scholarly journals Supervised Learning Based Peripheral Vision System for Immersive Visual Experiences for Extended Display

2021 ◽  
Vol 11 (11) ◽  
pp. 4726
Author(s):  
Muhammad Ayaz Shirazi ◽  
Riaz Uddin ◽  
Min-Young Kim
Keyword(s):  
Supervised Learning ◽  
Deep Neural Network ◽  
Peripheral Vision ◽  
Vision System ◽  
Video Frame ◽  
Living Room ◽  
Video Content ◽  
Video Display ◽  
Home Appliance ◽  
Display Content

Video display content can be extended to the walls of the living room around the TV using projection. The problem of providing appropriate projection content is hard for the computer and we solve this problem with deep neural network. We propose the peripheral vision system that provides the immersive visual experiences to the user by extending the video content using deep learning and projecting that content around the TV screen. The user may manually create the appropriate content for the existing TV screen, but it is too expensive to create it. The PCE (Pixel context encoder) network considers the center of the video frame as input and the outside area as output to extend the content using supervised learning. The proposed system is expected to pave a new road to the home appliance industry, transforming the living room into the new immersive experience platform.

Semi-Supervised Learning and Deep Neural Network on Detection of Roadway Cracking Using Unmanned Aerial System Imagery

2021 ◽  
Author(s):  
Long Ngo Hoang Truong ◽  
Edward Clay ◽  
Omar E. Mora ◽  
Wen Cheng ◽  
Maninder Kaur ◽  
...  
Keyword(s):  
Neural Network ◽  
Supervised Learning ◽  
Deep Neural Network ◽  
Unmanned Aerial System

scholarly journals Video Error-Resilience Research Based on Error-Resilient Screen Content

2020 ◽  
Vol 10 (14) ◽  
pp. 4923
Author(s):  
Zhe Liu ◽  
He Chen ◽  
Songlin Sun
Keyword(s):  
Video Transmission ◽  
Video Quality ◽  
Region Of Interest ◽  
Error Resilience ◽  
Transmission Error ◽  
Video Frame ◽  
Video Content ◽  
Selection Mechanism ◽  
Error Resilient ◽  
Screen Content

In order to make video transmission more stable, various error-resilient mechanisms are proposed on video coding in the literature. However, the redundancy mechanism behind classical redundant coding algorithms is relatively simple and is not suitable for the network environment and video content in the context of screen content sequence with multiple abrupt frames and still frames. Motivated by this, a frame-level coding selection mechanism is proposed in this paper for the error-resilience transmission of screen content, where additional code stream or redundant information is considered to improve error-resilient performance with redundant coding and acceptable video quality is obtained in the case of frame transmission error. In addition, selective allocation redundancy is conducted to take the importance of the video frame ROI (region of interest) area into account in the co-encoding process. As a result, the redundancy insertion efficiency and the reliability are improved in return. The corresponding experiments validate the effectiveness of the schemes proposed in this paper.

scholarly journals Impacts of Video Display on Purchase Intention for Digital and Home Appliance Products—Empirical Study from China

2019 ◽  
Vol 11 (11) ◽  
pp. 224
Author(s):  
Ruohong Hao ◽  
Bingjia Shao ◽  
Rong Ma
Keyword(s):  
Purchase Intention ◽  
Video Display ◽  
Internet Technologies ◽  
Video Tutorial ◽  
Online Retailers ◽  
Online Trading ◽  
Home Appliance ◽  
Empirical Tests ◽  
Effective Product ◽  
Video Presentations

Rapid online trading expansion and the bloom of internet technologies has raised the importance of effective product video presentations for online retailers. This article developed a model for the impacts of video presentations on purchase intention for digital and home appliance products. Four group experiments were designed, and empirical tests were performed. This research found that presenting videos on how to use digital and home appliance products increased purchase intention by raising the information gained by customers. Meanwhile, video tutorial information had insignificant effects related to the knowledge and experience of customers.

Binocular peripheral vision system

1997 ◽  
Author(s):  
Thierry Maniere ◽  
Ryad Benosman ◽  
Claude Gastaud ◽  
Jean Devars
Keyword(s):  
Peripheral Vision ◽  
Vision System

scholarly journals FPGA-Based Stereo-Vision System Mimicking Human Binocular Vision

2021 ◽  
Author(s):  
Gvarami Labartkava
Keyword(s):  
Real Time ◽  
Binocular Vision ◽  
Video Processing ◽  
High Performance ◽  
Moving Objects ◽  
Peripheral Vision ◽  
Vision System ◽  
Processing System ◽  
Object Motion ◽  
Human Vision

