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Infrared and ISAR image Fusion Based on Quadtree Decomposition and Bézier Interpolation

Infrared and ISAR Image Fusion Based on Quadtree Decompositi...

引用

IEEE international conference on signal and image processing (ICSIP)

作者： Chunlei Han Di Yang Yao Lu Kaifa Hou Wenbo An Hongmei Wang Xi'an Research Institute of Navigation Technology Xi'an China School of Astronautics Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781665446006

Inverse Synthetic Aperture Radar (ISAR) image obtained by stationary radar is the result of longitudinal and lateral two-dimensional high-resolution imaging of moving target. Infrared image is a thermal image corresponding to the temperature distribution of the scene, which formed by collecting infrared radiation emitted from the target and the surrounding. In order to make full use of the complementary information of infrared image and ISAR image, an algorithm that combines infrared image with ISAR image is researched. Firstly, this paper applies quadtree decomposition and Bézier interpolation to reconstruct the background of the ISAR image. Secondly, the salient features of the ISAR image are extracted by subtracting the reconstructed background from the original ISAR image. Finally, this algorithm fuses the salient features with the infrared image using the fusion rule proposed in this paper, through which to get the final fused image. Experimental results show that the algorithm can better preserve both the clear target and detailed information of infrared image and ISAR image in subjective evaluation. Compared with other fusion algorithms, experiments also verify the superiority of the algorithm in objective evaluation.

关键词： Interpolation Temperature distribution Fuses Shape Surveillance Radar imaging Maintenance engineering

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Numerical DWI phantoms to optimize accuracy and precision of quantitative parametric maps for non-Gaussian diffusion

Numerical DWI phantoms to optimize accuracy and precision of...

引用

Medical Imaging conference - image processing

作者： Malyarenko, Dariya, I Pang, Yuxi Amouzandeh, Ghoncheh Chenevert, Thomas L. Univ Michigan Radiol 1500 E Med Ctr Dr Ann Arbor MI 48109 USA

ISBN: (纸本)9781510633940

Clinical applications of quantitative diffusion-weighted imaging (qDWI) require confidence intervals for derived diffusion parameters to aid differentiation of technical errors from tissue characteristics. This study outlines practical procedures to evaluate precision (uncertainty) and accuracy (bias) of parametric maps derived for non-Gaussian diffusion models using numerical DWI phantoms (digital reference objects (DROs)) generated for advanced qDWI clinical trial protocols. The generated DROs include simulated acquisition noise, DICOM scaling and clinically-relevant qDWI parameter ranges for perfusion-fraction intra-voxel incoherent motion, kurtosis and stretched exponential diffusion models. Evaluation of fit accuracy and precision is illustrated for unsupervised linear least-squares (LLS) versus nonlinear minimization (NLM) algorithms ignorant of noise. DRO application examples are shown for calibration of noise-induced uncertainty with a physical DWI phantom, retrospective model fidelity analysis for brain tumor data and prospective error mapping in renal tissue. Physical DWI phantom analysis confirms adequate DRO noise model and realistic predicted fit errors. Detection of step-like bias activation in fit parameter space, consistent with model-dependent signal truncation at the noise floor, is proposed based on differences in NLM versus LLS fit maps. For all diffusion models, noise-induced bias in diffusivity is anti-correlated to bias in the model-specific (non-diffusivity) parameter. For confident assessment of fit parameter precision, bias minimization through b-range constraints and b-dependent averaging is advised prior to fit uncertainty mapping. For low bias, higher precision is achieved by LLS versus NLM, and for diffusivity versus model parameter. The described procedures allow efficient qDWI protocol optimization toward reduced acquisition and model-dependent errors.

关键词： digital reference object diffusion weighted imaging (DWI) non-Gaussian diffusion two-parameter diffusion Rician noise digital image communication in medicine (DICOM)

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A Meta-Learning Framework for Few-Shot Classification of Remote Sensing Scene

A Meta-Learning Framework for Few-Shot Classification of Rem...

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IEEE international conference on Acoustics, Speech and signal processing

作者： Pei Zhang Yunpeng Bai Dong Wang Bendu Bai Ying Li School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical University Xi’an China School of Communication and Information Engineering Xi’an University of Posts and Telecommunications Shaanxi Xi’an China

While achieving remarkable success in remote sensing (RS) scene classification for the past few years, convolutional neural network (CNN) based methods suffer from the demand for large amounts of training data. The bottleneck in prediction accuracy has shifted from data processing limits toward a lack of ground truth samples, usually collected manually by experienced experts. In this work, we provide a metalearning framework for few-shot classification of RS scene. Under the umbrella of meta-learning, we show it is possible to learn much information about a new category from only 1 or 5 samples. The proposed method is based on Prototypical Networks with a pre-trained stage and a learnable similarity metric. The experimental results show that our method outperforms three state-of-the-art few-shot algorithms and one typical CNN-based method, D-CNN, on two challenging datasets: NWPU-RESISC45 and RSD46-WHU.

