Person re-identification using Hybrid Task Convolutional Neural Network in camera sensor networks

2020 ◽  
Vol 97 ◽  
pp. 102018 ◽  
Author(s):  
Shuang Liu ◽  
Wenmin Huang ◽  
Zhong Zhang
Keyword(s):  
Neural Network ◽  
Sensor Networks ◽  
Convolutional Neural Network ◽  
Camera Sensor

scholarly journals Recognition of Damaged Arrow-Road Markings by Visible Light Camera Sensor Based on Convolutional Neural Network

Sensors ◽  
2016 ◽  
Vol 16 (12) ◽  
pp. 2160 ◽  
Author(s):  
Husan Vokhidov ◽  
Hyung Hong ◽  
Jin Kang ◽  
Toan Hoang ◽  
Kang Park
Keyword(s):  
Neural Network ◽  
Visible Light ◽  
Convolutional Neural Network ◽  
Camera Sensor

scholarly journals Multimodal Biometric Recognition Based on Convolutional Neural Network by the Fusion of Finger-Vein and Finger Shape Using Near-Infrared (NIR) Camera Sensor

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2296 ◽  
Author(s):  
Wan Kim ◽  
Jong Min Song ◽  
Kang Ryoung Park
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Near Infrared ◽  
Image Acquisition ◽  
Multimodal Biometrics ◽  
Finger Vein ◽  
Nir Light ◽  
Vein Recognition ◽  
Finger Vein Recognition ◽  
Camera Sensor

Finger-vein recognition, which is one of the conventional biometrics, hinders fake attacks, is cheaper, and it features a higher level of user-convenience than other biometrics because it uses miniaturized devices. However, the recognition performance of finger-vein recognition methods may decrease due to a variety of factors, such as image misalignment that is caused by finger position changes during image acquisition or illumination variation caused by non-uniform near-infrared (NIR) light. To solve such problems, multimodal biometric systems that are able to simultaneously recognize both finger-veins and fingerprints have been researched. However, because the image-acquisition positions for finger-veins and fingerprints are different, not to mention that finger-vein images must be acquired in NIR light environments and fingerprints in visible light environments, either two sensors must be used, or the size of the image acquisition device must be enlarged. Hence, there are multimodal biometrics based on finger-veins and finger shapes. However, such methods recognize individuals that are based on handcrafted features, which present certain limitations in terms of performance improvement. To solve these problems, finger-vein and finger shape multimodal biometrics using near-infrared (NIR) light camera sensor based on a deep convolutional neural network (CNN) are proposed in this research. Experimental results obtained using two types of open databases, the Shandong University homologous multi-modal traits (SDUMLA-HMT) and the Hong Kong Polytechnic University Finger Image Database (version 1), revealed that the proposed method in the present study features superior performance to the conventional methods.

An active model for ranging by deep convolutional neural network and elephant herding optimization algorithm (DCNN-EHOA) in WSNs

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Adireddy Rajasekhar Reddy ◽  
Appini Narayana Rao
Keyword(s):  
Neural Network ◽  
Sensor Networks ◽  
Convolutional Neural Network ◽  
Signal Propagation ◽  
Distance Measurement ◽  
Deep Convolutional Neural Network ◽  
Sensor Nodes ◽  
Target Node ◽  
Content Type ◽  
Accurate Position

Purpose In modern technology, the wireless sensor networks (WSNs) are generally most promising solutions for better reliability, object tracking, remote monitoring and more, which is directly related to the sensor nodes. Received signal strength indication (RSSI) is main challenges in sensor networks, which is fully depends on distance measurement. The learning algorithm based traditional models are involved in error correction, distance measurement and improve the accuracy of effectiveness. But, most of the existing models are not able to protect the user’s data from the unknown or malicious data during the signal transmission. The simulation outcomes indicate that proposed methodology may reach more constant and accurate position states of the unknown nodes and the target node in WSNs domain than the existing methods. Design/methodology/approach This paper present a deep convolutional neural network (DCNN) from the adaptation of machine learning to identify the problems on deep ranging sensor networks and overthrow the problems of unknown sensor nodes localization in WSN networks by using instance parameters of elephant herding optimization (EHO) technique and which is used to optimize the localization problem. Findings In this proposed method, the signal propagation properties can be extracted automatically because of this image data and RSSI data values. Rest of this manuscript shows that the ECO can find the better performance analysis of distance estimation accuracy, localized nodes and its transmission range than those traditional algorithms. ECO has been proposed as one of the main tools to promote a transformation from unsustainable development to one of sustainable development. It will reduce the material intensity of goods and services. Originality/value The proposed technique is compared to existing systems to show the proposed method efficiency. The simulation results indicate that this proposed methodology can achieve more constant and accurate position states of the unknown nodes and the target node in WSNs domain than the existing methods.

CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

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