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28th International conference on Artificial Neural Networks (ICANN)

作者： Yang, Lulu Lan, Long Zhang, Xiang Huang, Xuhui Luo, Zhigang NUDT Sci & Technol Parallel & Distributed Lab Changsha 410073 Hunan Peoples R China NUDT Coll Comp Changsha 410073 Hunan Peoples R China NUDT State Key Lab High Performance Comp Changsha 410073 Hunan Peoples R China NUDT Dept Comp Sci Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9783030305086;9783030305079

Semantic segmentation is a pixel-level image dense labeling task and plays a core role in autonomous driving. In this regard, how to balance between precision and speed is a frequently-studied issue. In this paper, we propose an alternative attentional residual dense factorized network (AttRDFNet) to address this issue. Specifically, we design a residual dense factorized convolution block (RDFB), which reaps the benefits of low-level and high-level layer-wise features through dense connection to boost segmentation precision whilst enjoying efficient computation by factorizing large convolution kernel into the product of two smaller kernels. This reduces computational burdens and makes real time become possible. To further leverage layer-wise features, we explore the graininess-aware channel and spatial attention modules to model different levels of salient features of interest. As a result, AttRDFNet can run with the inputs of the resolution 512 x 1024 at the speed of 55.6 frames per second on a single Titan X GPU with solid 68.5% Mean IOU on the test set of Cityscapes. Experiments on the Cityscapes dataset show that AttRDFNet has real-time inference whilst achieving competitive precision against well-behaved counterparts.

关键词： Real-time semantic segmentation Attention module Residual dense factorized convolution block

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Extraction of Features from Breast Cancer Mammogram image Using Some image processing Techniques 5

Extraction of Features from Breast Cancer Mammogram Image Us...

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5th IEEE International conference on parallel, distributed and Grid Computing (PDGC)

作者： Pati, Dakshya Prasad Panda, Sucheta Veer Surendra Sai Univ Technol Dept Comp Applicat Burla 768018 Odisha India

ISBN: (纸本)9781728106465

Breast cancer is a major root cause of death in most cancer affected women and every year millions of women are dying around the globe because of this deadly disease.. Now-a-days various image processing techniques are used to extract the features from mammogram image. Mammograms are X-ray images of the breast which are used to visualize the early sign of breast cancer. Oncologists are using various images processing technique for effective decisions making, by comparing the mammographic images. Microcalcifications are small calcium deposits that look like white specks on mammogram. These are usually not a result of cancer but if they appear in certain pattern and cluster together, can lead to early stage of cancer. Different image processing technique can be used for detecting these micro calcification. The aim of the paper is to detect the cancerous cells by the use of different existing image filter with their comparisons of output performance and the advantages of this method is future predictions of breast cancer. If the cancer cells are detected at early stage, many precious lives can be saved.

关键词： Breast Cancer image processing Mammograms image Filter

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4th International conference on Human Centered Computing, HCC 2018

4th International Conference on Human Centered Computing, HC...

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4th International conference on Human Centered Computing, HCC 2018

ISBN: (纸本)9783030151263

The proceedings contain 68 papers. The special focus in this conference is on Human Centered Computing. The topics include: Wi-Fi Attention Network for Indoor Fingerprint Positioning;Reinforcement Learning Based Cooperation Transmission Policy for HetNets with CoMP Technology;capacity Estimation of Time-Triggered Ethernet Network Based on Complex Network Theory;a New Communication P System Model Based on Hypergraph;Non-Orthogonal Multiple Access (NOMA) in Providing Services for High-Speed Railway and Local Users in DownLink MIMO System;research on Multi-agent distributed Supply Chain Information Collaboration Based on Cloud Environment;mobile Internet Mobile Agent System Dynamic Trust Model for Cloud Computing;Single image Super-Resolution by parallel CNN with Skip Connections and ResNet;incorporating Description Embeddings into Medical Knowledge Graphs Representation Learning;Research on the Classification and Channel Selection of Emotional EEG;A Piezoelectric MEMS Harvester Suitable Adopt a New Two-Degree-of-Freedom Structure;spiking Neural P Systems with Time Delay;energy Efficiency MapReduce Job Scheduling of Shuffle and Reduce Phases in Data Center;research on Weibo Emotion Classification Based on Context;research on Data Visualization in Different Scenarios;model Checking for Turn-Based Probability Epistemic Game Structure;depressive Emotion Recognition Based on Behavioral Data;knowledge Graph Embedding by Translation Model on Subgraph;linguistic Signatures of Impulsive Buying Consumer Based on Microblog;Research on the Model of Anomaly Detection of FMCG Based on Time Series: Illustrated by the Case of Cosmetics;research on Real-Time Low Air image Intelligence image Acquisition and processing methods;research on Font Emotion Based on Semantic Difference Method;simulation Research on Aircraft Anti-collision Algorithm.

