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نتیجه جستجو - طبقه بندی عکس

تعداد مقالات یافته شده: 2
ردیف عنوان نوع
1 myStone: A system for automatic kidney stone classification
myStone: یک سیستم طبقه بندی خودکار سنگ کلیه-2017
Article history:Received 5 April 2017Revised 14 July 2017Accepted 15 July 2017Available online 17 July 2017Keywords: Kidney stone Optical device Computer visionImage classificationKidney stone formation is a common disease and the incidence rate is constantly increasing worldwide. It has been shown that the classification of kidney stones can lead to an important reduction of the re- currence rate. The classification of kidney stones by human experts on the basis of certain visual color and texture features is one of the most employed techniques. However, the knowledge of how to analyze kidney stones is not widespread, and the experts learn only after being trained on a large number of samples of the different classes. In this paper we describe a new device specifically designed for cap- turing images of expelled kidney stones, and a method to learn and apply the experts knowledge with regard to their classification. We show that with off the shelf components, a carefully selected set of fea- tures and a state of the art classifier it is possible to automate this difficult task to a good degree. We report results on a collection of 454 kidney stones, achieving an overall accuracy of 63% for a set of eight classes covering almost all of the kidney stones taxonomy. Moreover, for more than 80% of samples the real class is the first or the second most probable class according to the system, being then the patient recommendations for the two top classes similar. This is the first attempt towards the automatic visual classification of kidney stones, and based on the current results we foresee better accuracies with the increase of the dataset size.© 2017 Elsevier Ltd. All rights reserved.
Keywords: Kidney stone | Optical device | Computer vision | Image classification
مقاله انگلیسی
2 Heterogeneous visual features integration for image recognition optimization in internet of things
یکپارچه سازی ویژگی های یکپارچه برای بهینه سازی تصویر در اینترنت از اشیا-2016
Recently, a large number of physical devices, together with distributed information systems, deployed in internet of things (IoT), are collecting more and more images. Such collected images recognition poses an important challenge on optimization in internet of things. Specially, most of existing methods only adopt shallow learning models to integrate various features of images for recognition limiting classification accuracy. In this paper, we propose a multimodal deep learning (MMDL) approach to integrate hetero geneous visual features by considering each type of visual feature as one modality for image recognition optimization in internet of things. In our scheme, we extract the high-level abstraction of each modality by a stacked autoencoders. Furthermore, we design a back propagation algorithm with shared weights learned from a softmax layer to update the pretrained parameters of multiple stacked autoencoders simultaneously. The integration is performed by concatenating the last hidden layers of the multimodal stacked autoencoders architecture. Extensive experiments are carried out on three datasets i.e. Ani mal with Attributes, NUS-WIDE-OBJECT, and Handwritten Numerals, by comparison with SVM, SAE, and AMMSS. Results demonstrate that our scheme has superior performance on heterogeneous visual features integration for image recognition optimization in internet of things.
Keywords: Multimodal integration optimization | Deep learning | Internet of things | Image classification | Stacked autoencoders
مقاله انگلیسی
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بازدید امروز: 1634 :::::::: بازدید دیروز: 0 :::::::: بازدید کل: 1634 :::::::: افراد آنلاین: 51