is proposed. When the maximum membership value of a sample point is far greater than other membership values, the centre of the class with the maximum membership value is taken as the centre of the fuzzy class. Otherwise, the average value of the centre of the two classes with the highest and second-highest membership values is used as the centre of the fuzzy class. In the preprocessing stage, wavelet technology is used to remove noise from the processed image, and the improved Bayesian algorithm is employed to calculate the filter threshold. The experiment results for synthetic and natural images show that the proposed method is more accurate and effective than the existing methods.
关键词： Image segmentation,wavelet transformation,neutrosophic fuzzy clustering
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE 38th International Conference on Electronics and Nanotechnology (ELNANO) - Kiev (2018.4.24-2018.4.26)] 2018 IEEE 38th International Conference on Electronics and Nanotechnology (ELNANO) - A Very High Resolution Satellite Imagery Classification Algorithm
摘要： This work is devoted to high-resolution WorldView-2 and WorldView-3 satellite imagery processing. We have developed a satellite imagery automatic classification algorithm based on the object-based approach. Three different segmentation methods are investigated in order to determine which is the most appropriate for our task. The experimental results show a good accuracy of the proposed algorithm.
关键词： image features,classification,segmentation,Kappa index,object-based image analysis
更新于2025-09-23 15:22:29
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An Improved DBSCAN Method for LiDAR Data Segmentation with Automatic Eps Estimation
摘要： Point cloud data segmentation, ?ltering, classi?cation, and feature extraction are the main focus of point cloud data processing. DBSCAN (density-based spatial clustering of applications with noise) is capable of detecting arbitrary shapes of clusters in spaces of any dimension, and this method is very suitable for LiDAR (Light Detection and Ranging) data segmentation. The DBSCAN method needs at least two parameters: The minimum number of points minPts, and the searching radius ε. However, the parameter ε is often harder to determine, which hinders the application of the DBSCAN method in point cloud segmentation. Therefore, a segmentation algorithm based on DBSCAN is proposed with a novel automatic parameter ε estimation method—Estimation Method based on the average of k nearest neighbors’ maximum distance—with which parameter ε can be calculated on the intrinsic properties of the point cloud data. The method is based on the ?tting curve of k and the mean maximum distance. The method was evaluated on different types of point cloud data: Airborne, and mobile point cloud data with and without color information. The results show that the accuracy values using ε estimated by the proposed method are 75%, 74%, and 71%, which are higher than those using parameters that are smaller or greater than the estimated one. The results demonstrate that the proposed algorithm can segment different types of LiDAR point clouds with higher accuracy in a robust manner. The algorithm can be applied to airborne and mobile LiDAR point cloud data processing systems, which can reduce manual work and improve the automation of data processing.
关键词： parameter estimation,segmentation,DBSCAN,LiDAR
更新于2025-09-23 15:22:29
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On the performance improvement of non-cooperative iris biometrics using segmentation and feature selection techniques
摘要： In this work, an improved segmentation methodology and a novel feature selection algorithm are proposed. From the input eye image, iris boundary is identified using Circular Hough Transform. A bounding box is defined using the radius obtained followed by iterative thresholding techniques to eliminate specular reflections, eyelids, eyelashes and pupil region. First-order and second-order statistical features are extracted from the segmented iris. For the first time, the statistical measure, Chi-square value is computed from GLCM as a new novel feature from iris images. Statistical dependency-based backward feature selection (SDBFS) algorithm is used to reduce the feature vector size. By operating on local features in reduced search space, computation complexity of segmentation is reduced with less mislocalisation count and eliminates pupil dilation effects. Results of SDBFS show the usefulness of minimal-useful features. Experimental results conducted on CASIA V1, V3-interval and UBIRIS V1 datasets show that statistical features in non-ideal iris images outperform some of the state-of-the-art methods.
关键词： backward feature selection,chi-square value,grey level co-occurrence matrix,iris recognition,GLCM,statistical dependency,Circular Hough Transform,segmentation
更新于2025-09-23 15:22:29
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Rectangular-Normalized Superpixel Entropy Index for Image Quality Assessment
摘要： Image quality assessment (IQA) is a fundamental problem in image processing that aims to measure the objective quality of a distorted image. Traditional full-reference (FR) IQA methods use fixed-size sliding windows to obtain structure information but ignore the variable spatial configuration information. In order to better measure the multi-scale objects, we propose a novel IQA method, named RSEI, based on the perspective of the variable receptive field and information entropy. First, we find that consistence relationship exists between the information fidelity and human visual of individuals. Thus, we reproduce the human visual system (HVS) to semantically divide the image into multiple patches via rectangular-normalized superpixel segmentation. Then the weights of each image patches are adaptively calculated via their information volume. We verify the effectiveness of RSEI by applying it to data from the TID2008 database and denoise algorithms. Experiments show that RSEI outperforms some state-of-the-art IQA algorithms, including visual information fidelity (VIF) and weighted average deep image quality measure (WaDIQaM).
关键词： image quality assessment,superpixel segmentation,mutual information
更新于2025-09-23 15:22:29
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Multispectral imaging: Monitoring vulnerable people
摘要： This paper describes the development of a new healthcare monitoring system for vulnerable people which uses a visible image sensor and passive infrared sensors, in an unconventional manner, to monitor daily living activities. It based on a novel method, using chromatic methodology, to process spatially and temporally the incoming multispectral data from the visible and infrared parts of the spectrum, to overcome the impact of noisy environments, illumination changes and a dynamic background. An efficient chromatic descriptor is suggested to improve activity recognition of vulnerable people. The new monitoring system is robust to distortions associated with healthcare systems and its descriptor has an improved quality of description. System performance was evaluated using a series of experimental data, the results showing the efficacy of using both spatial and temporal domains of multispectral data to deal with events that disturb monitoring systems. The chromatic descriptor achieved a better performance in comparison to traditional methods when describing daily living activities.
关键词： Raspberry Pi,Laser image segmentation,Healthcare monitoring,Multispectral imaging,Microsoft Kinect
更新于2025-09-23 15:22:29
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Steam piping infrared image segmentation with trend coefficients algorithm
摘要： In the industrial production or life of mankind, the use of steam piping network brings convenience and rapidity. As we all know, steam piping are often applied to transport high-temperature materials. Excessive temperature often has potential safety hazards or causes waste of resources. This requires real-time monitoring of abnormal phenomena such as excessive local temperature in steam pipelines. Based on the characteristics of infrared images, steam network pipe images can be easily captured by infrared thermal imager. However, the complexity and diversity of the environment make it difficult for infrared images to directly distinguish the high temperature area and normal temperature area of the pipes. In order to solve this problem, this paper proposes a trend coefficient algorithm for infrared spectroscopy image. Firstly, one-dimensional single-threshold Otsu method is extended to one-dimensional multi-threshold acquisition, and then one-dimensional method is extended to two-dimensional method to form two-dimensional double-threshold Otsu segmentation algorithm. The algorithm includes the trace of between-class scatter matrix as the evaluation function, and analyzes the trend coefficient to obtain the optimal threshold. Through the simulation experiment of MATLAB, it can be seen that the method can clearly get the distribution of high temperature area of pipeline image. It not only extracts the pipeline area from the image, but also accurately segments and locates the over-temperature area on the steam pipeline image. And it also eliminates the interference of trees and shrubs in the outdoor environment to a certain extent. Under the characteristics of different steam pipeline images, the evaluation results confirm that the proposed method can locate and segment the high temperature area of pipeline accurately.
关键词： Infrared high temperature region,Steam piping infrared image segmentation,Trend coefficients algorithm
更新于2025-09-23 15:22:29