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< Page ,Total 17 >
Visibility restoration of single foggy images under local surface analysis SCIE
期刊论文 | 2019 , 341 , 212-226 | NEUROCOMPUTING
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Abstract :

A variety of empirical methods, which are represented by dark channel prior, have been proved effective for haze removal. However, undesirable artifacts and color distortion are still left on some of dehazing results, which directly determines the performance of computer vision tasks. Different from traditional statistical methods, we apply Multi-dimensional theory that quickly predicts haze free images. To this purpose, the local manifold similarity is employed to reduce the error of initial estimation. Moreover, contrast-based Gaussian curvature is also introduced in order to obtain the smoothness transmission map. Compared with conventional methods, quantitative and qualitative comparisons have shown our approach improvement visual results. (C) 2019 Elsevier B.V. All rights reserved.

Keyword :

Local similarity tangent plane Visibility restoration Single image dehazing

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GB/T 7714 He, Lin-Yuan , Kun, Liu , Zhao, Ji-Zhong et al. Visibility restoration of single foggy images under local surface analysis [J]. | NEUROCOMPUTING , 2019 , 341 : 212-226 .
MLA He, Lin-Yuan et al. "Visibility restoration of single foggy images under local surface analysis" . | NEUROCOMPUTING 341 (2019) : 212-226 .
APA He, Lin-Yuan , Kun, Liu , Zhao, Ji-Zhong , Bi, Du-Yan . Visibility restoration of single foggy images under local surface analysis . | NEUROCOMPUTING , 2019 , 341 , 212-226 .
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Hardware Implementation of Reconfigurable Separable Convolution EI CPCI-S Scopus
会议论文 | 2018 , 232-237 | 17th IEEE-Computer-Society Annual Symposium on VLSI (ISVLSI)
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Abstract :

Convolution operations occupy large amounts of computation resource in convolutional neural networks (CNNs). Separable convolution can greatly reduce computational complexity. Unfortunately, most trained kernels in CNNs are not separable. In this paper, least squares approach is applied to decompose a non-separable 2D kernel into two 1D kernels. A reconfigurable convolutional architecture is proposed to convert a 2D convolution into 1D convolution in convolutional layers. Moreover, a denoising CNN is mapped to the proposed convolution architecture. Experimental results show that the hardware architecture can restore a 1280x 720 image in 0.83s, which achieves an 8.4x speed-up over GPU implementation. Verification experiments demonstrate that our approach and hardware architecture can drastically reduce the computational complexity in convolution operations without sacrificing the performance.

Keyword :

hardware implementation reconfigurable architecture separable convolution convolutional neural networks

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GB/T 7714 Rao, Lei , Zhang, Bin , Zhao, Jizhong . Hardware Implementation of Reconfigurable Separable Convolution [C] . 2018 : 232-237 .
MLA Rao, Lei et al. "Hardware Implementation of Reconfigurable Separable Convolution" . (2018) : 232-237 .
APA Rao, Lei , Zhang, Bin , Zhao, Jizhong . Hardware Implementation of Reconfigurable Separable Convolution . (2018) : 232-237 .
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Effective haze removal under mixed domain and retract neighborhood EI SCIE Scopus
期刊论文 | 2018 , 293 , 29-40 | NEUROCOMPUTING
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Abstract :

The quality of images would exhibit degraded visibility during inclement weather conditions. We proposed a novel method for estimating an optimal transmission map and recovering the real scene. Under HSI color model, saturation layer and intensity layer are mixed together for obtaining the rough transmission. To avoid halos and artifacts, proposed approach employs edge preserving constraint of shrinkage neighborhood on the color line model, which can maintain maximum smoothness and sharp edges in the refined transmission map. A comparative experiment with a few previous methods shows improvement visual results. (c) 2018 Elsevier B.V. All rights reserved.