关键词： Measurement image analysis conferences Training data signal processing algorithms signal processing Data processing

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DICE: Dynamically Induced Cross Entropy for Robust Learning with Noisy Labels 6th

DICE: Dynamically Induced Cross Entropy for Robust Learning ...

引用

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

作者： Liu, Tianyu State Key Lab on Intelligence Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9789811692468

image classification has lead to the revolution of artificial intelligence in the past five years. However, image classification algorithms are significantly affected by the inherent variance in sensory input and noise in the labelled data in a real-world situation. Also, the class count of items in the real world is significantly larger than that of the typical experiment setup. How to speedily train/fine-tune a large scale model in giant label space with considerable noise is a recent interest of the machine learning community. This paper proposed a multi-stage training algorithm to fine-tune a pre-trained EfficientNet model on AliProducts large scale product classification dataset, which has a large label space (50030 classes) and severe label noise. Our method can generalize well on such a dataset while keeping the prior knowledge gained in the large-scale pretraining stage. With our novel Dynamically Induced Cross-Entropy(DICE) network loss and several other methods to tackle unbalanced datasets and improve model convergence, the model achieved 76.67% of Top-1 Accuracy and 85.42% of Top-5 accuracy, which are 3.63%/2.9% higher than symmetric cross-entropy and are significantly higher than the usual fine-tuning method with categorical cross-entropy loss(CCE). © 2022, Springer Nature Singapore Pte Ltd.

关键词： image classification

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Gesture Recognition Based on YCbCr Color Space and Neural Network

Gesture Recognition Based on YCbCr Color Space and Neural Ne...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： HU Junping XIANG Siping Mechanical and Electrical Engineering Central South University Chang Sha Hunan China

ISBN: (纸本)9781665447300

Aiming at the problem of gesture recognition in complex background, a convolutional neural network gesture recognition algorithm based on improved YCbCr color space is proposed. Firstly, 8000 images of four gestures in the complex background are collected as the data set of this study. Then, the data set is preprocessed based on YCbCr color space, and the adaptive threshold method is used to improve it. Finally, a shallow convolutional neural network is built and trained with the preprocessed data set. The experimental results show that the gesture recognition accuracy of this method can reach 98.2% on the collected data set, which is higher than 85.7% and 90.6% using AlexNet and VGG-16.

关键词： Adaptive systems image color analysis Convolution Neural networks signal processing algorithms Gesture recognition Convolutional neural networks

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Improved Supervised Training of Physics-Guided Deep Learning image Reconstruction with Multi-Masking

Improved Supervised Training of Physics-Guided Deep Learning...

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IEEE international conference on Acoustics, Speech and signal processing

作者： Burhaneddin Yaman Seyed Amir Hossein Hosseini Steen Moeller Mehmet Akcakaya Electrical and Computer Engineering University of Minnesota Minneapolis MN USA Center for Magnetic Resonance Research University of Minnesota Minneapolis MN USA

ISBN: (纸本)9781728176055;9781728176062

Physics-guided deep learning (PG-DL) via algorithm unrolling has received significant interest for improved image reconstruction, including MRI applications. These methods unroll an iterative optimization algorithm into a series of regularizer and data consistency units. The unrolled networks are typically trained end-to-end using a supervised approach. Current supervised PG-DL approaches use all of the available sub-sampled measurements in their data consistency units. Thus, the network learns to fit the rest of the measurements. In this study, we propose to improve the performance and robustness of supervised training by utilizing randomness by retrospectively selecting only a subset of all the available measurements for data consistency units. The process is repeated multiple times using different random masks during training for further enhancement. Results on knee MRI show that the proposed multi-mask supervised PG-DL enhances reconstruction performance compared to conventional supervised PG-DL approaches.

关键词： Training Deep learning Magnetic resonance imaging Current measurement signal processing algorithms signal processing Robustness

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Pyramid U-Net for Retinal Vessel Segmentation

Pyramid U-Net for Retinal Vessel Segmentation

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IEEE international conference on Acoustics, Speech and signal processing

作者： Jiawei Zhang Yanchun Zhang Xiaowei Xu School of Computer Science Fudan University Shanghai China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China College of Engineering and Science Victoria University Melbourne Australia Guangdong Cardiovascular Institute Guangdong Provincial People’s Hospital Guangzhou China

