- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Chongqing (2018.6.27-2018.6.29)] 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - A New Strategy to Detect Lung Cancer on CT Images
摘要： Lung cancer has a very low cure rate in the advanced stages, with effective early detection, the survival rate of lung cancer could be highly raised. Detection of lung cancer in the early stages plays a vital role for human health. Computed tomography (CT) images, which provide electronic densities of tissues, are widely applied in radiotherapy planning. The proposed system based on CT technology consists of image acquisition, preprocessing, feature extraction, and classification. In the preprocessing stage, RGB images are converted to grayscale images, the median filter and the Wiener filter are used to uproot noises, Otsu thresholding method is applied to convert CT images, and REGIONPROPS function is used to exact body region from binary images. In the feature extraction stage, features, like Contrast, Correlation, Energy, Homogeneity, are extracted through statistic method Gray Level Co-occurrence Matrix (GLCM). In the final stage, extracted features, together with Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN), are used to identify lung cancer from CT images. The performance of the proposed system shows satisfactory results of 96.32% accuracy on SVM and 83.07% accuracy on BPNN respectively.
关键词： BPNN,SVM,image processing,lung cancer detection,GLCM
更新于2025-09-10 09:29:36
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Quantum image edge extraction based on classical Sobel operator for NEQR
摘要： As the basic problem in image processing and computer vision, the purpose of edge detection is to identify the point where the brightness of the digital image changes obviously. It is an indispensable task in digital image processing that image edge detection significantly reduces the amount of data and eliminates information that can be considered irrelevant, preserving the important structural properties of the image. However, because of the sharp increase in the image data in the actual applications, real-time problem has become a limitation in classical image processing. In this paper, quantum image edge extraction for the novel enhanced quantum representation (NEQR) is designed based on classical Sobel operator. The quantum image model of NEQR utilizes the inherent entanglement and superposition properties of quantum mechanics to store all the pixels of an image in a superposition state, which can realize parallel computation for calculating the gradients of the image intensity of all the pixels simultaneously. Through constructing and analyzing the quantum circuit of realization image edge extraction, we demonstrate that our proposed scheme can extract edges in the computational complexity of O(n2 + 2q+4) for a NEQR quantum image with a size of 2n × 2n. Compared with all the classical edge extraction algorithms and some existing quantum edge extraction algorithms, our proposed scheme can reach a significant and exponential speedup. Hence, our proposed scheme would resolve the real-time problem of image edge extraction in practice image processing.
关键词： Sobel operator,Edge detection,Real-time problem,Quantum image processing
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Representative Signature Generation for Plant Detection in Hyperspectral Images
摘要： In this study, the effect of utilizing different type of signatures on plant detection success is evaluated on hyperspectral aerial images. Plant regions are tried to detect using spectral signatures of leaf, stem and tassel belonging to the plant separately and the plant representative signature (PRS) is created by averaging of signatures of selected region on the aerial images. The signatures used for detection are generated from hyperspectral images taken from 10m distance to target plant. The Spectral Angle Mapper (SAM) and Generalized Likelihood Ratio Test (GLRT) algorithms are used for target detection. Performance evaluation is made by Receiver Operating Characteristic (ROC) curves. When the results are evaluated, it is observed that the detection performance with the use of PRS is higher.
关键词： hyperspectral image processing,plant classification,corn detection,spectral library
更新于2025-09-10 09:29:36
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Quantitative evaluation of skin surface roughness using optical coherence tomography in vivo
摘要： The quantitative monitoring of skin topography is important in the field of cosmetics and dermatology. The most widespread method for determining skin roughness in vivo is to use skin microrelief, PRIMOS device, which allows a noninvasive, fast and direct measurement of the skin surface. However, it has drawbacks, such as the interference of backscattering from volumetric skin and motion artifacts. In this study, we demonstrate the potential of OCT for providing reliable and quantitative skin surface roughness. In order to evaluate the performance of OCT for skin surface analysis, different types of skin phantoms are fabricated and measured. We utilize OCT to identify the effect of cosmetics as well as human skin topology for various aging groups and different skin regions. Skin surface roughness parameters based on ISO 25178 part 2 standard definitions are then derived from home-built image processing software and compared with one acquired from PRIMOS. Our results show that skin surface geometry acquired from 3D OCT images is well quantified to complex wrinkle structure and robust to the angle of the subject. Since OCT enables to present topology and volumetric skin anatomy quantitative skin simultaneously, it would be a useful tool to deliver comprehensive and intuitive information in dynamic skin observations.
关键词： Surfaces,Tomography,Biomedical image processing
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Airport Detection Based on Superpixel Segmentation and Saliency Analysis for Remote Sensing Images
摘要： Traditional target detection methods are usually based on prior knowledge by template matching and classification. Nowadays, remote sensing images contain richer and richer information. It will cause high computation complexity if we still apply traditional target detection methods to remote sensing images. This paper proposes an airport detection model based on superpixel segmentation and saliency analysis. First, the input image is segmented into superpixels. Then saliency analysis is performed by calculating differences between superpixels and corresponding weights in R, G and B color channels to get the saliency map. Finally we utilize the limitation in the ratio of perimeter and area and morphology operation to eliminate the interference. Experiments compare the proposed model with three saliency analysis models qualitatively and quantitatively. Results show that the proposed model is better than the three comparative models in keeping clear boundaries, eliminating interference and maintaining intact targets.
