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Depth image Super-resolution via Two-Branch Network 6th

Depth Image Super-resolution via Two-Branch Network

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6th international conference on Cognitive Systems and signal processing, ICCSIP 2021

作者： Guo, Jiaxin Xiong, Rong Ou, Yongsheng Wang, Lin Liu, Chao Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen China

ISBN: (纸本)9789811692468

With the continuous development of sensors, a number of inexpensive and effective depth cameras have shown up, which have greatly contributed to the development of autonomous driving and 3D reconstruction technologies. However, the depth images captured by low-cost depth cameras are low-resolution, which is difficult to meet the needs of practical applications. We propose a two-branch network to achieve depth map super-resolution with high-resolution guidance image, which can be viewed as a prior to guide the low-resolution depth map to restore the missing high-frequency details of structures. To emphasize the guidance role of high-resolution images, we use spatially-variant kernels based on the guidance feature map to replace the original convolution kernels. In addition, in order to extract the feature maps of the depth images more effectively, we add the channel attention mechanism between convolution layers. Our network is trained end-to-end, supporting various sizes of input images because the network backbone uses full convolution and no fully connected layers. The proposal model is trained only on a certain dataset under three super-resolution factors and utilized directly on other datasets without fine-tuning. We show the effectiveness of our model by comparing it with state-of-art methods. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Convolution

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A 3D Geometry Reconstruction Method for Space Targets Utilizing Improved ISAR image Sequence Energy Accumulation

A 3D Geometry Reconstruction Method for Space Targets Utiliz...

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IEEE international conference on Radar

作者： Zuobang Zhou Rongzhen Du Lei Liu Feng Zhou Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education Xidian University Xi'an China National Laboratory of Radar Signal Processing Xidian University Xi'an China

ISBN: (纸本)9781665468893

As for space targets with unknown motion, the accurate projection matrix cannot be obtained in advance, which makes the three-dimensional (3D) geometry reconstruction method based on inverse synthetic aperture radar (ISAR) image sequence energy accumulation (ISEA) inapplicable. To tackle this problem, a joint factorization and ISEA-based method is proposed. Firstly, the range-Doppler measurement matrix can be formed through the steadily tracking of a small number of feature points in the ISAR image sequence. Then the factorization method is utilized to estimate the projection matrix, which reflects the projection relationship between the 3D geometry and corresponding ISAR image sequence. Next, when projected to ISAR image sequence, feature points with larger ISEA values can be regarded as the true scattering points on the geometry of the target. Therefore, all the scattering points can be reconstructed by searching the feature points with maximum ISEA values. Experimental results based on simulated point model verify the effectiveness of the proposed method.

关键词： Geometry Space vehicles Three-dimensional displays Radar measurements Scattering Reconstruction algorithms Radar imaging

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Research On Sliding Spotlight And TOPS SAR Variable PRF Imaging Technology

Research On Sliding Spotlight And TOPS SAR Variable PRF Imag...

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IEEE international conference on Radar

作者： Songtao Gong Zhi Wang Guang-cai Sun Yang Jun Mengdao Xing National Laboratory of Radar Signal Processing Xidian University Xi'an China College of Geomatics Xi'an University of Science and Technology Xi'an China

ISBN: (纸本)9781665468893

Because the traditional single-based spaceborne synthetic aperture radar (SAR) cannot receive signals while transmitting signals, there is a defect that high azimuth resolution and wide swath imaging cannot be achieved at the same time. In response to this problem, researchers have proposed the use of variable pulse repetition frequency (PRF) technology to achieve high-resolution wide swath imaging. However, the echo data of the variable PRF SAR will have the problem of sampling at different intervals in the azimuth direction. Using traditional frequency domain algorithms to process the data will cause serious deterioration of the imaging quality, or even imaging; the time domain algorithm can directly focus on the data of the variable PRF, but the amount of calculation is huge. In this paper, we deduced the change rule of PRF and proposed a variable PRF imaging algorithm based on time-frequency scale scaling. A new interpolation method is used to perform uniform sampling of the unambiguous signal in the time domain. Then use the Omega-K algorithm to achieve range cell migration correction (RCMC), and finally focuses the signal in the azimuth frequency domain. The proposed SAR working system with variable PRF is verified by simulation experiments.

关键词： Time-frequency analysis Interpolation image resolution Azimuth Spaceborne radar Imaging Radar imaging

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Autonomy in robotic prostate biopsy through AI-assisted fusion

Autonomy in robotic prostate biopsy through AI-assisted fusi...