Keyword :

Visibility restoration Single image dehazing Color line model

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GB/T 7714 He, Lin-Yuan , Zhao, Ji-Zhong , Bi, Du-Yan . Effective haze removal under mixed domain and retract neighborhood [J]. | NEUROCOMPUTING , 2018 , 293 : 29-40 .
MLA He, Lin-Yuan et al. "Effective haze removal under mixed domain and retract neighborhood" . | NEUROCOMPUTING 293 (2018) : 29-40 .
APA He, Lin-Yuan , Zhao, Ji-Zhong , Bi, Du-Yan . Effective haze removal under mixed domain and retract neighborhood . | NEUROCOMPUTING , 2018 , 293 , 29-40 .
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SCFSen: A Sensor Node for Regional Soil Carbon Flux Monitoring SCIE PubMed
期刊论文 | 2018 , 18 (11) | SENSORS
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Abstract :

Estimation of regional soil carbon flux is very important for the study of the global carbon cycle. The spatial heterogeneity of soil respiration prevents the actual status of regional soil carbon flux from being revealed by measurements of only one or a few spatial sampling positions, which are usually used by traditional studies for the limitation of measurement instruments, so measuring in many spatial positions is very necessary. However, the existing instruments are expensive and cannot communicate with each other, which prevents them from meeting the requirement of synchronous measurements in multiple positions. Therefore, we designed and implemented an instrument for soil carbon flux measuring based on dynamic chamber method, SCFSen, which can measure soil carbon flux and communicate with each other to construct a sensor network. In its working stage, a SCFSen node measures the concentration of carbon in the chamber with an infrared carbon dioxide sensor for certain times periodically, and then the changing rate of the measurements is calculated, which can be converted to the corresponding value of soil carbon flux in the position during the short period. A wireless sensor network system using SCFSens as soil carbon flux sensing nodes can carry out multi-position measurements synchronously, so as to obtain the spatial heterogeneity of soil respiration. Furthermore, the sustainability of such a wireless sensor network system makes the temporal variability of regional soil carbon flux can also be obtained. So SCFSen makes thorough monitoring and accurate estimation of regional soil carbon flux become more feasible.

Keyword :

spatial and temporal heterogeneity soil carbon flux measurement wireless sensor networks dynamic chamber method

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GB/T 7714 Wang, Guoying , Wu, Xiaoping , Mo, Lufeng et al. SCFSen: A Sensor Node for Regional Soil Carbon Flux Monitoring [J]. | SENSORS , 2018 , 18 (11) .
MLA Wang, Guoying et al. "SCFSen: A Sensor Node for Regional Soil Carbon Flux Monitoring" . | SENSORS 18 . 11 (2018) .
APA Wang, Guoying , Wu, Xiaoping , Mo, Lufeng , Zhao, Jizhong . SCFSen: A Sensor Node for Regional Soil Carbon Flux Monitoring . | SENSORS , 2018 , 18 (11) .
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Hierarchical and Parallel Pipelined Heterogeneous SoC for Embedded Vision Processing EI SCIE Scopus
期刊论文 | 2018 , 28 (6) , 1434-1444 | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
WoS CC Cited Count: 1 SCOPUS Cited Count: 1
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Abstract :

Object recognition is widely used in vision computing for various applications. Traditional CPU and application specific integrated circuit for vision computing cannot provide high performance and enough flexibility, which limit the use of vision systems. In this paper, a hierarchical and parallel pipelined heterogeneous chip for object recognition is proposed to achieve high flexibility, high performance, and area efficiency. In addition, a reformulation of 3D position estimation is proposed. The method uses single precision to achieve the short computing time and accuracy requirement. The hardware resource is small. Application-specific components, such as connected component information extractor and information extraction accelerator, are designed for high performance. Reconfiguration processors and application-specific instruction set processor are introduced to improve flexibility. These components are connected to hierarchical parallel buses. The chip is fabricated in 180-nm CMOS technology and occupies 72.25 mm(2) with 1.09M bits on-chip memory. It delivers 204 GOPS + 665M FLOPS operations. The results show that this hierarchical and parallel pipelined heterogeneous chip is suitable for embedded vision systems.

Keyword :

3D position estimation application-specific instruction set processor (ASIP) system on chip (SoC) reconfigurable embedded vision system

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GB/T 7714 Zhang, Bin , Zhao, Chen , Mei, Kuizhi et al. Hierarchical and Parallel Pipelined Heterogeneous SoC for Embedded Vision Processing [J]. | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2018 , 28 (6) : 1434-1444 .
MLA Zhang, Bin et al. "Hierarchical and Parallel Pipelined Heterogeneous SoC for Embedded Vision Processing" . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 28 . 6 (2018) : 1434-1444 .
APA Zhang, Bin , Zhao, Chen , Mei, Kuizhi , Zhao, Jizhong , Zheng, Nanning . Hierarchical and Parallel Pipelined Heterogeneous SoC for Embedded Vision Processing . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2018 , 28 (6) , 1434-1444 .
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Verifiable Smart Packaging with Passive RFID EI Scopus
期刊论文 | 2018 | IEEE Transactions on Mobile Computing
SCOPUS Cited Count: 1
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Abstract :