ISBN: (纸本)9781728176055;9781728176062

Retinal blood vessel can assist doctors in diagnosis of eyerelated diseases such as diabetes and hypertension, and its segmentation is particularly important for automatic retinal image analysis. However, it is challenging to segment these vessels structures, especially the thin capillaries from the color retinal image due to low contrast and ambiguousness. In this paper, we propose pyramid U-Net for accurate retinal vessel segmentation. In pyramid U-Net, the proposed pyramid-scale aggregation blocks (PSABs) are employed in both the encoder and decoder to aggregate features at higher, current and lower levels. In this way, coarse-to-fine context information is shared and aggregated in each block thus to improve the location of capillaries. To further improve performance, two optimizations including pyramid inputs enhancement and deep pyramid supervision are applied to PSABs in the encoder and decoder, respectively. For PSABs in the encoder, scaled input images are added as extra inputs. While for PSABs in the decoder, scaled intermediate outputs are supervised by the scaled segmentation labels. Extensive evaluations show that our pyramid U-Net outperforms the current state-of-the-art methods on the public DRIVE and CHASE-DB1 datasets.

关键词： image segmentation image analysis image color analysis Aggregates Medical services signal processing Retinal vessels

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Performance Comparison of Different CS based Reconstruction Methods for WSN Application

Performance Comparison of Different CS based Reconstruction ...

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international conference on Applied Electromagnetics, signal processing and Communication (AESPC)

作者： D. Nayak K. B. Ray T. Kar School of Electronics KIIT Deemed to be University Bhubaneswar Odisha India

ISBN: (纸本)9781665443005

Compression of the Wireless Sensor Network (WSN) images are always challenging, as we need to maintain the quality of the reconstructed images with restricted resources and the bandwidth of transmission is also less. To meet these diverging requirements Compressive sensing is one of the best among all solutions. In this paper, we have compared the performance of two popular reconstruction methods for compressive sensing namely Orthogonal Matching Pursuits and L1-minimization found in the literature. The performances of these two methods are compared for standard dataset images as well as real wildlife CCTV captured color images. Result analysis shows that OMP method has faster processing with lower stability and L1 method has slower processing with higher stability.

关键词： Wireless sensor networks image coding Wildlife Matching pursuit algorithms Reconstruction algorithms Minimization Stability analysis

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Overcoming Measurement Inconsistency In Deep Learning For Linear Inverse Problems: Applications In Medical Imaging

Overcoming Measurement Inconsistency In Deep Learning For Li...

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IEEE international conference on Acoustics, Speech and signal processing

作者： Marija Vella Joao F. C. Mota Institute of Sensors Signals and Systems Heriot-Watt University Edinburgh UK

The remarkable performance of deep neural networks (DNNs) currently makes them the method of choice for solving linear inverse problems. They have been applied to super-resolve and restore images, as well as to reconstruct MR and CT images. In these applications, DNNs invert a forward operator by finding, via training data, a map between the measurements and the input images. It is then expected that the map is still valid for the test data. This framework, however, introduces measurement inconsistency during testing. We show that such inconsistency, which can be critical in domains like medical imaging or defense, is intimately related to the generalization error. We then propose a framework that post-processes the output of DNNs with an optimization algorithm that enforces measurement consistency. Experiments on MR images show that enforcing measurement consistency via our method can lead to large gains in reconstruction performance.

关键词： Inverse problems Neural networks signal processing algorithms Training data Speech processing image reconstruction signal resolution

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Application of CLIP on advanced GAN of Zero-Shot Learning

Application of CLIP on Advanced GAN of Zero-Shot Learning

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2021 international conference on signal processing and Machine Learning (CONF-SPML)

作者： Peize Li School of Control Engineering Northeastern University at Qinhuangdao

ISBN: (纸本)9781665417358

In recent years,deep learning models have achieved world-renowned achievements in the fields of image,speech and text ***,the insufficient amount of labeled data has brought serious problems,and it is also difficult to identify unseen classes ***,if we want to achieve perfect recognition of unseen classes,we need to perform zeroshot *** order to solve the zero-shot learning problem,a better solution can be obtained by using the semantic space ***-shot learning attempts to classify unseen data after learning the seen *** this case,it is one of the most difficult learning methods to achieve perfect *** uses a data set of 400 million data pairs,resulting in higher efficiency and better *** the features obtained by traditional RESNET neural network and CLIP,two advanced methods,F-CLSWGAN and TF-VAEGAN,were *** ZSL and GZSL experiments,excellent results have been achieved and the effectiveness of the combined method has been *** paper has tested the good effect of the application of CLIP on ZSL and *** experimental results show that CLIP has excellent performance on the AWA2 data set,whether it is using F-CLSWGAN or *** them,the effect of TF-VAEGAN is better.

关键词： zero-shot learning image classification feature synthesis class-level attributes

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