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15th International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Qiao, Wenbao Creput, Jean-Charles Univ Technol Belfort Montbeliard IRTES SET Belfort France

ISBN: (纸本)9783319393841

As an extension of using image segmentation to do stereo matching, firstly, by using self-organizing map (som) and K-means algorithms, this paper provides a self-distributed segmentation method that allocates segments according to image's texture changement where in most cases depth discontinuities appear. Then, for stereo, under the fact that the segmentation of left image is not exactly same with the segmentation of right image, we provide a matching strategy that matches segments of left image to pixels of right image as well as taking advantage of border information from these segments. Also, to help detect occluded regions, an improved aggregation cost that considers neighbor valid segments and their matching characteristics is provided. For post processing, a gradient border based median filter that considers the closest adjacent valid disparity values instead of all pixels' disparity values within a rectangle window is provided. As we focus on real-time execution, these time-consumming works for segmentation and stereo matching are executed on a massively parallel cellular matrix GPU computing model. Finaly, we provide our visual dense disparity maps before post processing and final evaluation of sparse results after post-processing to allow comparison with several ranking methods top listed on Middlebury.

关键词： Stereo image segmentation SOM Self-distributed segments

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Denoising model for parallel magnetic resonance imaging images using higher-order Markov random fields

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IET image processing 2016年第12期10卷 962-970页

作者： Xu, Zhihuo Shi, Quan Nantong Univ Sch Transportat Image Res Grp Nantong 226001 Peoples R China

This study presents a novel robust method for Bayesian denoising of parallel magnetic resonance imaging (pMRI) images. For the first time, the authors' proposal applies fields of experts (FoE), a filter-based higher-order Markov random field (MRF), to model the prior of the pMRI image statistics. The noise in pMRI data behaves to be non-central Chi (nc-chi) distributed. In practice, correlation between coils exists, resulting in that nc-. distribution does not hold anymore and the spatially varying noise problem. Thus, preservation of fine textures requires to adapt locally the estimation. Therefore, more precisely, the noise is reduced by using a sliding window scheme. In each window, the likelihood probability function is accurately modelled from corrupted data by using an innovative Gaussian mixture model (GMM). The parameters of GMM are calculated by applying an iterative expectation maximisation approach. With the priors via the learned FoE model and the likelihood function via GMM, a maximum a posteriori (MAP) estimator is formulated. Then, the noise in the each window is filtered by applying an efficient nonlinear quasi-Newton method to explore an optimal solution for the MAP estimator. Finally, experiments have been conducted on both the simulated and real data to compare the proposed model with some state-of-the-art denoising methods. The experimental results demonstrate the robustness and effectiveness of the proposed denoising model.

关键词： Likelihood Functions corrupted data sliding windows Noise reduction Maximum a posteriori estimation Markov random fields optimal solution Gm(m)

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Learning non-local image diffusion for image denoising

arXiv

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arXiv 2017年

作者： Qiao, Peng Dou, Yong Feng, Wensen Chen, Yunjin National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha410073 China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing

image diffusion plays a fundamental role for the task of image denoising. Recently proposed trainable nonlinear reaction diffusion (TNRD) model defines a simple but very effective framework for image denoising. However, as the TNRD model is a local model, the diffusion behavior of which is purely controlled by information of local patches, it is prone to create artifacts in the homogenous regions and over-smooth highly textured regions, especially in the case of strong noise levels. Meanwhile, it is widely known that the non-local selfsimilarity (NSS) prior stands as an effective image prior for image denoising, which has been widely exploited in many nonlocal methods. In this work, we are highly motivated to embed the NSS prior into the TNRD model to tackle its weaknesses. In order to preserve the expected property that end-to-end training is available, we exploit the NSS prior by a set of non-local filters, and derive our proposed trainable non-local reaction diffusion (TNLRD) model for image denoising. Together with the local filters and influence functions, the non-local filters are learned by employing loss-specific training. The experimental results show that the trained TNLRD model produces visually plausible recovered images with more textures and less artifacts, compared to its local versions. Moreover, the trained TNLRD model can achieve strongly competitive performance to recent state-of-theart image denoising methods in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Copyright © 2017, The Authors. All rights reserved.

关键词： image denoising

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A Review of Indoor-Outdoor Scene Classification

A Review of Indoor-Outdoor Scene Classification

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2017 2nd International Conference on Control, Automation and Artificial Intelligence(CAAI2017)

作者： Zhehang Tong Dianxi Shi Bingzheng Yan Jing Wei National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology

ISBN: (纸本)9781510845541

Indoor-Outdoor scene classification problem have been proposed for almost 20 years and widely applied to general scene classification, image retrieval, image processing and robot application. But there is no consensus on one particular scene classification technique that can solve the Indoor-Outdoor scene classification problem perfectly. As larger image dataset has been developed and machine learning technology especially deep learning based methods achieve remarkable performance in computer vision, we aim to provide guidance and direction for researchers to tackle the Indoor-Outdoor scene classification problem with more powerful and robust solution through concluding the Indoor-Outdoor scene classification approaches which have been proposed in last 20 years. In this paper, we review the Indoor-Outdoor scene classification including feature extraction, classifier and related dataset. Their advantages and disadvantages are discussed. At last we conclude some challenging problems remain unsolved and propose some potential solutions.

