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IEEE workshop on Multimedia Signal processing

作者： Yingying Zhang Chao Ren Honggang Chen Ce Zhu University of Electronic Science and Technology of China Chengdu China Sichuan University Chengdu China

ISBN: (数字)9781728193205

ISBN: (纸本)9781728193236

Depth maps are widely used in 3D imaging techniques because of the appearance of the consumer depth cameras. However, the practical application of the depth map is limited by the poor image quality. In this paper, we propose a novel framework for the single depth map super-resolution via joint the local and non-local constraints simultaneously in the depth map. For the non-local constraint, we use the group-based sparse representation to explore the non-local self-similarity of the depth map. For the local constraint, we first estimate gradient images in different directions of the desired high-resolution (HR) depth map, and then build a multi-directional gradient guided regularizer using these estimated gradient images to describe depth gradients with different orientations. Finally, the two complementary regularizers are cast into a unified optimization framework to obtain the desired HR image. The experimental results show that the proposed method can achieve better depth super-resolution performance than state-of-the-art methods.

关键词： image quality Three-dimensional displays Conferences Signal processing Cameras Optimization

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Application of 2 x 5 MPX3 camera with monolithic sensor for phase contrast imaging and computed tomography

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JOURNAL OF INSTRUMENTATION 2022年第3期17卷 C03020页

作者： Vavrik, D. Dudak, J. Kumpova, I Zemlicka, J. Czech Acad Sci Inst Theoret & Appl Mech Prosecka 76 Prague 6 Czech Republic Czech Tech Univ Inst Expt & Appl Phys Husova 240-5 Prague 1 Czech Republic

This work focuses on the non-destructive identification of delamination in layered structures utilizing the X-ray phase contrast effect. This effect naturally occurs on sharp edges, material interfaces and fine textures, and can be retrieved from radiographic data even without any grating in special cases, as will be demonstrated for the layered carbon fibre reinforced plastic composites used in the aerospace industry. Such delamination can be described as the local disbonding of composite layers, which often occurs due to the cyclic loading of the structural elements while in service. Such a flaw may significantly decrease its bearing capacity, and therefore periodic non-destructive inspection is needed. Aside from ultrasound inspection, which provides limited resolution and detectability, X-ray imaging and tomography is widely used in practise. Nevertheless, the identification of delamination may be difficult utilizing X-ray computed tomography, as delaminated faces may remain in contact - the defect has negligible volume. On the other hand, the distance between faces is not relevant for X-ray phase contrast imaging. In addition, access to the structural elements is often limited for X-ray imaging. For shaped parts, it will be shown in this work that limited access is not as critical a problem in comparison with standard computed tomography.

关键词： Data processing methods Detection of defects image filtering Inspection with X-rays

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JOINT SPARSE RECOVERY OF MISALIGNED MULTIMODAL imageS VIA ADAPTIVE local AND nonlocal CROSS-MODAL REGULARIZATION 8

JOINT SPARSE RECOVERY OF MISALIGNED MULTIMODAL IMAGES VIA AD...

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8th IEEE international workshop on Computational Advances in Multi-Sensor Adaptive processing (CAMSAP)

作者： Eslahi, Nasser Foi, Alessandro Tampere Univ Tampere Finland

ISBN: (纸本)9781728155494

Given few noisy linear measurements of distinct misaligned modalities, we aim at recovering the underlying multimodal image using a sparsity promoting algorithm. Unlike previous multimodal sparse recovery approaches employing side information under the naive assumption of perfect calibration of modalities or of known deformation parameters, we adaptively estimate the deformation parameters from the images separately recovered from the incomplete measurements. We develop a multiscale dense registration method that proceeds alternately by finding block-wise intensity mapping models and a shift vector field which is used to obtain and refine the deformation parameters through a weighted least-squares approximation. The co-registered images are then jointly recovered in a plug-and-play framework where a collaborative filter leverages the local and nonlocal cross-modal correlations inherent to the multimodal image. Our experiments with this fully automatic registration and joint recovery pipeline show a better detection and sharper recovery of fine details which could not be separately recovered.

关键词： Recovery

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Permutohedral Attention Module for Efficient non-local Neural Networks 22nd

Permutohedral Attention Module for Efficient Non-local Neura...

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10th international workshop on Machine Learning in Medical Imaging (MLMI) / 22nd international Conference on Medical image Computing and Computer-Assisted Intervention (MICCAI)

作者： Joutard, Samuel Dorent, Reuben Isaac, Amanda Ourselin, Sebastien Vercauteren, Tom Modat, Marc Kings Coll London Sch Biomed Engn & Imaging Sci London England

ISBN: (纸本)9783030322267;9783030322250

Medical image processing tasks such as segmentation often require capturing non-local information. As organs, bones, and tissues share common characteristics such as intensity, shape, and texture, the contextual information plays a critical role in correctly labeling them. Segmentation and labeling is now typically done with convolutional neural networks (CNNs) but the context of the CNN is limited by the receptive field which itself is limited by memory requirements and other properties. In this paper, we propose a new attention module, that we call Permutohedral Attention Module (PAM), to efficiently capture non-local characteristics of the image. The proposed method is both memory and computationally efficient. We provide a GPU implementation of this module suitable for 3D medical imaging problems. We demonstrate the efficiency and scalability of our module with the challenging task of vertebrae segmentation and labeling where context plays a crucial role because of the very similar appearance of different vertebrae.