Smart packaging adds sensing abilities to traditional packages. This paper investigates the possibility of using RF signals to test the internal status of packages and detect abnormal internal changes. Towards this goal, we design and implement a nondestructive package testing and verification system using commodity passive RFID systems, called Echoscope. Echoscope extracts unique features from the backscatter signals penetrating the internal space of a package and compares them with the previously collected features during the check-in phase. The use of backscatter signals guarantees that there is no difference in RF sources and the features reflecting the internal status will not be affected. Compared to other nondestructive testing methods such as X-ray and ultrasound, Echoscope is much cheaper and provides ubiquitous usage. Our experiments in practical environments show that Echoscope can achieve very high accuracy and is very sensitive to various types abnormal changes. IEEE

Keyword :

Backscatter signals Design and implements Internal changes Non destructive Nondestructive testing method RF signal Sensing abilities Verification systems

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GB/T 7714 WANG, GE , Han, Jinsong , Qian, Chen et al. Verifiable Smart Packaging with Passive RFID [J]. | IEEE Transactions on Mobile Computing , 2018 .
MLA WANG, GE et al. "Verifiable Smart Packaging with Passive RFID" . | IEEE Transactions on Mobile Computing (2018) .
APA WANG, GE , Han, Jinsong , Qian, Chen , Xi, Wei , Ding, Han , Jiang, Zhiping et al. Verifiable Smart Packaging with Passive RFID . | IEEE Transactions on Mobile Computing , 2018 .
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Secure Device Pairing via Handshake Detection EI SCIE Scopus CSCD
期刊论文 | 2018 , 23 (5) , 621-633 | TSINGHUA SCIENCE AND TECHNOLOGY
WoS CC Cited Count: 1 SCOPUS Cited Count: 1
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Abstract :

Multi-party applications are becoming popular due to the development of mobile smart devices. In this work, we explore Secure Device Pairing (SDP), a novel pairing mechanism, which allows users to use smart watches to detect the handshake between users, and use the shaking information to create security keys that are highly random. Thus, we perform device pairing without complicated operations. SDP dynamically adjusts the sensor's sampling frequency and uses different classifiers at varying stages to save the energy. A multi-level quantization algorithm is used to maximize the mutual information between two communicating entities without information leakage. We evaluate the main modules of SDP with 1800 sets of handshake data. Results show that the recognition accuracy of the handshake detection algorithm is 98.2%, and the power consumption is only 1/3 of that of the single sampling frequency classifier.

Keyword :

device pairing handshake detection key extraction

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GB/T 7714 Guo, Zhenge , Gao, Xueguang , Ma, Qiang et al. Secure Device Pairing via Handshake Detection [J]. | TSINGHUA SCIENCE AND TECHNOLOGY , 2018 , 23 (5) : 621-633 .
MLA Guo, Zhenge et al. "Secure Device Pairing via Handshake Detection" . | TSINGHUA SCIENCE AND TECHNOLOGY 23 . 5 (2018) : 621-633 .
APA Guo, Zhenge , Gao, Xueguang , Ma, Qiang , Zhao, Jizhong . Secure Device Pairing via Handshake Detection . | TSINGHUA SCIENCE AND TECHNOLOGY , 2018 , 23 (5) , 621-633 .
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A power-delay-product efficient and SEU-tolerant latch design EI SCIE Scopus
期刊论文 | 2017 , 14 (23) | IEICE ELECTRONICS EXPRESS | IF: 0.475
WoS CC Cited Count: 1 SCOPUS Cited Count: 3
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Abstract :

With the increasing high requirements for digital circuits in space application, devices with smaller feature size are put into use, which have more potential suffering from Single Event Upset (SEU) under certain radiation environment. In this paper, we propose a SEU-tolerant latch with low power-delay-product (PDP) that combines a SEU-tolerant cross-coupled structure with isolation operation of flipped state. Negative feedback paths are introduced to help recover the flipped state and can be cut off to speed up the write operation at transparent mode. Furthermore, isolation of flipped state is utilized to achieve better SEU-tolerance. The simulation results with 180 nm and 40 nm CMOS technology show that the proposed latch can achieve outstanding SEU-tolerance (Q(critical) > 10 fC) and a relatively low PDP of 0.0095 fsxJ for 40 nm CMOS technology.

