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

共 1582 条记录，以下是371-380 订阅

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Wavelength Independent image Classification Through A Multimode Fiber Using Deep Neural Networks

Wavelength Independent Image Classification Through A Multim...

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conference on Lasers and Electro-Optics Europe

作者： Eirini Kakkava Navid Borhani Babak Rahmani Ugur Tegin Christophe Moser Demetri Psaltis Optics Laboratory École Polytechnique Fédérale de Lausanne 1015 Lausanne Switzerland Laboratory of Applied Photonic Devices École Polytechnique Fédérale de Lausanne 1015 Lausanne Switzerland

ISBN: (纸本)9781728104690

Deep Neural Networks (DNNs) have been increasingly implemented in different research fields or industrial applications. Large amounts of data are processed daily in order to extract useful information using machine learning techniques. Many research groups have shown impressive results on improving resolution in microscopy and quantitative phase retrieval by training DNNs on real datasets. Recently, recovery and reconstruction of images after they have propagated through multimode optical fibers (MMFs) have also been achieved using DNNs. When images propagate through MMFs they suffer severe scrambling because the information gets distributed among the different spatial modes that the fiber supports. Furthermore, since the fiber modes propagate with different velocities, the local information of the input decorrelates after a few millimeters along the MMF, thus resulting in the formation of a speckle pattern at the output. Recovery of information from such speckle patterns is of practical interest for integrating the MMFs for endoscopic applications in medicine or for signal recovery in telecommunications.

关键词： image reconstruction Laser sources Multimode fibers Neural networks Phase retrieval Speckle patterns

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Comparison of 3D Surgical Tool Segmentation Procedures with Robot Kinematics Prior

Comparison of 3D Surgical Tool Segmentation Procedures with ...

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25th IEEE/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Su, Yun-Hsuan Huang, Issac Huang, Kevin Hannaford, Blake Univ Washington Dept Elect Engn Paul Allen Ctr 185 Stevens WayRoom AE100RCampus Box 352500 Seattle WA 98195 USA Trinity Coll Dept Engn 300 Summit St Hartford CT 06106 USA

ISBN: (纸本)9781538680940

3D reconstruction and surgical tool segmentation are necessary for several advanced tasks in robot-assisted laparoscopic surgery. These tasks include vision-based force estimation, surgical guidance, and medical image registration where pre-operative data (CT or MRI scan image slices) are overlaid on patient anatomy in real-time during surgery [1] to name a few. In this work, two main strategies were considered: (1) initialize with surgical tool segmentation from 2D images, then proceed to local 3D reconstruction near the tool-tissue interaction region by projecting the segmented result into 3D space, and (2) initialize with 3D reconstruction of the entire surgical task space, followed by surgical tool segmentation from within the 3D reconstructed model. Both methods were implemented on the Raven II surgical robot system, and accuracy and time complexity for both methods were comparatively analyzed while considering various task parameters. Finally, based on the results of this work, guidelines for selecting reconstruction and segmentation strategies and procedure for particular situations are outlined in Section v.

关键词： Tools Three-dimensional displays Cameras image segmentation Robot vision systems Force image reconstruction

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Human vision reconstruction using brain activity profiles 4

Human vision reconstruction using brain activity profiles

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4th IEEE International conference on Computing Communication and Automation, ICCCA 2018

作者： Rathi, K. Gomathi, v. Dept. Of. CSE National Engineering College Kovilpatti TamilNadu India

ISBN: (纸本)9781538669471

The visual cue understandings of the human frequently happen at the occipital lobe of the brain with a fantastic mapping process. Nowadays, the ability of reconstruction of visual cues from the human cerebrum activities using functional Magnetic Resonance Imaging (fMRI) is an active research field under neuropsychological research. This research would bring scope for those suffering on the autism spectrum disorder (ASD) or those affected by psychotic disorders. This capability is also called a theory of mind (ToM). Also, this ability has evolved a long time, especially in primates, because of selection pressure brought about by increasing social complexity. This speculation is called 'social brain hypothesis Few researchers have suggested mapping techniques that can reconstruct a digital image of what a person is picturing in their mind, by merely considering their electrical activity of the brain as fMRI voxel information. During the gathering of voxel information for experiments, it usually is required to display the participants the MNIST digit images, faces of many individuals, one-by-one, on the computer screen, in parallel to recording event of their brain activity. There are open research challenges to develop efficient reconstruction models to predict the potential space of mapping the human vision from voxel data. Primarily, this mapping algorithm identified the pattern of brain signals related to the mental image and reproduced it as a digital image. The primary goal is to propose a new stimuli recreation technique, in which the voxel data and corresponding pixel information will correlate with an in-depth learning approach based on latent-variable distributions. This reconstruction method mainly depends on the observed brain activity patterns in the form of physiological modalities fMRI or EEG. In this work, we will also study the scope for exploiting the spatiotemporal EEG data to decide the neural associates of visual scene portrayals and to recreate th

关键词： Recurrent neural networks

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Benchmarking Coding Standards for Digital Holography represented on the Object Plane 5

Benchmarking Coding Standards for Digital Holography represe...