关键词： non-local neural networks Attention module Permutohedral Lattice Vertebrae segmentation

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A global-local embedding module for fashion landmark detection 17

A global-local embedding module for fashion landmark detecti...

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17th IEEE/CVF international Conference on Computer Vision workshop, ICCVW 2019

作者： Lee, Sumin Oh, Sungchan Jung, Chanho Kim, Changick Korea Republic of Electronics and Telecommunications Research Institute Hanbat National University Korea Republic of

ISBN: (纸本)9781728150239

Detecting fashion landmarks is a fundamental technique for visual clothing analysis. Due to the large variation and non-rigid deformation of clothes, localizing fashion landmarks suffers from large spatial variances across poses, scales, and styles. Therefore, understanding contextual knowledge of clothes is required for accurate landmark detection. To that end, in this paper, we propose a fashion landmark detection network with a global-local embedding module. The global-local embedding module is based on a non-local operation for capturing long-range dependencies and a subsequent convolution operation for adopting local neighborhood relations. With this processing, the network can consider both global and local contextual knowledge for a clothing image. We demonstrate that our proposed method has an excellent ability to learn advanced deep feature representations for fashion landmark detection. Experimental results on two benchmark datasets show that the proposed network outperforms the state-of-the-art methods. © 2019 IEEE.

关键词： Embeddings

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Towards image processing Based on System of Difference Equations 10

Towards Image Processing Based on System of Difference Equat...

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10th IEEE international Conference on Intelligent Data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

作者： Cheredarchuk, Andrii Kashpirovskyi, Oleksii Kriukova, Galyna Sarana, Maksym Natl Univ Kyiv Mohyla Acad 2 Skovoroda St UA-04070 Kiev Ukraine

ISBN: (纸本)9781728140681

In the last few years image processing and analysis, particularly, video and image compression became very important due to immense development of new multimedia products, surveillance systems, automotive industry, etc, and the shortage of bandwidth and storage space of the memory. In the paper we consider a mathematical model as an approach to image processing, compression and restoration. We use edge detection algorithm to construct infinite system of difference equations, which is encoded by means of given family of functions, i.e. low degree polynomials, piece-wise linear functions or Heaviside step functions. Corresponding encoding and decoding algorithm is proposed, along with comprehensive theoretical background. The existence and uniqueness of decoded image is considered. This paper presents some preliminary study of the advantages and drawbacks of the approach and indicates areas of possible further research.

关键词： infinite system of difference equations local approximation image compression

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Enhanced Feature Selection for Facial Expression Recognition Systems with Genetic Algorithms 20th

Enhanced Feature Selection for Facial Expression Recognition...

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20th international Conference on Engineering Applications of Neural Networks (EANN) /1st international workshop on Pervasive Intelligence (PEINT)

作者： Chengeta, Kennedy Univ KwaZulu Natal Dept Math Stat & Comp Sci Westville Campus Durban South Africa

ISBN: (纸本)9783030202576;9783030202569

Humans use voice and facial expressions to infer their state of emotions. Key expressions include being happy, angry, sad and neutral. The expressions accounts for a third of non-verbal communication. The study presents an efficient method to identify facial expressions in images based on artificial neural networks enhanced by genetic algorithms. We use Viola Jones for facial detections and PCA, a statistical method to reduce the dimensionality and extract the features with a local variant of local binary patterns called CS-LBP for static images which is a local algorithm that reduces feature set by comparing symmetrical pixels halving the feature set. The features are then optimally selected using genetic algorithm before classification using artificial neural networks. It is also crucial to note that with these emotions being natural reactions, recognition of feature selection and edge detection from the images can increase accuracy and reduce the error rate. This can be achieved by removing unimportant information from the facial images. The genetic algorithm (GA) chooses a subset of image features based on a reduced-dimensional dataset. The study proposes local binary pattern variant central symmetric local directional pattern (CS-LDP), central symmetric LBP (CS-LBP) and artificial neural networks aided by genetic algorithms for feature selection. The study used the Japanese Female Facial Expression, JAFFE database. The approach outperformed other traditional approaches and proved that with added feature selection and optimization the processing time is reduced and accuracy improved.