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international conference on advanced Robotics (ICAR)

作者： Luigi Palladino Bogdan Maris Alessandro Antonelli Paolo Fiorini Altair Laboratory University of Verona Verona Italy Hospital Borgo Trento of Verona Verona Italy

ISBN: (纸本)9781665436854

Artificial intelligence (AI) and especially deep learning (DL) are applied in many fields and are the leading technologies towards robot autonomy. Proper DL algorithms and sufficient amount of medical data may be applied in medical robotics to increase autonomy, safety, accuracy and precision of a medical procedure. We investigate in this paper the implementation and the integration of DL techniques in a new robotic system for prostate biopsy-PROST. The DL algorithm, named PROST-Net, is a convolutional neural network (CNN) employed for the segmentation of the prostate in different types of medical images: pre-operative magnetic resonance (MRI) and intra-operative ultrasound (US). The US images come from different acquisition planes (axial and sagittal) with different alignments of the sensors (convex and linear) making the design of the CNN challenging. Tests on patient data produced an accuracy of 86% in US images and 77% in MRI and were estimated by using Dice similarity Coefficient (DC). The biopsy robot will use the output of PROST-Net for the initial fusion of pre-operative and intra-operative images to define the biopsy targets and the planning of the procedure. Real-time processing of the data with PROST-Net will empower dynamic update of the initial fusion by following the current position of the prostate with respect to the robotic reference frame.

关键词： image segmentation Ultrasonic imaging Magnetic resonance imaging Biopsy Transformers Real-time systems User experience

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GADO-Net: An Improved AOD-Net Single image Dehazing Algorithm

GADO-Net: An Improved AOD-Net Single Image Dehazing Algorith...

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Science and Technology Innovation (IAECST), international Academic Exchange conference on

作者： Shuaiyu Chen Jia Cheng Zeyu Huang College Of Software Henan University Kaifeng China College of Agronomy and Biotechnology China Agricultural University Beijing China College of Science China University of Petroleum (East China) Qingdao China

ISBN: (纸本)9781665402682

With the development of modern industry, the frequency of special weather such as haze has increased, and images taken on foggy days will have blurred picture details, reduced contrast, and partial loss of image information, making subsequent image processing difficult. In previous studies, many scholars have proposed many algorithms with excellent performance to solve such problems, and the more widely used one is AOD-Net. However, there are a series of problems such as easy overfitting of the joint estimation of parameters during training, too small sensory field, and poor defogging effect of low-illumination images. The proposed algorithm—GADO-Net uses a depth-separable convolutional neural network instead of a 5-layer convolutional neural network for joint estimation of atmospheric light values and transmittance, while adding a pyramidal pooling module, which makes the algorithm have the advantages of extending the perception field to a certain extent, effectively improving the overfitting phenomenon during training, and improving the ability of the algorithm to fetch global information of foggy sky images. Finally, in this algorithm, the Peak signal to Noise Ratio (PSNR) is significantly improved by using MS-SSIM and L1 weighting as the LOSS function and using optimization search to obtain the optimal parameters of the model. The experimental show that GAOD-Net performs better than the DCP (Dark Channel Prior), histogram equalization algorithm and conventional AOD-Net in terms of SSE and PSNR, and can dehazing foggy images more effectively.

关键词： Training Industries Technological innovation Histograms PSNR image processing Estimation

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AI Traffic Control System Based on Deepstream and IoT Using NVIDIA Jetson Nano

AI Traffic Control System Based on Deepstream and IoT Using ...

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international conference on Robotics,Electrical and signal processing Techniques (ICREST)

作者： Md. Imran Uddin Md. Shahriar Alamgir Md. Mahabubur Rahman Muhammed Shahnewaz Bhuiyan Md Asif Moral American International University-Bangaldesh (AIUB) Dhaka Bangladesh

ISBN: (数字)9781665415767

ISBN: (纸本)9781665415774

With the rapid growth of population and economic development, the number of vehicles is increasing day by day. Which leads to traffic congestion. In certain time slots of the day, it becomes unbearable. So, it has become very difficult to control the traffic and maintain traffic law and order traditionally. Thus, it creates a demand for an improved and optimized traffic management system. Fixed timers or manual control are being used in conventional ways of traffic control, which has been proven ineffective to mitigate this situation. In this paper, a completely new system has been introduced to solve the problem of traffic congestion. Which will monitor vehicle traffic in real-time and the system is smart enough to detect traffic conditions and provide decisions depending on the situation. Besides that, the system is designed in a such way, that can also detect the number plate of those vehicles which violate the traffic rules. advanced features have been added to inform law enforcement agencies about that number plate and send a confirmation message to the driver mentioning the amount of penalty. Throughout the paper, attempts have been made to represent this system's advancement and efficiency over the existing manual traffic control system.

关键词： image processing Sociology Traffic control signal processing Statistics Traffic congestion Vehicles

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Neural Network-Based Filtered Back-Projection Reconstruction and Limited-View Artifacts Correction 20

Neural Network-Based Filtered Back-Projection Reconstruction...