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conference on Optics, Photonics, and Digital Technologies for Imaging Applications v

作者： Bernardo, Marco, v Pinheiro, Antonio M. G. Pereira, Manuela Univ Beira Interior IT Rua Marques Avila & Bolama P-6201001 Covilha Portugal

ISBN: (数字)9781510618855

ISBN: (纸本)9781510618855

Digital holography is a growing field that owes its success to the provided three-dimensional imaging representation. This is achieved by encoding the wave field transmitted or scattered by an object in the form of an interference pattern with a reference beam. While in conventional imaging systems it is usually impossible to recover the correct focused image from a defocused one, with digital holography the image can be numerically retrieved at any distance from the hologram. Digital holography also allows the reconstruction of multiple objects at different depths. The complex object field at the hologram plane can be separated on real and imaginary, or amplitude and phase components for further compression. It could be inferred that more inter-component redundancies exist in real and imaginary information than in the amplitude and phase information. Also, several compression schemes, like lossless compression, lossy compression, based on subsampling, quantization, and transformation, mainly using wavelets were considered. The benchmark of the main available image coding standard solutions like JPEG, JPEG 2000, and the intra coding modes available on MPEG-2, H264/AvC and HEvC video codecs were also analyzed for digital holographic data compression on the hologram plane. In the current work, the benchmark of the main available image coding standard solutions JPEG, JPEG-XT, JPEG 2000 and the intra mode of HEvC, are performed for digital holographic data represented on the object plane, instead of the hologram plane. This study considers Real-Imaginary and Amplitude-Phase representations. As expected Real, Imaginary and Amplitude information present very similar compression performance and are coded very efficiently with the different standards. However, the phase information requires much higher bitrates (3/4 bpp more) to reach similar quality levels. Moreover, the Amplitude information results in slightly larger bitrates for the same quality level than real or imagi

关键词： Holographic data holographic representation format holographic coding holographic reconstruction

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CRCOED: Collaborative Representation-Based Classification using Odd Even Decomposition for Hyperspectral Remote Sensing imagery 8

CRCOED: Collaborative Representation-Based Classification us...

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8th International conference on Advances in Computing & Communications (ICACC)

作者： Sharma, Monika Biswas, Mantosh Natl Inst Technol Kurukshetra Dept Comp Engn Kurukshetra 136119 Haryana India

In the modern era exploiting the texture data is of excessive concern for classification of hyperspectral imagery (HSI). However, it is very challenging and prolonged task to obtain appropriate training samples which represents most discriminative features. This research paper suggested an improved collaborative representation based classification by means of odd even decomposition theorem i.e. CRCOED. The proposed method employs that augment the training samples using odd even decomposition theorem. In addition, every sample is characterized by means of a linear combination of the training samples from the whole training set;after that reconstruction of the image will be performed with related involvement from each class. The main purpose of the suggested method is to produce the highest parity symmetrical demonstration of sample for accurate and robust classification. Lastly, the observed outcomes on numerous HSI data sets revealed that this technique is more effective in comparison to recent methods. (C) 2018 The Authors. Published by Elsevier B.v.

关键词： Hyperspectral image Classification Collaborative Representation Classification Sparse Representation Classification

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Perceptually Driven Conditional GAN for Fourier Ptychography

Perceptually Driven Conditional GAN for Fourier Ptychography

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Asilomar conference on Signals, Systems & Computers

作者： Abhinau Kumar v. Shashank Gupta Sumohana S. Channappayya Department of Electrical Engineering Indian Institute of Technology Hyderabad Hyderabad India

ISBN: (数字)9781728143002

ISBN: (纸本)9781728143019

Fourier Ptychography (FP) is a computational imaging technique which artificially increases the effective numerical aperture of an imaging system. In FP, the object is imaged using an array of Light Emitting Diodes (LEDs), each from a different illumination angle. A high resolution image is synthesized from this low resolution stack, typically using iterative phase retrieval algorithms. However, such algorithms are time consuming and fail when the overlap between the spectra of images is low, leading to high data requirements. At the crux of FP lies a phase retrieval problem. In this paper, we propose a Deep Learning (DL) algorithm to perform this synthesis under low spectral overlap between samples, and show a significant improvement in phase reconstruction over existing DL algorithms.