Keyword :

power-delay-product latch single event upset

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GB/T 7714 Liu, Pei , Zhao, Tian , Liang, Feng et al. A power-delay-product efficient and SEU-tolerant latch design [J]. | IEICE ELECTRONICS EXPRESS , 2017 , 14 (23) .
MLA Liu, Pei et al. "A power-delay-product efficient and SEU-tolerant latch design" . | IEICE ELECTRONICS EXPRESS 14 . 23 (2017) .
APA Liu, Pei , Zhao, Tian , Liang, Feng , Zhao, Jizhong , Jiang, Peilin . A power-delay-product efficient and SEU-tolerant latch design . | IEICE ELECTRONICS EXPRESS , 2017 , 14 (23) .
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Combining local and global hypotheses in deep neural network for multi-label image classification EI SCIE Scopus
期刊论文 | 2017 , 235 , 38-45 | NEUROCOMPUTING | IF: 3.241
WoS CC Cited Count: 7 SCOPUS Cited Count: 9
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Abstract :

Multi-label image classification is a challenging problem in computer vision. Motivated by the recent development in image classification performance using Deep Neural Networks, in this work, we propose a flexible deep Convolutional Neural Network (CNN) framework, called Local-Global-CNN (LGC), to improve multi-label image classification performance. LGC consists of firstly a local level multi-label classifier which takes object segment hypotheses as inputs to a local CNN. The output results of these local hypotheses are aggregated together with max-pooling and then re-weighted to consider the label co-occurrence or inter-dependencies information by using a graphical model in the label space. LGC also utilizes a global CNN that is trained by multi-label images to directly predict the multiple labels from the input. The predictions of local and global level classifiers are finally fused together to obtain MAP estimation of the final multi-label prediction. The above LGC framework could benefit from a pre-train process with a large-scale single-label image dataset, e.g., ImageNet. Experimental results have shown that the proposed framework could achieve promising performance on Pascal VOC2007 and VOC2012 multi-label image dataset.

Keyword :

Multi-label classification Deep learning Convolutional neural network

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GB/T 7714 Yu, Qinghua , Wang, Jinjun , Zhang, Shizhou et al. Combining local and global hypotheses in deep neural network for multi-label image classification [J]. | NEUROCOMPUTING , 2017 , 235 : 38-45 .
MLA Yu, Qinghua et al. "Combining local and global hypotheses in deep neural network for multi-label image classification" . | NEUROCOMPUTING 235 (2017) : 38-45 .
APA Yu, Qinghua , Wang, Jinjun , Zhang, Shizhou , Gong, Yihong , Zhao, Jizhong . Combining local and global hypotheses in deep neural network for multi-label image classification . | NEUROCOMPUTING , 2017 , 235 , 38-45 .
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Haze Removal Using the Difference-Structure-Preservation Prior EI SCIE Scopus
期刊论文 | 2017 , 26 (3) , 1063-1075 | IEEE TRANSACTIONS ON IMAGE PROCESSING | IF: 5.071
WoS CC Cited Count: 14 SCOPUS Cited Count: 17
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Abstract :

Fog cover is generally present in outdoor scenes, which limits the potential for efficient information extraction from images. In this paper, the goal of the developed algorithm is to obtain an optimal transmission map as well as to remove hazes from a single input image. To solve the problem, we meticulously analyze the optical model and recast the initial transmission map under an additional boundary prior. For better preservation of the results, the difference-structure-preservation dictionary could be learned, such that the local consistency features of the transmission map could be well preserved after coefficient shrinkage. Experimental results show that the method preserves the natural appearance of the image.

Keyword :

local depth consistency difference-structure-preservation Visibility restoration contrast restoration single image dehazing

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GB/T 7714 He, Linyuan , Zhao, Jizhong , Zheng, Nanning et al. Haze Removal Using the Difference-Structure-Preservation Prior [J]. | IEEE TRANSACTIONS ON IMAGE PROCESSING , 2017 , 26 (3) : 1063-1075 .
MLA He, Linyuan et al. "Haze Removal Using the Difference-Structure-Preservation Prior" . | IEEE TRANSACTIONS ON IMAGE PROCESSING 26 . 3 (2017) : 1063-1075 .
APA He, Linyuan , Zhao, Jizhong , Zheng, Nanning , Bi, Duyan . Haze Removal Using the Difference-Structure-Preservation Prior . | IEEE TRANSACTIONS ON IMAGE PROCESSING , 2017 , 26 (3) , 1063-1075 .
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