scholarly journals A Multi-Column Deep Framework for Recognizing Artistic Media

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1277
Author(s):  
Yang ◽  
Min
Keyword(s):  
Neural Network ◽  
Computer Graphics ◽  
Deep Neural Network ◽  
Input Image ◽  
Local Scale ◽  
Gram Matrix ◽  
Final Decision ◽  
Texture Information ◽  
Artistic Medium ◽  
Image Datasets

We present a multi-column structured framework for recognizing artistic media from artwork images. We design the column of our framework using a deep neural network. Our key idea is to recognize the distinctive stroke texture of an artistic medium, which plays a key role in distinguishing artistic media. Since stroke texture is in a local scale, the whole image is not proper for recognizing the texture. Therefore, we devise two ideas for our framework: Sampling patches from an input image and employing a Gram matrix to extract the texture. The patches sampled from an input artwork image are processed in the columns of our framework to make local decisions on the patch, and the local decisions from the patches are merged to make a final decision for the input artwork image. Furthermore, we employ a Gram matrix, which is known to effectively capture texture information, to improve the accuracy of recognition. Our framework is trained and tested using two real artwork image datasets: WikiSet of traditional artwork images and YMSet of contemporary artwork images. Finally, we build SynthSet, which is a collection of synthesized artwork images from many computer graphics literature, and propose a guideline for evaluating the synthesized artwork images.

scholarly journals Optimized Multi-Model Biometric Based Human Authentication using Deep Neural Network

2019 ◽  
Vol 8 (3S3) ◽  
pp. 280-290
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Histogram Equalization ◽  
Extraction Process ◽  
Input Image ◽  
Suggested Approach ◽  
Biometric Systems ◽  
Recognition Systems ◽  
The Right ◽  
Facial Identification

Biometrics provides greater security and usability than conventional personal authentication methods. Fingerprints, facial identification systems and voice recognition systems are the features that biometric systems can use. To improve biometric authentication, the proposed method considered that the input image is iris and fingerprint; at first, pre-processing is performed through histogram equalization for all image inputs to enhance the image quality. Then the extraction process of the feature will be performed. The suggested method uses modified Local Binary Pattern (MLBP), GLCM with orientation transformation, and DWT features next to the extracted features to be combined for feature extraction. Then the optimum function is found with the Rider Optimization Algorithm (ROA) for all MLBP, GLCM and DWT. Eventually, the approach suggested is accepted. Deep Neural Network (DNN) performs the proposed authentication process. A DNN is a multilayered artificial neural network between the layers of input and output. The DNN finds the right mathematical manipulation to turn the input into the output, whether it is an acknowledged image or not. Suggested process quality is measured in terms of reliability recognition. In the MATLAB platform, the suggested approach is implemented.

Comparison of Input Image Dimensions for Ship Detection from KOMPSAT-5 SAR Image Using Deep Neural Network

2020 ◽  
Vol 102 (sp1) ◽  
Author(s):  
Wook Park ◽  
Won-Kyung Baek ◽  
Joong-Sun Won ◽  
Hyung-Sup Jung
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Input Image ◽  
Sar Image ◽  
Ship Detection

scholarly journals Image Classification: A Survey

2020 ◽  
Vol 1 (2) ◽  
pp. 1-9
Author(s):  
Ankita Singh ◽  
◽  
Pawan Singh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Language Processing ◽  
Classification Accuracy ◽  
Deep Neural Network ◽  
Learning Ability ◽  
Final Decision

The Classification of images is a paramount topic in artificial vision systems which have drawn a notable amount of interest over the past years. This field aims to classify an image, which is an input, based on its visual content. Currently, most people relied on hand-crafted features to describe an image in a particular way. Then, using classifiers that are learnable, such as random forest, and decision tree was applied to the extract features to come to a final decision. The problem arises when large numbers of photos are concerned. It becomes a too difficult problem to find features from them. This is one of the reasons that the deep neural network model has been introduced. Owing to the existence of Deep learning, it can become feasible to represent the hierarchical nature of features using a various number of layers and corresponding weight with them. The existing image classification methods have been gradually applied in real-world problems, but then there are various problems in its application processes, such as unsatisfactory effect and extremely low classification accuracy or then and weak adaptive ability. Models using deep learning concepts have robust learning ability, which combines the feature extraction and the process of classification into a whole which then completes an image classification task, which can improve the image classification accuracy effectively. Convolutional Neural Networks are a powerful deep neural network technique. These networks preserve the spatial structure of a problem and were built for object recognition tasks such as classifying an image into respective classes. Neural networks are much known because people are getting a state-of-the-art outcome on complex computer vision and natural language processing tasks. Convolutional neural networks have been extensively used.

scholarly journals A novel deep neural network for hidden target detection in images