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NON-LOCAL PATCH-BASED REGULARIZATION FOR image RESTORATION 25

NON-LOCAL PATCH-BASED REGULARIZATION FOR IMAGE RESTORATION

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25th IEEE International conference on image Processing (ICIP)

作者： Unni, v. S. Chaudhury, Kunal N. Indian Inst Sci IISc Dept Elect Engn Bangalore Karnataka India

ISBN: (纸本)9781479970612

Several patch-based models have been proposed for image restoration in the literature. A common feature with these models is that patches are used for filtering or optimization, where the aggregation is often performed over a non-local (NL) neighborhood. We propose a NL patch-based regularizer, where patches are used (1) for computing weights between NL neighbors, and (2) for defining a Tv-type norm in patch space. A general form of the latter construction was originally proposed by Peyre et al. and later studied by other authors. In most of these proposals, both the weights and the image are treated as variables, which makes the model non-convex. In particular, the corresponding numerical solvers cannot guarantee local optimality. On the other hand, our regularizer is convex. Along with an l(2) data fidelity term, we apply the regularizer for denoising, deblurring and super-resolution, and develop an efficient ADMM solver for computing the global minimum. An interesting finding is that, while our model is weaker than the non-convex counterparts, the minimizer of the former is generally closer to the ground truth than the reconstruction from the latter. Moreover, we demonstrate that our regularizer can outperform existing regularization techniques for deblurring and super-resolution.

关键词： regularization patch ADMM deblurring super-resolution

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Cascaded Generative and Discriminative Learning for Microcalcification Detection in Breast Mammograms

Cascaded Generative and Discriminative Learning for Microcal...

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IEEE/CvF conference on Computer vision and Pattern Recognition

作者： Fandong Zhang Ling Luo Xinwei Sun Zhen Zhou Xiuli Li Yizhou Yu Yizhou Wang Center for Data Science Peking University Deepwise AI Lab

ISBN: (纸本)9781728132945

Accurate microcalcification (μC) detection is of great importance due to its high proportion in early breast cancers. Most of the previous μC detection methods belong to discriminative models, where classifiers are exploited to distinguish μCsfrom other backgrounds. However, it is still challenging for these methods to tell the μCs from amounts of normal tissues because they are too tiny (at most 14 pixels). Generative methods can precisely model the normal tissues and regard the abnormal ones as outliers, while they fail to further distinguish the μCs from other anomalies, i.e. vessel calcifications. In this paper, we propose a hybrid approach by taking advantages of both generative and discriminative models. Firstly, a generative model named Anomaly Separation Network (ASN) is used to generate candidate μCs. ASN contains two major components. A deep convolutional encoder-decoder network is built to learn the image reconstruction mapping and a t-test loss function is designed to separate the distributions of the reconstruction residuals of μCs from normal tissues. Secondly, a discriminative model is cascaded to tell the μCs from the false positives. Finally, to verify the effectiveness of our method, we conduct experiments on both public and in-house datasets, which demonstrates that our approach outperforms previous state-of-the-art methods.

关键词： Discrimination Learning Microcalcification cascades Breast Cancer Digital Mammography Classifiers outliers Tell Breast

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Spectral reconstruction from Dispersive Blur: A Novel Light Efficient Spectral imager

Spectral Reconstruction from Dispersive Blur: A Novel Light ...

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IEEE/CvF conference on Computer vision and Pattern Recognition

作者： Yuanyuan Zhao Xuemei Hu Hui Guo Zhan Ma Tao Yue Xun Cao Nanjing University

ISBN: (纸本)9781728132945

Developing high light efficiency imaging techniques to retrieve high dimensional optical signal is a long-term goal in computational photography. Multispectral imaging, which captures images of different wavelengths and boosting the abilities for revealing scene properties, has developed rapidly in the last few decades. from scanning method to snapshot imaging, the limit of light collection efficiency is kept being pushed which enables wider applications especially under the light-starved scenes. In this work, we propose a novel multispectral imaging technique, that could capture the multispectral images with a high light efficiency. Through investigating the dispersive blur caused by spectral dispersers and introducing the difference of blur (DoB) constraints, we propose a basic theory for capturing multispectral information from a single dispersive-blurred image and an additional spectrum of an arbitrary point in the scene. Based on the theory, we design a prototype system and develop an optimization algorithm to realize snapshot multispectral imaging. The effectiveness of the proposed method is verified on both the synthetic data and real captured images.

关键词： collection efficiency images multispectral imagery Snapshots dispersion Optimization algorithms scanning method Light synthetic data imaging instrument

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Frequency-Diverse Computational Polarimetic Imaging

Frequency-Diverse Computational Polarimetic Imaging

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European conference on Antennas and Propagation

作者： Okan Yurduseven Thomas fromenteze Rixi Peng David R. Smith School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast United Kingdom XLIM UMR 7252 Université de Limoges/CNRS Limoges France Department of Electrical and Computer Engineering Duke University Durham NC

In this paper, a frequency-diverse computational polarimetric imaging system is demonstrated at K-band (17.5-26.5 GHz) frequencies. The frequency-diverse operation enables all-electronic data-acquisition by means of a simple frequency-sweep and requires no mechanically moving apparatus or phase shifting circuits for imaging. Leveraging a tensor model for the scattering behavior, as opposed to a scalar model, enables the extraction of further information from the imaged objects. This is shown by means of frequency-diverse polarimetric imaging of complex objects, such as the letters of the word "DUKE", and comparing the reconstructed polarimetric images to the reflectivity-only reconstructions of the same objects.

关键词： Cavity resonators Frequency measurement Antenna measurements Microwave imaging Apertures image reconstruction

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