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检索条件"任意字段=Conference on Image Reconstruction from Incomplete Data V"

共 1583 条记录，以下是361-370 订阅

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A Method to Detect Subsurface Targets by Under-sampled Multifrequency data

A Method to Detect Subsurface Targets by Under-sampled Multi...

引用

European conference on Antennas and Propagation

作者： Adriana Brancaccio Giovanni Leone Raffaele Solimene Department of Engineering University of Campania ”Luigi Vanvitelli” Aversa Italy

Imaging buried objects embedded within electrically large investigation domains can require a large amount of measurement points. Thus, the need to reduce the number of spatial measurement points arises. Here, a method for detecting and localising shallowly buried scattering targets from under-sampled multifrequency far-zone data is proposed and checked by numerical examples.

关键词： Ground penetrating radar Sensors Scattering Remote sensing image reconstruction Frequency measurement

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DEEP NEURAL NETWORKS FOR NON-LINEAR MODEL-BASED ULTRASOUND reconstruction

DEEP NEURAL NETWORKS FOR NON-LINEAR MODEL-BASED ULTRASOUND R...

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IEEE Global conference on Signal and Information Processing (GlobalSIP)

作者： Almansouri, H. venkatakrishnan, S. v. Buzzard, G. T. Bouman, C. A. Santos-villalobos, H. Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA Oak Ridge Natl Lab Imaging Signals & Machine Learning Grp Oak Ridge TN 37831 USA Purdue Univ Dept Math W Lafayette IN 47907 USA

ISBN: (纸本)9781728112954

Ultrasound reflection tomography is widely used to image large complex specimens that are only accessible from a single side, such as well systems and nuclear power plant containment walls. Typical methods for inverting the measurement rely on delay-and-sum algorithms that rapidly produce reconstructions but with significant artifacts. Recently, model-based reconstruction approaches using a linear forward model have been shown to significantly improve image quality compared to the conventional approach. However, even these techniques result in artifacts for complex objects because of the inherent non-linearity of the ultrasound forward model. In this paper, we propose a non-iterative model-based reconstruction method for inverting measurements that are based on non-linear forward models for ultrasound imaging. Our approach involves obtaining an approximate estimate of the reconstruction using a simple linear back-projection and training a deep neural network to refine this to the actual reconstruction. We apply our method to simulated and experimental ultrasound data to demonstrate dramatic improvements in image quality compared to the delay-and-sum approach and the linear model-based reconstruction approach.

关键词： image reconstruction Ultrasonic imaging Mathematical model Training Neural networks Imaging Transducers

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Learning Non-volumetric Depth Fusion using Successive Reprojections

Learning Non-volumetric Depth Fusion using Successive Reproj...

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IEEE/CvF conference on Computer vision and Pattern Recognition

作者： Simon Donne Andreas Geiger Autonomous Vision Group MPI for Intelligent Systems and University of Tubingen

ISBN: (纸本)9781728132945

Given a set of input views, multi-view stereopsis techniques estimate depth maps to represent the 3D reconstruction of the scene;these are fused into a single, consistent, reconstruction - most often a point cloud. In this work we propose to learn an auto-regressive depth refinement directly from data. While deep learning has improved the accuracy and speed of depth estimation significantly, learned MvS techniques remain limited to the planesweeping paradigm. We refine a set of input depth maps by successively reproject-ing information from neighbouring views to leverage multi-view constraints. Compared to learning-based volumetric fusion techniques, an image-based representation allows significantly more detailed reconstructions;compared to traditional point-based techniques, our method learns noise suppression and surface completion in a data-driven fashion. Due to the limited availability of high-quality reconstruction datasets with ground truth, we introduce two novel synthetic datasets to (pre-)train our network. Our approach is able to improve both the output depth maps and the reconstructed point cloud, for both learned and traditional depth estimation front-ends, on both synthetic and real data.

关键词： STRUCTURE MAP three-dimensional displays Noise suppression ground truth Neighbor solidification Reconstructive Surgical Procedures SYNTHETICS Fusion reactions FUSION fusion (melting) Fusion, biological Machine Learning observational data

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Deep Reinforcement Learning of volume-guided Progressive view Inpainting for 3D Point Scene Completion from a Single Depth image

Deep Reinforcement Learning of Volume-guided Progressive Vie...