关键词： Image processing,saliency analysis,superpixel,remote sensing,airport detection
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - An Automatic Deployment Support for Processing Remote Sensing Data in the Cloud
摘要： Master/Worker distributed programming model enables huge remote sensing data processing by assigning tasks to Workers in which data is stored. Cloud computing features include the deployment of Workers by using virtualized technologies such as virtual machines and containers. These features allow programmers to configure, create, and start virtual resources for instance. In order to develop remote sensing applications by taking advantage of high-level programming languages (e.g., R, Matlab, and Julia), users have to manually address Cloud resource deployment. This paper presents the design, implementation, and evaluation of the Infra.jl research prototype. Infra.jl takes advantage of Julia Master/Worker programming simplicity for providing automatic deployment of Julia Workers in the Cloud. The assessment of Infra.jl automatic deployment is only ～2.8s in two different Azure Cloud data centers.
关键词： data management,cloud computing,big data,image processing,remote sensing
更新于2025-09-10 09:29:36
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[IEEE 2018 International Conference on Network Infrastructure and Digital Content (IC-NIDC) - Guiyang, China (2018.8.22-2018.8.24)] 2018 International Conference on Network Infrastructure and Digital Content (IC-NIDC) - Ship detection in foggy remote sensing image via scene classification R-CNN
摘要： The object detection networks via Faster R-CNN for ship detection have demonstrated impressive performance. However, the complexity of weather conditions in high resolution satellite images exposes the limited capacity of these networks. Images interfered by fog are common in optical remote sensing images. In this paper, we embrace this observation and introduce our research. Unlike SAR images, optical sensor images are very susceptible to the effects of the weather, especially clouds and fog.So, accurate target information cannot be obtained from these image, which reduces the accuracy of ship detection. To solve this problem, we attempts to introduce the image defogging methods into object detection networks to suppress the interference of clouds. Secondly, the SC-R-CNN structure is proposed, which uses the scene classification network (SCN) to realize the classification of fog-containing images and cascaded with the object detection network to form a dual-stream object detection framework. In addition, the combination of defogging methods and the SC-R-CNN network also produces more optimized results. We use the remote sensing image data set containing various types of weather conditions to confirm the validity and accuracy of the proposed method.
关键词： Remote sensing,Image processing,Defogging,Object detection,Convolutional neural network,Deep learning
更新于2025-09-10 09:29:36
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SoilJ: An ImageJ Plugin for the Semiautomatic Processing of Three-Dimensional X-ray Images of Soils
摘要： Noninvasive three- and four-dimensional X-ray imaging approaches have proved to be valuable analysis tools for vadose zone research. One of the main bottlenecks for applying X-ray imaging to data sets with a large number of soil samples is the relatively large amount of time and expertise needed to extract quantitative data from the respective images. SoilJ is a plugin for the free and open imaging software ImageJ that aims at automating the corresponding processing steps for cylindrical soil columns. It includes modules for automatic column outline recognition, correction of image intensity bias, image segmentation, extraction of particulate organic matter and roots, soil surface topography detection, as well as morphology and percolation analyses. In this study, the functionality and precision of some key SoilJ features were demonstrated on five different image data sets of soils. SoilJ has proved to be useful for strongly decreasing the amount of time required for image processing of large image data sets. At the same time, it allows researchers with little experience in image processing to make use of X-ray imaging methods. The SoilJ source code is freely available and may be modified and extended at will by its users. It is intended to stimulate further community-driven development of this software.
关键词： ImageJ plugin,image processing,SoilJ,3-D X-ray imaging,vadose zone research
更新于2025-09-10 09:29:36
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[IEEE 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Honolulu, HI (2018.7.18-2018.7.21)] 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - An mRMR-SVM Approach for Opto-Fluidic Microorganism Classification
摘要： The detection of microorganisms is important in numerous applications such as water quality monitoring, blood analysis, and food testing. The conventional detection methods are tedious and labour-intensive. Establish methods involve culturing, counting and identification of the pathogen by an experienced technician which typically can take several days. The use of opto-fluidic technology to capture microorganism images offers o route to reduce the overall assay time. However, the detection still requires a trained technician. This paper proposes an image processing method that can be used to classify microorganism images captured by an opto-fluidic set up in an automatic manner. The proposed algorithm incorporates some of the features used in other microorganism image detection methods and proposes two new features - Entropy of Histogram of Oriented Gradients (HOG) and the filtered intensities. In addition, we propose to apply the minimal-Redundancy-Maximal-Relevance (mRMR) criterion to select and rank these features. The probability and joint probability distribution functions of the mRMR are estimated using a Gaussian model and the Kernel Density Estimation model. The performance of the proposed method was validated using SVM and data collected from an experimental setup. The results show that our proposed method outperforms existing methods and is capable of achieving a classification accuracy up to 95.8%.
关键词： mRMR,SVM,image processing,opto-fluidic technology,microorganism detection
更新于2025-09-10 09:29:36
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[IEEE 2018 37th Chinese Control Conference (CCC) - Wuhan (2018.7.25-2018.7.27)] 2018 37th Chinese Control Conference (CCC) - A Detection System of Tool Parameter Using Machine Vision
摘要： This paper suggests a novel technique for the tool parameter measurement based on machine vision. Tool images are captured by using a machine vision system and the outer contour image of the cutter is obtained by using the machine vision technology. The HALCON image processing library is used as the development platform to build the tool parameters test system. Several algorithms including image segmentation, edge extraction and ?tting ellipse determination is used for image processing. The tool parameters such as external diameter and contour angle of tool edge can be obtained after rebuilding the contour of tool edge.The proposed scheme is shown to be reliable and effective for the automated tool parameter measurement.
关键词： tool parameter,image processing,Machine vision
更新于2025-09-10 09:29:36