期刊文献+
共找到6篇文章
< 1 >
每页显示 20 50 100
Symbolic Images of Colors, Animals and Machines in The Red Badge of Courage
1
作者 王晓俊 《英语广场(学术研究)》 2014年第1期23-27,30,共6页
Abstract:Stephen Crane was an outstanding American novelist,poet,and journalist.He achieved great success in his literary works during his brief career.Crane’s most well-known work,The Red Badge of Courage,is commonl... Abstract:Stephen Crane was an outstanding American novelist,poet,and journalist.He achieved great success in his literary works during his brief career.Crane’s most well-known work,The Red Badge of Courage,is commonly believed to be the first great novel of the American Civil War,largely because of its vivid and detailed description of the experience of warfare.This paper analyzes the images of color,animal and machine,which convey Crane’s thoughts of war:war is full of chaos,brutality,and confusion,without any romantic elements or heroism. 展开更多
关键词 The Red Badge of Courage symbolic images color images animal images machine images
下载PDF
Application of artificial intelligence in ophthalmology 被引量:12
2
作者 Xue-Li Du Wen-Bo Li Bo-Jie Hu 《International Journal of Ophthalmology(English edition)》 SCIE CAS 2018年第9期1555-1561,共7页
Artificial intelligence is a general term that means to accomplish a task mainly by a computer, with the least human beings participation, and it is widely accepted as the invention of robots. With the development of ... Artificial intelligence is a general term that means to accomplish a task mainly by a computer, with the least human beings participation, and it is widely accepted as the invention of robots. With the development of this new technology, artificial intelligence has been one of the most influential information technology revolutions. We searched these English-language studies relative to ophthalmology published on PubMed and Springer databases. The application of artificial intelligence in ophthalmology mainly concentrates on the diseases with a high incidence, such as diabetic retinopathy, agerelated macular degeneration, glaucoma, retinopathy of prematurity, age-related or congenital cataract and few with retinal vein occlusion. According to the above studies, we conclude that the sensitivity of detection and accuracy for proliferative diabetic retinopathy ranged from 75% to 91.7%, for non-proliferative diabetic retinopathy ranged from 75% to 94.7%, for age-related macular degeneration it ranged from 75% to 100%, for retinopathy of prematurity ranged over 95%, for retinal vein occlusion just one study reported ranged over 97%, for glaucoma ranged 63.7% to 93.1%, and for cataract it achieved a more than 70% similarity against clinical grading. 展开更多
关键词 artificial intelligence deep learning machine learning images processing OPHTHALMOLOGY
原文传递
An edge-adaptive demosaicking method based on image correlation 被引量:1
3
作者 贾晓芬 赵佰亭 +1 位作者 周孟然 陈兆权 《Journal of Central South University》 SCIE EI CAS CSCD 2015年第4期1397-1404,共8页
To reduce the cost, size and complexity, a consumer digital camera usually uses a single sensor overlaid with a color filter array(CFA) to sample one of the red-green-blue primary color values, and uses demosaicking a... To reduce the cost, size and complexity, a consumer digital camera usually uses a single sensor overlaid with a color filter array(CFA) to sample one of the red-green-blue primary color values, and uses demosaicking algorithm to estimate the missing color values at each pixel. A novel image correlation and support vector machine(SVM) based edge-adaptive algorithm was proposed, which can reduce edge artifacts and false color artifacts, effectively. Firstly, image pixels were separated into edge region and smooth region with an edge detection algorithm. Then, a hybrid approach switching between a simple demosaicking algorithm on the smooth region and SVM based demosaicking algorithm on the edge region was performed. Image spatial and spectral correlations were employed to create middle planes for the interpolation. Experimental result shows that the proposed approach produced visually pleasing full-color result images and obtained higher CPSNR and smaller S-CIELAB*ab?E than other conventional demosaicking algorithms. 展开更多
关键词 demosaicking image correlation support vector machine edge-adaptability
下载PDF
Employment of predictive search algorithm in digital image correlation
4
作者 马志峰 王昊 韩福海 《Journal of Beijing Institute of Technology》 EI CAS 2014年第2期254-259,共6页