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IEEE/CvF conference on Computer vision and Pattern Recognition

作者： Xiaoguang Han Zhaoxuan Zhang Dong Du Mingdai Yang Jingming Yu Pan Pan Xin Yang Ligang Liu Zixiang Xiong Shuguang Cui CUHK (Shenzhen) DUT SRIBD Alibaba Group USTC TAMU

ISBN: (纸本)9781728132945

We present a deep reinforcement learning method of progressive view inpainting for 3D point scene completion under volume guidance, achieving high-quality scene reconstruction from only a single depth image with severe occlusion. Our approach is end-to-end, consisting of three modules: 3D scene volume reconstruction, 2D depth map inpainting, and multi-view selection for completion. Given a single depth image, our method first goes through the 3D volume branch to obtain a volumetric scene reconstruction as a guide to the next view inpainting step, which attempts to make up the missing information;the third step involves projecting the volume under the same view of the input, concatenating them to complete the current view depth, and integrating all depth into the point cloud. Since the occluded areas are unavailable, we resort to a deep Q-Network to glance around and pick the next best view for large hole completion progressively until a scene is adequately reconstructed while guaranteeing validity. All steps are learned jointly to achieve robust and consistent results. We perform qualitative and quantitative evaluations with extensive experiments on the SUNCG data, obtaining better results than the state of the art.

关键词： Completion learning (artificial intelligence) Learning view Quantitative Evaluation end-to-end image images imagery (Psychotherapy) Not Available

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Multi-level Encoder-Decoder Architectures for image Restoration

Multi-level Encoder-Decoder Architectures for Image Restorat...

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IEEE/CvF conference on Computer vision and Pattern Recognition Workshops

作者： Indra Deep Mastan Shanmuganathan Raman Indian Institute of Technology Gandhinagar Gandhinagar

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Many real-world solutions for image restoration are learning-free and based on handcrafted image priors such as self-similarity. Recently, deep-learning methods that use training data, have achieved state-of-the-art results in various image restoration tasks (e.g., super-resolution and in-painting). Ulyanov et al. bridge the gap between these two families of methods in [29]. They have shown that learning-free methods perform close to the state-of-the-art learning-based methods (≈ 1 PSNR). Their approach benefits from the encoder-decoder network (ed). In this paper, we propose a framework based on the multi-level extensions of the encoder-decoder network (med) to investigate interesting aspects of the relationship between image restoration and network construction independent of learning. Our framework allows various network structures by modifying the following network components: skip links, cascading of the network input into intermediate layers, a composition of the encoder-decoder subnetworks, and network depth. These handcrafted network structures illustrate how the construction of untrained networks influence the following image restoration tasks: de-noising, super-resolution, and inpainting. We also demonstrate image reconstruction using flash and no-flash image pairs. We provide performance comparisons with the state-of-the-art methods for all the restoration tasks above.

关键词： image restoration technique super-resolution NETWORK STRUCTURE Networking Frameworks Flashing Religious Missions image reconstruction

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Autoencoder Ensemble for Person Re-Identification

Autoencoder Ensemble for Person Re-Identification

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IEEE International conference on Multimedia Big data (BigMM)

作者： Kajal Kansal A.v. Subramanyam Department of Computer Science and Engineering Indraprastha Institute of Information Technology Delhi India

Person re-identification (re-id) aims to match people from non-overlapping multi-camera networks. Recently, with the advancement of deep learning techniques, the performance of re-id has been improved swiftly. However, most of the existing re-id methods need large number of samples for training due to which the models do not generalize well on smaller datasets and suffers from small sample size problem. Additionally, they focus on single scale appearance information while ignoring rich information that can be exploited from other scales. In this paper, we propose a simple yet effective autoencoder, comprising of an encoder and a sequential decoder. The goal of the network is two fold. First, the network learns features by introducing a generative task to the embedding layer so that it can make features more generalizable to the unknown test data to prevent from overfitting. Second, the encoder feature embedding is used as an input to decoder to reconstruct the input image with various scales to achieve robustness against scale variations. The effectiveness of our proposed method is validated on three public person re-identification datasets, Market-1501, DukeMTMC-reID and CUHK03.

关键词： Feature extraction Cameras image reconstruction Decoding image resolution Task analysis Robustness

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Can you tell a face from a HEvC bitstream? 1

Can you tell a face from a HEVC bitstream?