Human vision is a complex system which involves processing frames and retrieving information in a real-time with optimization of the memory, energy and computational resources usage. It can be widely utilized in many real-world applications from security systems to space missions. The research investigates fundamental principles of human vision and accordingly develops a FPGA-based video processing system with binocular vision, capable of high performance and real-time tracking of moving objects in 3D space. The undertaken research and implementation consist of: 1. Analysis of concepts and methods of human vision system; 2. Development stereo and peripheral vision prototype of a system-on-programmable chip (SoPC) for multi-object motion detection and tracking; 3. Verification, test run and analysis of the experimental results gained on the prototype and associated with the performance constraints; The implemented system proposes a platform for real-time applications which are limited in current approaches.

scholarly journals Deep Neural Network for Protein Contact Prediction by Weighting Sequences in a Multiple Sequence Alignment

2018 ◽  
Author(s):  
Hiroyuki Fukuda ◽  
Kentaro Tomii
Keyword(s):  
Neural Network ◽  
Supervised Learning ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Structure Prediction ◽  
Deep Neural Network ◽  
Multiple Sequence ◽  
Contact Prediction ◽  
Meta Learning ◽  
Correlation Information

AbstractProtein contact prediction is a crucially important step for protein structure prediction. To predict a contact, approaches of two types are used: evolutionary coupling analysis (ECA) and supervised learning. ECA uses a large multiple sequence alignment (MSA) of homologue sequences and extract correlation information between residues. Supervised learning uses ECA analysis results as input features and can produce higher accuracy. As described herein, we present a new approach to contact prediction which can both extract correlation information and predict contacts in a supervised manner directly from MSA using a deep neural network (DNN). Using DNN, we can obtain higher accuracy than with earlier ECA methods. Simultaneously, we can weight each sequence in MSA to eliminate noise sequences automatically in a supervised way. It is expected that the combination of our method and other meta-learning methods can provide much higher accuracy of contact prediction.

scholarly journals Compression and Decompression which Use Deep Neural Network

2022 ◽  
Author(s):  
Jinxin Wei
Keyword(s):  
Neural Network ◽  
Supervised Learning ◽  
Compression Ratio ◽  
Deep Neural Network ◽  
Inverse Function

<p>an auto-encoder which can be split into two parts is designed. The two parts can work well separately. The top half is an abstract network which is trained by supervised learning and can be used to classify and regress. The bottom half is a concrete network which is accomplished by inverse function and trained by self-supervised learning. It can generate the input of abstract network from concept or label. It is tested by tensorflow and mnist dataset. The abstract network is like LeNet-5. The concrete network is the inverse of the abstract network.Lossy compression can achieved by the test. The large compression ratio which is 19.6 is achieved. The decompression performance is ok through regression which treats classification as regression.</p>

scholarly journals Compression and Decompression which Use Deep Neural Network

2021 ◽  
Author(s):  
Jinxin Wei
Keyword(s):  
Neural Network ◽  
Supervised Learning ◽  
Compression Ratio ◽  
Deep Neural Network ◽  
Inverse Function

<p>an auto-encoder which can be split into two parts is designed. The two parts can work well separately. The top half is an abstract network which is trained by supervised learning and can be used to classify and regress. The bottom half is a concrete network which is accomplished by inverse function and trained by self-supervised learning. It can generate the input of abstract network from concept or label. It is tested by tensorflow and mnist dataset. The abstract network is like LeNet-5. The concrete network is the inverse of the abstract network.Lossy compression can achieved by the test. The large compression ratio which is 19.6 is achieved. The decompression performance is ok through regression which treats classification as regression.</p>

scholarly journals A Deep Learning Algorithm for the Max-Cut Problem Based on Pointer Network Structure with Supervised Learning and Reinforcement Learning Strategies

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 298 ◽  
Author(s):  
Shenshen Gu ◽  
Yue Yang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Reinforcement Learning ◽  
Combinatorial Optimization ◽  
Supervised Learning ◽  
Learning Strategies ◽  
Large Scale ◽  
Deep Neural Network ◽  
Max Cut Problem ◽  
Cut Problems

The Max-cut problem is a well-known combinatorial optimization problem, which has many real-world applications. However, the problem has been proven to be non-deterministic polynomial-hard (NP-hard), which means that exact solution algorithms are not suitable for large-scale situations, as it is too time-consuming to obtain a solution. Therefore, designing heuristic algorithms is a promising but challenging direction to effectively solve large-scale Max-cut problems. For this reason, we propose a unique method which combines a pointer network and two deep learning strategies (supervised learning and reinforcement learning) in this paper, in order to address this challenge. A pointer network is a sequence-to-sequence deep neural network, which can extract data features in a purely data-driven way to discover the hidden laws behind data. Combining the characteristics of the Max-cut problem, we designed the input and output mechanisms of the pointer network model, and we used supervised learning and reinforcement learning to train the model to evaluate the model performance. Through experiments, we illustrated that our model can be well applied to solve large-scale Max-cut problems. Our experimental results also revealed that the new method will further encourage broader exploration of deep neural network for large-scale combinatorial optimization problems.

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