A predictive search algorithm to estimate the size and direction of displacement vectors was presented.The algorithm decreased the time of calculating the displacement of each pixel.In addition,the updating reference ... A predictive search algorithm to estimate the size and direction of displacement vectors was presented.The algorithm decreased the time of calculating the displacement of each pixel.In addition,the updating reference image scheme was used to update the reference image and to decrease the computation time when the displacement was larger than a certain number.In this way,the search range and computational complexity were cut down,and less EMS memory was occupied.The capability of proposed search algorithm was then verified by the results of both computer simulation and experiments.The results showed that the algorithm could improve the efficiency of correlation method and satisfy the accuracy requirement for practical displacement measuring. 展开更多
关键词 machine vision predictive search algorithm digital image correlation sub-pixel displacement measurement
下载PDF
Spatial-Aware Supervised Learning for Hyper-Spectral Image Classification Comprehensive Assessment
5
作者 SOOMRO Bushra Naz XIAO Liang +1 位作者 SOOMRO Shahzad Hyder MOLAEI Mohsen 《Journal of Donghua University(English Edition)》 EI CAS 2016年第6期954-960,共7页
A comprehensive assessment of the spatial.aware mpervised learning algorithms for hyper.spectral image (HSI) classification was presented. For this purpose, standard support vector machines ( SVMs ), mudttnomial l... A comprehensive assessment of the spatial.aware mpervised learning algorithms for hyper.spectral image (HSI) classification was presented. For this purpose, standard support vector machines ( SVMs ), mudttnomial logistic regression ( MLR ) and sparse representation (SR) based supervised learning algorithm were compared both theoretically and experimentally. Performance of the discussed techniques was evaluated in terms of overall accuracy, average accuracy, kappa statistic coefficients, and sparsity of the solutions. Execution time, the computational burden, and the capability of the methods were investigated by using probabilistie analysis. For validating the accuracy a classical benchmark AVIRIS Indian pines data set was used. Experiments show that integrating spectral.spatial context can further improve the accuracy, reduce the misclassltication error although the cost of computational time will be increased. 展开更多
关键词 learning algorithms hyper-spectral image classification support vector machine(SVM) multinomial logistic regression(MLR) elastic net regression(ELNR) sparse representation(SR) spatial-aware
下载PDF
Developing global image feature analysis models to predict cancer risk and prognosis
6
作者 Bin Zheng Yuchen Qiu +3 位作者 Faranak Aghaei Seyedehnafiseh Mirniaharikandehei Morteza Heidari Gopichandh Danala 《Visual Computing for Industry,Biomedicine,and Art》 2019年第1期150-163,共14页
In order to develop precision or personalized medicine,identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest... In order to develop precision or personalized medicine,identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest recently.Most of these research approaches use the similar concepts of the conventional computer-aided detection schemes of medical images,which include steps in detecting and segmenting suspicious regions or tumors,followed by training machine learning models based on the fusion of multiple image features computed from the segmented regions or tumors.However,due to the heterogeneity and boundary fuzziness of the suspicious regions or tumors,segmenting subtle regions is often difficult and unreliable.Additionally,ignoring global and/or background parenchymal tissue characteristics may also be a limitation of the conventional approaches.In our recent studies,we investigated the feasibility of developing new computer-aided schemes implemented with the machine learning models that are trained by global image features to predict cancer risk and prognosis.We trained and tested several models using images obtained from full-field digital mammography,magnetic resonance imaging,and computed tomography of breast,lung,and ovarian cancers.Study results showed that many of these new models yielded higher performance than other approaches used in current clinical practice.Furthermore,the computed global image features also contain complementary information from the features computed from the segmented regions or tumors in predicting cancer prognosis.Therefore,the global image features can be used alone to develop new case-based prediction models or can be added to current tumor-based models to increase their discriminatory power. 展开更多
关键词 machine learning models of medical images Global medial image feature analysis Cancer risk prediction Cancer prognosis prediction Quantitative imaging markers
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部