Artificial Neural Networks and Machine Learning - ICANN 2019: Image Processing : 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, Proceedings, Part III / edited by Igor V. Tetko, Věra Kůrková, Pavel Karpov, Fabian Theis.

Format:

Book

Contributor:

Tetko, Igor V., editor.

Kůrková, V. (Vera), 1948- editor.

Karpov, Pavel, editor.

Theis, Fabian, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

LNCS sublibrary. Theoretical computer science and general issues ; SL 1, 11729.

Theoretical Computer Science and General Issues ; 11729

Language:

English

Subjects (All):

Artificial intelligence.

Optical data processing.

Computer organization.

Computers.

Algorithms.

Computer security.

Artificial Intelligence.

Image Processing and Computer Vision.

Computer Systems Organization and Communication Networks.

Information Systems and Communication Service.

Algorithm Analysis and Problem Complexity.

Systems and Data Security.

Local Subjects:

Artificial Intelligence.

Image Processing and Computer Vision.

Computer Systems Organization and Communication Networks.

Information Systems and Communication Service.

Algorithm Analysis and Problem Complexity.

Systems and Data Security.

Physical Description:

1 online resource (XXX, 733 pages) : 417 illustrations, 273 illustrations in color.

Edition:

First edition 2019.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2019.

System Details:

text file PDF

Summary:

The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019. The total of 277 full papers and 43 short papers presented in these proceedings was carefully reviewed and selected from 494 submissions. They were organized in 5 volumes focusing on theoretical neural computation; deep learning; image processing; text and time series; and workshop and special sessions. .

Contents:

Unsharp Masking Layer: Injecting Prior Knowledge in Convolutional Networks for Image Classification

Distortion Estimation Through Explicit Modeling of the Refractive Surface

Eye Movement-based Analysis on Methodologies and Efficiency in the Process of Image Noise Evaluation

IBDNet: Lightweight Network for On-orbit Image Blind Denoising

Aggregating Rich Deep Semantic Features for Fine-Grained Place Classification

Improving Reliability of Object Detection for Lunar Craters using Monte Carlo Dropout

An improved convolutional neural network for steganalysis in the scenario of reuse of the stego-key

A New Learning-based One Shot Detection Framework For Natural Images

Dense Receptive Field Network: A Backbone Network for Object Detection

Referring Expression Comprehension via Co-attention and Visual Context

Comparison between U-Net and U-ReNet models in OCR tasks

Severe Convective Weather Classification in Remote Sensing Images by Semantic Segmentation

Action Recognition Based on Divide-and-conquer

An Adaptive Feature Channel Weighting Scheme for Correlation Tracking

In-silico staining from bright-field and fluorescent images using deep learning

A lightweight neural network for hard exudate segmentation of fundus image

Attentional Residual Dense Factorized Network for Real-time Semantic Segmentation

Random drop loss for tiny object segmentation: Application to lesion segmentation in fundus images

Flow2Seg: Motion-Aided Semantic Segmentation

COCO_TS Dataset: Pixel-level Annotations Based on Weak Supervision for Scene Text Segmentation

Learning Deep Structured Multi-Scale Features for crisp and occlusion edge detection

Graph-Boosted Attentive Network for Semantic Body Parsing

A Global-Local Architecture Constrained by Multiple Attributes for Person Re-identification

Recurrent Connections Aid Occluded Object Recognition by Discounting Occluders

Learning Relational-Structural Networks for Robust Face Alignment

An Efficient 3D-NAS Method for Video-based Gesture Recognition

Robustness of deep LSTM networks in freehand gesture recognition

Delving into the Impact of Saliency Detector: A GeminiNet for Accurate Saliency Detection

FCN Salient Object Detection Using Region Cropping

Object-Level Salience Detection By Progressively Enhanced Network

Action unit assisted Facial Expression Recognition

Discriminative Feature Learning using Two-stage Training Strategy for Facial Expression Recognition

Action Units Classification using ClusWiSARD

Automatic Estimation of Dog Age: The DogAge Dataset and Challenge

Neural Network 3D Body Pose Tracking and Prediction for Motion-to-Photon Latency Compensation in Distributed Virtual Reality

Variational Deep Embedding with Regularized Student-t Mixture Model

A mixture-of-experts model for vehicle prediction using an online learning approach

An Application of Convolutional Neural Networks for Analyzing Dogs' Sleep Patterns

On the Inability of Markov Models to Capture Criticality in Human Mobility

LSTM with Uniqueness Attention for Human Activity Recognition

Comparative Research on SOM with Torus and Sphere Topologies for Peculiarity Classification of Flat Finishing Skill Training

Generative Creativity: Adversarial Learning for Bionic Design

Self-attention StarGAN for Multi-domain Image-to-image Translation

Generative Adversarial Networks for Operational Scenario Planing of Renewable Energy Farms: A Study on Wind and Photovoltaic

Constraint-Based Visual Generation

Text to Image Synthesis based on Multiple Discrimination

Disentangling Latent Factors of Variational Auto-Encoder with Whitening

Training Discriminative Models to Evaluate Generative Ones

Scene Graph Generation via Convolutional Message Passing and Class-aware Memory Embeddings

Change Detection in Satellite Images using Reconstruction Errors of Joint Autoencoders

Physical Adversarial Attacks by Projecting Perturbations

Improved Forward-backward Propagation to Generate Adversarial Examples

Incremental Learning of GAN for Detecting Multiple Adversarial Attacks

Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples

DCT:Differential Combination Testing of Deep Learning Systems

Restoration as a Defense Against Adversarial Perturbations for Spam Image Detection

HLR: Generating Adversarial Examples by High-Level Representations.

Other Format:

Printed edition:

ISBN:

978-3-030-30508-6

9783030305086

Access Restriction:

Restricted for use by site license.