2021 ◽  
Author(s):  
Rabeb Hendaoui ◽  
◽  
Vasif Nabiyev ◽  
Keyword(s):  
Neural Network ◽  
Target Detection ◽  
Deep Neural Network ◽  
Target Identification ◽  
Input Image ◽  
Second Phase ◽  
Natural Environments ◽  
Significant Similarity ◽  
Target Segmentation ◽  
Two Phases

The significant similarity between the hidden target and the background makes it difficult to find camouflaged people, such as warriors in warfare, or even camouflaged objects in natural environments. Hence, it is hard to ascertain these concealed targets. To address this issue, a novel deep neural network is proposed in this paper that produces an estimated mask within the hidden target for an input image. Our approach consists of two phases: hidden target segmentation and hidden target identification. For the first phase, we propose the Multilevel Attention Network (MA-Net), which generates the camouflaged target mask based on a Multi-Attention Module (MAM) that helps distinguish the hidden people from the background. Later on, the concealed target will be highlighted in the second phase. Experimental results on the camouflaged people dataset demonstrate that our proposed method can achieve state-of-the-art performance for hidden target detection.

scholarly journals Simultaneous Monocular Visual Odometry and Depth Reconstruction with Scale Recovery

2020 ◽  
Author(s):  
Guoliang Liu
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Visual Odometry ◽  
Input Image ◽  
View Synthesis ◽  
Depth Image ◽  
The Core ◽  
Depth Prediction ◽  
Camera Pose ◽  
Depth Reconstruction

In this paper, we propose a deep neural networkthat can estimate camera poses and reconstruct thefull resolution depths of the environment simultaneously usingonly monocular consecutive images. In contrast to traditionalmonocular visual odometry methods, which cannot estimatescaled depths, we here demonstrate the recovery of the scaleinformation using a sparse depth image as a supervision signalin the training step. In addition, based on the scaled depth,the relative poses between consecutive images can be estimatedusing the proposed deep neural network. Another novelty liesin the deployment of view synthesis, which can synthesize anew image of the scene from a different view (camera pose)given an input image. The view synthesis is the core techniqueused for constructing a loss function for the proposed neuralnetwork, which requires the knowledge of the predicted depthsand relative poses, such that the proposed method couples thevisual odometry and depth prediction together. In this way,both the estimated poses and the predicted depths from theneural network are scaled using the sparse depth image as thesupervision signal during training. The experimental results onthe KITTI dataset show competitive performance of our methodto handle challenging environments.<br>

scholarly journals Three Stream Network Model for Lung Cancer Classification in the CT Images

2021 ◽  
Vol 11 (1) ◽  
pp. 251-261
Author(s):  
T. Arumuga Maria Devi ◽  
V. I. Mebin Jose
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Deep Neural Network ◽  
Early Stage ◽  
Low Frequency ◽  
Ct Images ◽  
Input Image ◽  
Support Vector ◽  
Human Beings ◽  
Stream Network

Abstract Lung cancer is considered to be one of the deadly diseases that threaten the survival of human beings. It is a challenging task to identify lung cancer in its early stage from the medical images because of the ambiguity in the lung regions. This paper proposes a new architecture to detect lung cancer obtained from the CT images. The proposed architecture has a three-stream network to extract the manual and automated features from the images. Among these three streams, automated feature extraction as well as the classification is done using residual deep neural network and custom deep neural network. Whereas the manual features are the handcrafted features obtained using high and low-frequency sub-bands in the frequency domain that are classified using a Support Vector Machine Classifier. This makes the architecture robust enough to capture all the important features required to classify lung cancer from the input image. Hence, there is no chance of missing feature information. Finally, all the obtained prediction scores are combined by weighted based fusion. The experimental results show 98.2% classification accuracy which is relatively higher in comparison to other existing methods.

scholarly journals A deep neural network approach for sentiment analysis of medically related texts: an analysis of tweets related to concussions in sports

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Kayvan Tirdad ◽  
Alex Dela Cruz ◽  
Alireza Sadeghian ◽  
Michael Cusimano
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Single Layer ◽  
Final Decision ◽  
Network Approach ◽  
Neural Network Approach ◽  
Neural Network System ◽  
Sporting Activities

