期刊文献+
共找到2篇文章
< 1 >
每页显示 20 50 100
Discrimination of Acacia seeds at species and subspecies levels using an image analyzer 被引量:1
1
作者 V.SIVAKUMAR R.ANANDALAKSHMI +3 位作者 Rekha R.WARRIER B.G.SINGH M.TIGABU b.nagarajan 《Forestry Studies in China》 CAS 2013年第4期253-260,共8页
Seeds of Acacia species and subspecies were characterized using an image analyzer and discriminated for the purpose of identification of species, using their seeds. The species considered in the study were Acacia nilo... Seeds of Acacia species and subspecies were characterized using an image analyzer and discriminated for the purpose of identification of species, using their seeds. The species considered in the study were Acacia nilotica subsp. indica, A. nilotica subsp. cupressiformis, A. nilotica subsp. tomentosa, A. tortilis subsp. raddiana, A. tortilis subsp. spirocarpa, A. raddiana, A. senegal, A. auriculiformis, A. farnesiana, A. leucophloea, A. mearnsii, A. melanoxylon, A. planifrons and A. mangium. Eight samples each consisting of 25 seeds per species were studied using the image analyzer for physical characteristics of seeds, such as 2D surface area, length, width, perimeter, roundness, aspect ratio and fullness ratio. Discriminant analysis showed that acacias can be discriminated at species and subspecies levels, with 96% accuracy. Exceptions were A. nilotica subsp. tomentosa(75.0%), A. tortilis subsp. spirocarpa(75.0%) and A. raddiana(87.5%) which had relatively low discrimination accuracy. However, discriminant analysis within selected species showed complete recognition of these species except for A. tortilis subsp. spirocarpa, that had still a large overlap with A. leucophloea. The study also revealed that both seed size and shape characteristics were responsible for species discrimination. It can be concluded that rapid analysis of seed size and shape characteristics using image analysis techniques can be used as primary and secondary keys for identification of acacias. 展开更多
关键词 ACACIA image analyzer discriminant analysis seed identification
下载PDF
Convolutional Neural Networks for Automated Diagnosis of Diabetic Retinopathy in Fundus Images
2
作者 S.Rama Krishna Naresh Cherukuri +4 位作者 Y.Dileep Kumar R.Jayakarthik b.nagarajan Allam Balaram G.Divya Jyothi 《Journal of Artificial Intelligence and Technology》 2023年第4期205-214,共10页
Diabetic retinopathy(DR),a long-term complication of diabetes,is notoriously hard to detect in its early stages due to the fact that it only shows a subset of symptoms.Standard diagnostic procedures for DR now include... Diabetic retinopathy(DR),a long-term complication of diabetes,is notoriously hard to detect in its early stages due to the fact that it only shows a subset of symptoms.Standard diagnostic procedures for DR now include optical coherence tomography and digital fundus imaging.If digital fundus images alone could provide a reliable diagnosis,then eliminating the costly optical coherence tomography would be beneficial for all parties involved.Optometrists and their patients will find this useful.Using deep convolutional neural networks(DCNNs),we provide a novel approach to this problem.Our approach deviates from standard DCNN methods by exchanging typical max-pooling layers with fractional max-pooling ones.In order to collect more subtle information for categorization,two such DCNNs,each with a different number of layers,are trained.To establish these limits,we use DCNNs and features extracted from picture metadata to train a support vector machine classifier.In our experiments,we used information from Kaggle’s open DR detection database.We fed our model 34,124 training images,1,000 validation examples,and 53,572 test images to train and test it.Each of the five classes in the proposed DR classifier corresponds to one of the steps in the DR process and is given a numeric value between 0 and 4.Experimental results show a higher identification rate(86.17%)than those found in the existing literature,indicating the suggested strategy may be effective.We have jointly developed an algorithm for machine learning and accompanying software,and we’ve named it deep retina.Images of the fundus acquired by the typical person using a portable ophthalmoscope may be instantly analyzed using our technology.This technology might be used for self-diagnosis,at-home care,and telemedicine. 展开更多
关键词 automated diagnosis of diabetic retinopathy DCNNs diabetic retinopathy self-diagnosis
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部