关键词： indoor-outdoor scene classification computer vision

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parallelized deblocking filtering of HEVC decoders based on complexity estimation

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JOURNAL OF REAL-TIME image processing 2016年第2期12卷 369-382页

作者： Jo, Hyunho Park, Seanae Sim, Donggyu Kwangwoon Univ Dept Comp Engn 20 Gwangun Ro Seoul South Korea

This paper proposes a parallelization method for a deblocking filter in a high-efficiency video coding (HEVC) decoder based on complexity estimation. A deblocking filter of HEVC is generally considered to be appropriate for data-level parallelism (DLP) because there are no data-level dependencies among adjacent blocks in the horizontal and vertical filtering processes. However, an imbalanced workload can increase the idle time on some of the threads, and thus, the maximum parallel performance cannot be achieved with only DLP. To alleviate this problem, the proposed method estimates the computational complexity by utilizing the coding unit (CU) segment information and the on/off flag of the deblocking filter in advance. Then, the workload is distributed equally across all threads. The experimental results indicate that the proposed method can accelerate the decoding speed by a factor of 3.97, with six threads on top of the sequential deblocking filtering. In addition, the ratio of the maximum elapsed time to ideal elapsed time is reduced to approximately 21 %, as compared to conventional DLP-based parallel deblocking filtering methods that do not implement a complexity estimation method.

关键词： HEVC Deblocking filter parallel decoding Workload imbalance

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High Performance Implementation of 3D Convolutional Neural Networks on a GPU

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COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2017年第1期2017卷 1-8页

作者： Lan, Qiang Wang, Zelong Wen, Mei Zhang, Chunyuan Wang, Yijie Natl Univ Def Technol Coll Comp Changsha 410073 Hunan Peoples R China Natl Key Lab Parallel & Distributed Proc Changsha 410073 Hunan Peoples R China

Convolutional neural networks have proven to be highly successful in applications such as image classification, object tracking, and many other tasks based on 2D inputs. Recently, researchers have started to apply convolutional neural networks to video classification, which constitutes a 3D input and requires far larger amounts of memory and much more computation. FFT based methods can reduce the amount of computation, but this generally comes at the cost of an increased memory requirement. On the other hand, the Winograd Minimal Filtering Algorithm (WMFA) can reduce the number of operations required and thus can speed up the computation, without increasing the required memory. This strategy was shown to be successful for 2D neural networks. We implement the algorithm for 3D convolutional neural networks and apply it to a popular 3D convolutional neural network which is used to classify videos and compare it to cuDNN. For our highly optimized implementation of the algorithm, we observe a twofold speedup for most of the 3D convolution layers of our test network compared to the cuDNN version.

关键词： NEURAL networks (Computer science) GRAPHICS processing units (Computers) CONVOLUTION (Signal processing) HIGH performance computing FAST Fourier transforms

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Scalable splitting algorithms for big-data interferometric imaging in the SKA era

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MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 2016年第4期462卷 4314-4335页

作者： Onose, Alexandru Carrillo, Rafael E. Repetti, Audrey McEwen, Jason D. Thiran, Jean-Philippe Pesquet, Jean-Christophe Wiaux, Yves Heriot Watt Univ Inst Sensors Signals & Syst Edinburgh EH14 4AS Midlothian Scotland Ecole Polytech Fed Lausanne Signal Proc Lab LTS5 CH-1015 Lausanne Switzerland Univ Coll London Mullard Space Sci Lab Surrey RH5 6NT England Univ Paris Est Lab Informat Gaspard Monge F-77454 Marne La Vallee France

In the context of next-generation radio telescopes, like the Square Kilometre Array (SKA), the efficient processing of large-scale data sets is extremely important. Convex optimization tasks under the compressive sensing framework have recently emerged and provide both enhanced image reconstruction quality and scalability to increasingly larger data sets. We focus herein mainly on scalability and propose two new convex optimization algorithmic structures able to solve the convex optimization tasks arising in radio-interferometric imaging. They rely on proximal splitting and forward-backward iterations and can be seen, by analogy, with the CLEAN major-minor cycle, as running sophisticated CLEAN-like iterations in parallel in multiple data, prior, and image spaces. Both methods support any convex regularization function, in particular, the well-studied l(1) priors promoting image sparsity in an adequate domain. Tailored for big-data, they employ parallel and distributed computations to achieve scalability, in terms of memory and computational requirements. One of them also exploits randomization, over data blocks at each iteration, offering further flexibility. We present simulation results showing the feasibility of the proposed methods as well as their advantages compared to state-of-the-art algorithmic solvers. Our MATLAB code is available online on GitHub.

