Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing; 28th International C Igor V. Tetko,Věra K?rková,Fabian Thei

正當理由

書目名稱Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing影響因子(影響力)




書目名稱Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing影響因子(影響力)學科排名




書目名稱Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing網(wǎng)絡公開度




書目名稱Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing網(wǎng)絡公開度學科排名




書目名稱Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing被引頻次




書目名稱Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing被引頻次學科排名




書目名稱Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing年度引用




書目名稱Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing年度引用學科排名




書目名稱Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing讀者反饋




書目名稱Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing讀者反饋學科排名

新星

overshadow

Eye Movement-Based Analysis on Methodologies and Efficiency in the Process of Image Noise Evaluationistorted images for making decision on noise evaluation is rather limited. In this paper, we conducted psychophysical eye-tracking studies to deeply understand the process of image noise evaluation. We identified two different types of methodologies in the evaluation processing, speed-driven and acc

Additive

IBDNet: Lightweight Network for On-orbit Image Blind Denoisingnerstone of image processing, image denoising exceedingly improves the image quality to contribute to subsequent works. For on-orbit image denoising, we propose an end-to-end trainable image blind denoising network, namely IBDNet. Unlike existing image denoising methods, which either have a large nu

Femine

Aggregating Rich Deep Semantic Features for Fine-Grained Place Classificationages depends on the objects and text as well as the various semantic regions, hierarchical structure, and spatial layout. However, most recently designed fine-grained classification systems ignored this, the complex multi-level semantic structure of images associated with fine-grained classes has no

效果

Improving Reliability of Object Detection for Lunar Craters Using Monte Carlo Dropoutesent the uncertainty of such detection. However, a measure of uncertainty could be expressed as the variance of the prediction by using Monte Carlo Dropout Sampling (MC Dropout). Although MC Dropout has often been applied to fully connected layers in a network in recent studies, many convolutional

negligence

An Improved Convolutional Neural Network for Steganalysis in the Scenario of Reuse of the Stego-Keyhe steganalysis scenario of the repeated use of the stego-key is considered. Firstly, a study of the influence of the depth and width of the convolution layers on the effectiveness of classification was conducted. Next, a study on the influence of depth and width of fully connected layers on the eff

帶子

A New Learning-Based One Shot Detection Framework for Natural Imagesbjects well. In this paper, we propose a new framework that applies one-shot learning to object detection. During the training period, the network learns an ability from known object classes to compare the similarity of two image parts. For the image of a new category, selective search seeks proposa

Mystic

Dense Receptive Field Network: A Backbone Network for Object Detection detection tasks. So, designing a special backbone network for detection tasks is one of the best solutions. In this paper, a backbone network named Dense Receptive Field Network (DRFNet) is proposed for object detection. DRFNet is based on Darknet-60 (our modified version of Darknet-53) and contain

left-ventricle