AbstractAnnually, over three million people in North America suffer concussions. Every age group is susceptible to concussion, but youth involved in sporting activities are particularly vulnerable, with about 6% of all youth suffering a concussion annually. Youth who suffer concussion have also been shown to have higher rates of suicidal ideation, substance and alcohol use, and violent behaviors. A significant body of research over the last decade has led to changes in policies and laws intended to reduce the incidence and burden of concussions. However, it is also clear that youth engaging in high-risk activities like sport often underreport concussion, while others may embellish reports for specific purposes. For such policies and laws to work, they must operate effectively within a facilitative social context so understanding the culture around concussion becomes essential to reducing concussion and its consequences. We present an automated deep neural network approach to analyze tweets with sport-related concussion context to identify the general public’s sentiment towards concerns in sport-related concussion. A single-layer and multi-layer convolutional neural networks, Long Short-Term Memory (LSTM) networks, and Bidirectional LSTM were trained to classify the sentiments of the tweets. Afterwards, we train an ensemble model to aggregate the predictions of our networks to provide a final decision of the tweet’s sentiment. The system achieves an evaluation F1 score of 62.71% based on Precision and Recall. The trained system is then used to analyze the tweets in the FIFA World Cup 2018 to measure audience reaction to events involving concussion. The neural network system provides an understanding of the culture around concussion through sentiment analysis.

scholarly journals Simultaneous Monocular Visual Odometry and Depth Reconstruction with Scale Recovery

2020 ◽  
Author(s):  
Guoliang Liu
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Visual Odometry ◽  
Input Image ◽  
View Synthesis ◽  
Depth Image ◽  
The Core ◽  
Depth Prediction ◽  
Camera Pose ◽  
Depth Reconstruction

In this paper, we propose a deep neural networkthat can estimate camera poses and reconstruct thefull resolution depths of the environment simultaneously usingonly monocular consecutive images. In contrast to traditionalmonocular visual odometry methods, which cannot estimatescaled depths, we here demonstrate the recovery of the scaleinformation using a sparse depth image as a supervision signalin the training step. In addition, based on the scaled depth,the relative poses between consecutive images can be estimatedusing the proposed deep neural network. Another novelty liesin the deployment of view synthesis, which can synthesize anew image of the scene from a different view (camera pose)given an input image. The view synthesis is the core techniqueused for constructing a loss function for the proposed neuralnetwork, which requires the knowledge of the predicted depthsand relative poses, such that the proposed method couples thevisual odometry and depth prediction together. In this way,both the estimated poses and the predicted depths from theneural network are scaled using the sparse depth image as thesupervision signal during training. The experimental results onthe KITTI dataset show competitive performance of our methodto handle challenging environments.<br>

A deep neural network based context-aware smart epidemic surveillance in smart cities

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Harsuminder Kaur Gill ◽  
Vivek Kumar Sehgal ◽  
Anil Kumar Verma
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Smart Cities ◽  
Geographical Area ◽  
Final Decision ◽  
Context Aware ◽  
Content Type ◽  
Data Points ◽  
Risk Areas ◽  
The Impact

PurposeEpidemics not only affect the public health but also are a threat to a nation's growth and economy as well. Early prediction of epidemic can be beneficial to take preventive measures and to reduce the impact of epidemic in an area.Design/methodology/approachA deep neural network (DNN) based context aware smart epidemic system has been proposed to prevent and monitor epidemic spread in a geographical area. Various neural networks (NNs) have been used: LSTM, RNN, BPNN to detect the level of disease, direction of spread of disease in a geographical area and marking the high-risk areas. Multiple DNNs collect and process various data points and these DNNs are decided based on type of data points. Output of one DNN is used by another DNN to reach to final prediction.FindingsThe experimental evaluation of the proposed framework achieved the accuracy of 87% for the synthetic dataset generated for Zika epidemic in Brazil in 2016.Originality/valueThe proposed framework is designed in a way that every data point is carefully processed and contributes to the final decision. These multiple DNNs will act as a single DNN for the end user.

scholarly journals An Efficient Prediction System for Diabetes Disease Based on Deep Neural Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Tawfik Beghriche ◽  
Mohamed Djerioui ◽  
Youcef Brik ◽  
Bilal Attallah ◽  
Samir Brahim Belhaouari
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Premature Mortality ◽  
Healthcare Services ◽  
Performance Comparison ◽  
Machine Learning Techniques ◽  
Medical Decision ◽  
Final Decision

One of the main reasons for disability and premature mortality in the world is diabetes disease, which can cause different sorts of damage to organs such as kidneys, eyes, and heart arteries. The deaths by diabetes are increasing each year, so the need to develop a system that can effectively diagnose diabetes patients becomes inevitable. In this work, an efficient medical decision system for diabetes prediction based on Deep Neural Network (DNN) is presented. Such algorithms are state-of-the-art in computer vision, language processing, and image analysis, and when applied in healthcare for prediction and diagnosis purposes, these algorithms can produce highly accurate results. Moreover, they can be combined with medical knowledge to improve decision-making effectiveness, adaptability, and transparency. A performance comparison between the DNN algorithm and some well-known machine learning techniques as well as the state-of-the-art methods is presented. The obtained results showed that our proposed method based on the DNN technique provides promising performances with an accuracy of 99.75% and an F1-score of 99.66%. This improvement can reduce time, efforts, and labor in healthcare services as well as increasing the final decision accuracy.

Sign in / Sign up

Export Citation Format

Share Document