scholarly journals Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction

Sensors ◽  
2017 ◽  
Vol 17 (3) ◽  
pp. 637 ◽  
Author(s):  
Dat Nguyen ◽  
Ki Kim ◽  
Hyung Hong ◽  
Ja Koo ◽  
Min Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Visible Light ◽  
Convolutional Neural Network ◽  
Human Body ◽  
Image Feature ◽  
Gender Recognition ◽  
Thermal Camera ◽  
Body Images ◽  
Image Feature Extraction

scholarly journals Automatic Lip-Reading System Based on Deep Convolutional Neural Network and Attention-Based Long Short-Term Memory

2019 ◽  
Vol 9 (8) ◽  
pp. 1599 ◽  
Author(s):  
Yuanyao Lu ◽  
Hongbo Li
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Image Features ◽  
Attention Mechanism ◽  
Image Feature ◽  
Fusion Model ◽  
Image Feature Extraction ◽  
Lip Reading

With the improvement of computer performance, virtual reality (VR) as a new way of visual operation and interaction method gives the automatic lip-reading technology based on visual features broad development prospects. In an immersive VR environment, the user’s state can be successfully captured through lip movements, thereby analyzing the user’s real-time thinking. Due to complex image processing, hard-to-train classifiers and long-term recognition processes, the traditional lip-reading recognition system is difficult to meet the requirements of practical applications. In this paper, the convolutional neural network (CNN) used to image feature extraction is combined with a recurrent neural network (RNN) based on attention mechanism for automatic lip-reading recognition. Our proposed method for automatic lip-reading recognition can be divided into three steps. Firstly, we extract keyframes from our own established independent database (English pronunciation of numbers from zero to nine by three males and three females). Then, we use the Visual Geometry Group (VGG) network to extract the lip image features. It is found that the image feature extraction results are fault-tolerant and effective. Finally, we compare two lip-reading models: (1) a fusion model with an attention mechanism and (2) a fusion model of two networks. The results show that the accuracy of the proposed model is 88.2% in the test dataset and 84.9% for the contrastive model. Therefore, our proposed method is superior to the traditional lip-reading recognition methods and the general neural networks.

scholarly journals An Advanced Relevance Feedback Method to Improve Performance of CBIR using Convolutional Neural Network and Comprehensive Values

2019 ◽  
Vol 9 (2) ◽  
pp. 5427-5438
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Activation Function ◽  
Image Feature ◽  
Similarity Measurement ◽  
Query Image ◽  
Image Production

Content-Based Image Retrieval (CBIR) is extensively used technique for image retrieval from large image databases. However, users are not satisfied with the conventional image retrieval techniques. In addition, the advent of web development and transmission networks, the number of images available to users continues to increase. Therefore, a permanent and considerable digital image production in many areas takes place. Quick access to the similar images of a given query image from this extensive collection of images pose great challenges and require proficient techniques. From query by image to retrieval of relevant images, CBIR has key phases such as feature extraction, similarity measurement, and retrieval of relevant images. However, extracting the features of the images is one of the important steps. Recently Convolutional Neural Network (CNN) shows good results in the field of computer vision due to the ability of feature extraction from the images. Alex Net is a classical Deep CNN for image feature extraction. We have modified the Alex Net Architecture with a few changes and proposed a novel framework to improve its ability for feature extraction and for similarity measurement. The proposal approach optimizes Alex Net in the aspect of pooling layer. In particular, average pooling is replaced by max-avg pooling and the non-linear activation function Maxout is used after every Convolution layer for better feature extraction. This paper introduces CNN for features extraction from images in CBIR system and also presents Euclidean distance along with the Comprehensive Values for better results. The proposed framework goes beyond image retrieval, including the large-scale database. The performance of the proposed work is evaluated using precision. The proposed work show better results than existing works.

scholarly journals Cell-Free DNA Pattern Identification and its Classification using Convolution Neural Network

2019 ◽  
Vol 9 (1) ◽  
pp. 5362-5364
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
User Interface ◽  
Graphical User Interface ◽  
Probabilistic Method ◽  
Convolution Neural Network ◽  
Image Feature ◽  
Image Feature Extraction ◽  
Free Dna ◽  
Dna Pattern

Circulating cell DNA (cfDNA) design identification assumes a cardinal job in fetal drug, transplantation and oncology. Be that as it may, it has additionally demonstrated to be a biomarker for different maladies. There are numerous order strategies by which the acknowledgment and arrangement should be possible. So as to have a superior time unpredictability and improve the precision further, this strategy targets distinguishing and arranging the general DNA examples and ailments related with them utilizing cfDNA Images in a Convolution Neural Network. A probabilistic method is used for cfDNA image feature extraction, fragmentation and interpretation. Graphical User Interface is the platform where this method is employed since it uses visual indicators in place of text-based interface. The aftereffects of this test demonstrate that the Convolution Neural Network calculation can perceive cfDNA successions accurately and successfully with no dubiety. Prepared with examples, the CNN can effectively characterize the picture surrendered to coordinated and unparalleled examples with numerical highlights.

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