关键词： local Directional and Binary patterns Genetic algorithms

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1st international SIPAIM workshop on processing and Analysis of Biomedical Information, SaMBa 2018 held in Conjunction with Medical image Computing and Computer-Assisted Intervention, MICCAI 2018

1st International SIPAIM Workshop on Processing and Analysis...

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1st international SIPAIM workshop on processing and Analysis of Biomedical Information, SaMBa 2018 held in Conjunction with Medical image Computing and Computer-Assisted Intervention, MICCAI 2018

ISBN: (纸本)9783030138349

The proceedings contain 19 papers. The special focus in this conference is on processing and Analysis of Biomedical Information. The topics include: Common carotid artery lumen automatic segmentation from cine fast spin echo magnetic resonance imaging;using deep learning to classify burnt body parts images for better burns diagnosis;mixed-model noise removal in 3d via rotation-and-scale invariant non-local means;autism spectrum disorders (Asd) characterization in children by decomposing mri brain regions with zernike moments;mp-idb: The malaria parasite image database for image processing and analysis;an algorithm for individual intermediate filament tracking;an automatic segmentation of gland nuclei in gastric cancer based on local and contextual information;a transfer learning exploited for indexing protein structures from 3d point clouds;proposal of a smart hospital based on internet of things (Iot) concept;a web-based telepathology framework for collaborative work of pathologists to support teaching and research in latin america;proposal of methodology of a bipedal humanoid gait generation based on cognitive algorithm;ocular control characterization of motor disabilities: The cerebral palsy case.

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Analysis of Approximate Message Passing With non-Separable Denoisers and Markov Random Field Priors

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IEEE TRANSACTIONS ON INFORMATION THEORY 2019年第11期65卷 7367-7389页

作者： Ma, Yanting Rush, Cynthia Baron, Dror North Carolina State Univ Raleigh NC 27695 USA Mitsubishi Elect Res Labs Cambridge MA 02138 USA Columbia Univ Dept Stat New York NY 10027 USA North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA

Approximate message passing (AMP) is a class of low-complexity, scalable algorithms for solving high-dimensional linear regression tasks where one wishes to recover an unknown signal from noisy, linear measurements. AMP is an iterative algorithm that performs estimation by updating an estimate of the unknown signal at each iteration and the performance of AMP (quantified, for example, by the mean squared error of its estimates) depends on the choice of a "denoiser" function that is used to produce these signal estimates at each iteration. An attractive feature of AMP is that its performance can be tracked by a scalar recursion referred to as state evolution. Previous theoretical analysis of the accuracy of the state evolution predictions has been limited to the use of only separable denoisers or block-separable denoisers, a class of denoisers that underperform when sophisticated dependencies exist between signal entries. Since signals with entrywise dependencies are common in image/video-processing applications, in this work we study the high-dimensional linear regression task when the dependence structure of the input signal is modeled by a Markov random field prior distribution. We provide a rigorous analysis of the performance of AMP, demonstrating the accuracy of the state evolution predictions, when a class of non-separable sliding-window denoisers is applied. Moreover, we provide numerical examples where AMP with sliding-window denoisers can successfully capture local dependencies in images.

关键词： approximation algorithms Task analysis Approximate message passing non-separable denoiser Markov random field finite sample analysis

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Compression and Completion of Animated Point Clouds using Topological Properties of the Manifold

Compression and Completion of Animated Point Clouds using To...

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international Conference on 3D Imaging, Modeling, processing, Visualization and Transmission (3DIMPVT)

作者： Linfei Pan Ľubor Ladický Marc Pollefeys ETH Zurich ETH Zurich/Microsoft

ISBN: (数字)9781728181288

ISBN: (纸本)9781728181295

Recent progress in consumer hardware allowed for the collection of a large amount of animated point cloud data, which is on the one hand highly redundant and on the other hand incomplete. Our goal is to bridge this gap and find a low dimensional representation capable of approximation to a desired precision and completion of missing data. Model-less non-rigid 3D reconstruction algorithms, formulated as a linear factorization of observed point tracks into static shape component and dynamic pose, have been found insufficient to create suitable generative models, capable of generating new unobserved poses. This is due to the non-locality of the linear models, over-fitting to the non-causal correlations present in the data, which manifests in the reconstruction containing rigidly behaving not directly connected parts. In this paper, we propose a new method that can distinguish body parts and factorize the data into shape and pose purely using topological properties of the manifold-local deformations and neighborhoods. To obtain localized factorization, we formulate the deformation distance between two point tracks as the smallest deformation along the path between them. After embedding such distance in low dimensional space, a clustering of embedded data leads to close to rigid components, suitable as initialization for fitting a model-a skinned rigged mesh, used extensively in computer graphics. As both local deformations and neighborhoods of a point are local and can be estimated only from the part of the animation, the method can be used to recover unobserved data in each frame.

关键词： Strain Three-dimensional displays Data models Computational modeling Shape Animation image reconstruction

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