引用

Proceedings of the 4th international conference on Advances in image processing

作者： Maoyu Tian Bingzhen Lei Junhai Wen Beijing Institute of Technology China

ISBN: (纸本)9781450388368

Computed tomography is an advanced imaging technology. It determines the attenuation coefficient distribution of the measured object based on known projection data and reconstruction algorithms. The filtered back-projection algorithm is one of the traditional algorithms commonly used in image reconstruction. It can quickly obtain the image to be reconstructed. The quality of the reconstructed image relies on the selection of the filter designed manually. Besides, the image reconstructed will contain serious artifacts when the projection data is incomplete. In this paper, a fully connected layer is used to simulate the filtered back-projection algorithm by designing a neural network model, it can adaptively learn a suitable filter for the current projection system. Meanwhile, the artifacts generated in the case of limited-angle reconstruction are corrected through the convolution layer. The results indicate that the method proposed in this paper can obtain the image with better quality compared to the traditional filtered back-projection algorithm. Moreover, it is easy to obtain the training set, with only the point source for training, and the network can learn the reconstruction process of the image with any pixel distribution.

关键词： filtered back-projection limited-view reconstruction image reconstruction neural network

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Another Look at the Connection between CAN signal Ringing & In-Vehicle ECU Identification

Another Look at the Connection between CAN Signal Ringing & ...

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IEEE international conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Yucheng Liu Hao Wu Xiangxue Li School of Software Engineering East China Normal University Shanghai China CNCERT/CC Beijing China Shanghai Key Laboratory of Trustworthy Computing Shanghai China Westone Cryptologic Research Center Beijing China

ISBN: (纸本)9781665416597

We revisit the problem of Electronic Control Unit (ECU) identification from signal characteristics in the physical layer of in-vehicle Controller Area Network (CAN). Ringing is oscillation of a signal in signal processing and prior work has already noticed the ringing force in identifying ECUs via differ-ential CAN signaling. However, they either count on advanced do-main transformations or turn out to be fragile once CAN topology alters (e.g., the length of the stub lines, the number of connected ECUs, etc). The paper further dissects the connection between the ringing intensity and signal characteristics, based on which we can procure robust ECU identification scheme (and IDS, intrusion detection system) independent of CAN topology without any mathematical transformations. We first look into the factors enlarging signal ringing, and then abstract the characterization of ringing intensity where rising edges (from recessive to dominant states) and falling edges (from dominant to recessive states) make distinct contributions to ECU identification. The rising edges (along with dominant states) suffice to fingerprint the ECUs, whereas the falling edges pitch in shaky performance (correlated strongly to CAN topology). The unique contribution of the former can thereby be isolated to construct robust mechanisms for ECU identification and IDS. Robustness and accuracy of the resulting approach are validated on not only prototype implementations (with diverse portfolio in 3/6/9/13 ECUs and 1/2/3-meter stub lines) but real vehicles. Concrete comparisons among prior work and ours are also conducted and substantiate our argumentation and mechanisms.

关键词： Privacy Network topology image edge detection Prototypes Fingerprint recognition signal processing Robustness

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Unsupervised Domain Adaptation for Acoustic Scene Classification Using Band-Wise Statistics Matching

Unsupervised Domain Adaptation for Acoustic Scene Classifica...

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European signal processing conference (EUSIPCO)

作者： Alessandro Ilic Mezza Emanuël A. P. Habets Meinard Müller Augusto Sarti Informazione e Bioingegneria Politecnico di Milano Milan Italy International Audio Laboratories Erlangen⋆ Erlangen Germany

ISBN: (数字)9789082797053

ISBN: (纸本)9781728150017

The performance of machine learning algorithms is known to be negatively affected by possible mismatches between training (source) and test (target) data distributions. In fact, this problem emerges whenever an acoustic scene classification system which has been trained on data recorded by a given device is applied to samples acquired under different acoustic conditions or captured by mismatched recording devices. To address this issue, we propose an unsupervised domain adaptation method that consists of aligning the first- and second-order sample statistics of each frequency band of target-domain acoustic scenes to the ones of the source-domain training dataset. This approach is devised to adapt audio samples from unseen devices before they are fed to a pre-trained classifier, thus avoiding any further learning phase. Using the DCASE 2018 Task 1-B development dataset, we show that the proposed method outperforms the state-of-the-art unsupervised methods found in the literature in terms of both source- and target-domain classification accuracy.

关键词： Training Performance evaluation image analysis Machine learning algorithms signal processing Acoustics Task analysis

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Accelerating the Gabor Transform with a GPU for SAR image Compression 20

Accelerating the Gabor Transform with a GPU for SAR Image Co...

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2020 2nd international conference on Video, signal and image processing

作者： Conner McInnes Shadi Alawneh Oakland University United States Oakland University USA

ISBN: (纸本)9781450388931

The Gabor transform can be utilized in an algorithm for compression due to its ability to allow the user to isolate high frequency information to filter. This transform can be implemented using FFT's to aid in calculating the Gabor coefficients of a particular image. In the C++ programming language, an open source library exists called FFTW that is able to perform FFT's quickly on the CPU. We sought to prove statistically the hypothetical gain that cuFFT could have computationally over FFTW when performing an FFT. cuFFT does have a bottleneck during the initial allocation of the input, output, and plan for the desired FFT, but with larger images these became less and less impactful. image compression algorithms using the Gabor transform can benefit in reduced computational time from cuFFT's functions.

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