引用

1st IEEE conference on Multimedia Information Processing and Retrieval (MIPR)

作者： Alvar, Saeed Ranjbar Choi, Hyomin Bajic, Ivan v. Simon Fraser Univ Sch Engn Sci Burnaby BC Canada

ISBN: (纸本)9781538618578

image and video analytics are being increasingly used on a massive scale. Not only is the amount of data growing, but the complexity of the data processing pipelines is also increasing, thereby exacerbating the problem. It is becoming increasingly important to save computational resources wherever possible. We focus on one of the poster problems of visual analytics - face detection - and approach the issue of reducing the computation by asking: Is it possible to detect a face without full image reconstruction from the High Efficiency video Coding (HEvC) bitstream? We demonstrate that this is indeed possible, with accuracy comparable to conventional face detection, by training a Convolutional Neural Network on the output of the HEvC entropy decoder.

关键词： face detection HEvC deep learning convolutional neural network

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Douglas-Rachford Networks: Learning Both the image Prior and data Fidelity Terms for Blind image Deconvolution

Douglas-Rachford Networks: Learning Both the Image Prior and...

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IEEE/CvF conference on Computer vision and Pattern Recognition

作者： Raied Aljadaany Dipan K. Pal Marios Savvides Dept. of Electrical and Computer Engineering. Carnegie Mellon University

ISBN: (纸本)9781728132945

Blind deconvolution problems are heavily ill-posed where the specific blurring kernel is not known. Recovering these images typically requires estimates of the kernel. In this paper, we present a method called Dr-Net, which does not require any such estimate and is further able to invert the effects of the blurring in blind image recovery tasks. These image recovery problems typically have two terms, the data fidelity term (for faithful reconstruction) and the image prior (for realistic looking reconstructions). We use the Douglas-Rachford iterations to solve this problem since it is a more generally applicable optimization procedure than methods such as the proximal gradient descent algorithm. Two proximal operators originate from these iterations, one for the data fidelity term and the second for the image prior. It is non-trivial to design a hand-crafted function to represent these proximal operators which would work with real-world image distributions. We therefore approximate both these proximal operators using deep networks. This provides a sound motivation for the final architecture for Dr-Net which we find outperforms the state-of-the-art on two mainstream blind deconvolution benchmarks. While doing so, we also find that Dr-Net is one of the fastest algorithms according to wall-clock times.

关键词： images image imagery (Psychotherapy) BLURRING Fidelity iterative methods blind deconvolution Blind Seeds Optimization Procedure Operon

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Unsupervised Domain-Specific Deblurring via Disentangled Representations

Unsupervised Domain-Specific Deblurring via Disentangled Rep...

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IEEE/CvF conference on Computer vision and Pattern Recognition

作者： Boyu Lu Jun-Cheng Chen Rama Chellappa UMIACS University of Maryland

ISBN: (纸本)9781728132945

image deblurring aims to restore the latent sharp images from the corresponding blurred ones. In this paper, we present an unsupervised method for domain-specific, single-image deblurring based on disentangled representations. The disentanglement is achieved by splitting the content and blur features in a blurred image using content encoders and blur encoders. We enforce a KL divergence loss to regularize the distribution range of extracted blur attributes such that little content information is contained. Meanwhile, to handle the unpaired training data, a blurring branch and the cycle-consistency loss are added to guarantee that the content structures of the deblurred results match the original images. We also add an adversarial loss on de-blurred results to generate visually realistic images and a perceptual loss to further mitigate the artifacts. We perform extensive experiments on the tasks of face and text deblurring using both synthetic datasets and real images, and achieve improved results compared to recent state-of-the-art deblurring methods.

关键词： deblurring image reconstruction image blur original image ENCODER BLURRING Religious Missions representations real image

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Fingerprint image identification for crime detection 8

Fingerprint image identification for crime detection

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8th IEEE International conference on Communication and Signal Processing, ICCSP 2019

作者： Pavithra, R. Suresh, K.v. Siddaganga Institute of Technology Bengaluru India

ISBN: (纸本)9781538675953

Fingerprint images in crime scene are important clues to solve serial cases. In this paper we present a complete crime scene fingerprint identification system using deep machine learning with Convolutional Neural Network (CNN). images are acquired from crime scene using methods ranging from precision photography to complex physical and chemical processing techniques and saved as the database. The images collected from the crime scene are usually incomplete and hence difficult to categorize. Suitable enhancement methods are required for pre-processing the fingerprint images. Minutiae are extracted from the fingerprint images. The features of preprocessed data are fed into the CNN as input to train and test the network. The experimental results demonstrated on database using Open Cv-Python shows high accuracy of 80% recognition an partial or full fingerprints in the criminal database. © 2019 IEEE.

关键词： Python

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