关键词： techniques: image processing techniques: interferometric

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distributed computation of linear inverse problems with application to computed tomography reconstruction

arXiv

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arXiv 2017年

作者： Gao, Yushan Blumensath, Thomas Faculty of Engineering and Environment University of Southampton SouthamptonSO17 1BJ United Kingdom

The inversion of linear systems is a fundamental step in many inverse problems. Computational challenges exist when trying to invert large linear systems, where limited computing resources mean that only part of the system can be kept in computer memory at any one time. We are here motivated by tomographic inversion problems that often lead to linear inverse problems. In state of the art x-ray systems, even a standard scan can produce 4 million individual measurements and the reconstruction of x-ray attenuation profiles typically requires the estimation of a million attenuation coefficients. To deal with the large data sets encountered in real applications and to utilise modern graphics processing unit (GPU) based computing architectures, combinations of iterative reconstruction algorithms and parallel computing schemes are increasingly applied. Although both row and column action methods have been proposed to utilise parallel computing architectures, individual computations in current methods need to know either the entire set of observations or the entire set of estimated xray absorptions, which can be prohibitive in many realistic big data applications. We present a fully parallelizable computed tomography (CT) image reconstruction algorithm that works with arbitrary partial subsets of the data and the reconstructed volume. We further develop a non-homogeneously randomised selection criteria which guarantees that sub-matrices of the system matrix are selected more frequently if they are dense, thus maximising information flow through the algorithm. A grouped version of the algorithm is also proposed to further improve convergence speed and performance. Algorithm performance is verified experimentally. Copyright © 2017, The Authors. All rights reserved.

关键词： image reconstruction

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An image-based approach to automatic crop organ extraction via low-rank matrix recovery 15

An image-based approach to automatic crop organ extraction v...

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15th International Symposium on parallel and distributed Computing (ISPDC)

作者： Yu, Zhenghong Yin, Haichang Feng, Haijie Chen, Minfang Zhou, Huabing Lu, Tongwei Min, Feng Guangdong Polytech Sci & Technol Coll Mech & Elect Engn Zhuhai Peoples R China Wuhan Inst Technol Hubei Prov Key Lab Intelligent Robot Wuhan Hubei Peoples R China

ISBN: (纸本)9781509041527

Automatic extraction of crop organ from images is a crucial step for quantitatively acquiring crop growth information in precision agriculture. There has been some attempt on this task, but the performance is not satisfactory. In this paper, we proposed an image-based method based on low-rank matrix recovery to extract organ accurately. In our method, a crop image is considered to be compose of two factors: background and organ. In a certain feature space, the image is represented as a low-rank matrix plus sparse noises. The organ is then extracted by identifying the sparse noises when using low-rank matrix recovery algorithm. In order to ensure the rank of background is low, a linear transform for the feature space is introduced and needs to be learned from historical data. Dynamic threshold segmentation followed by vegetation removing techniques are ultimately adopted in the final step. The experimental results on the benchmark farmland dataset show that our method achieve competitive performance, compared with the other well-established methods, yielding the highest performance of 93.9% with the lowest standard deviation of 2.86%, which means our method is more robust and not sensitive to the complex environmental elements and different cultivars.

关键词： Crop organ automatic extraction low-rank matrix recovery feature space transformation

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Automatic Detection and Classification of Tuberculosis Bacilli from Camera-enabled Smartphone Microscopic images 4

Automatic Detection and Classification of Tuberculosis Bacil...

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4th IEEE International Conference on parallel, distributed and Grid Computing (IEEE PDGC)

作者： Shah, Mi Mishra, S. Sarkar, Malay Sudarshan, S. K. Jaypee Univ Informat Technol Dept Biotechnol & Bioinformat Solon 173215 Himachal Prades India Indira Gandhi Med Coll Shimla 171001 Himachal Prades India

ISBN: (纸本)9781509036691

Sputum smear conventional microscopy (CM) is used as primary bacteriological test for detection of TB. This technique is the most preferred technique in low and middle income countries due to its availability as well as accessibility. Manual screening of bacilli using CM is time consuming and labor intensive. As a result, the sensitivity of TB detection is compromised leading to misdiagnosis 33-50% of active cases. Automated methods can increase the sensitivity and specificity of TB detection. Currently, the remote areas of TB-endemic developing countries have easy accessibility to portable and camera-enabled Smartphone microscope for capturing images from ZN-stained smear slide. In this paper, the performance of watershed segmentation method for detection and classification of bacilli from camera-enable Smartphone microscopic images is presented. Several preprocessing techniques have been implemented prior to watershed segmentation. Current method has achieved the sensitivity and specificity of 93.3% and 87% respectively for classifying an image as TB positive or negative.

关键词： TB diagnosis Bacilli detection Watershed segmentation ZN-stainin Mobile Microscopy image processing

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