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Adaptable image segmentation via simple pixel classification

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COMPUTATIONAL INTELLIGENCE 2018年第2期34卷 734-762页

作者： Kharma, Nawwaf Mazhurin, Anton Saigol, Kamil Sabahi, Farzad Concordia Univ Dept Elect & Comp Engn 1455 Blvd Maisonneuve O Montreal PQ H3G 1M8 Canada Rubikloud Corp Toronto ON Canada Georgia Inst Technol Inst Robot & Intelligent Machines Atlanta GA 30332 USA

We propose an approach to image segmentation that views it as one of pixel classification using simple features defined over the local neighborhood. We use a support vector machine for pixel classification, making the approach automatically adaptable to a large number of image segmentation applications. Since our approach utilizes only local information for classification, both training and application of the image segmentor can be done on a distributed computing platform. This makes our approach scalable to larger images than the ones tested. This article describes the methodology in detail and tests it efficacy against 5 other comparable segmentation methods on 2 well-known image segmentation databases. Hence, we present the results together with the analysis that support the following conclusions: (i) the approach is as effective, and often better than its studied competitors;(ii) the approach suffers from very little overfitting and hence generalizes well to unseen images;(iii) the trained image segmentation program can be run on a distributed computing environment, resulting in linear scalability characteristics. The overall message of this paper is that using a strong classifier with simple pixel-centered features gives as good or better segmentation results than some sophisticated competitors and does so in a computationally scalable fashion.

关键词： image segmentation machine learning parallel processing pixel classification

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Evaluation of the Suitability of Intel Xeon Phi Clusters for the Simulation of Ultrasound Wave Propagation Using Pseudospectral methods 19th

Evaluation of the Suitability of Intel Xeon Phi Clusters for...

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19th Annual International conference on Computational Science (ICCS)

作者： Vaverka, Filip Treeby, Bradley E. Jaros, Jiri Brno Univ Technol Ctr Excellence IT4Innovat Fac Informat Technol Bozetechova 2 Brno 61200 Czech Republic UCL Biomed Ultrasound Grp Med Phys & Biomed Engn Malet Pl Engn Bldg London WC1E 6BT England

ISBN: (纸本)9783030227449;9783030227432

The ability to perform large-scale ultrasound simulations using Fourier pseudospectral methods has generated significant interest in medical ultrasonics, including for treatment planning in therapeutic ultrasound and image reconstruction in photoacoustic tomography. However, the routine execution of such simulations is computationally very challenging. Nowadays, the trend in parallel computing is towards the use of accelerated clusters where computationally intensive parts are offloaded from processors to accelerators. During last five years, Intel has released two generations of Xeon Phi accelerators. The goal of this paper is to investigate the performance on both architectures with respect to current processors, and evaluate the suitability of accelerated clusters for the distributed simulation of ultrasound propagation using Fourier-based methods. The paper reveals that the former version of Xeon Phis, the Knight's Corner architecture, suffers from several flaws that reduce the performance far below the Haswell processors. On the other hand, the second generation called Knight's Landing shows very promising performance comparable with current processors.

关键词： Ultrasound simulations Pseudospectral methods k-Wave toolbox Intel Xeon Phi KNC KNL MPI OpenMP Performance evaluation Scaling

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dMazeRunner: Executing Perfectly Nested Loops on Dataflow Accelerators

dMazeRunner: Executing Perfectly Nested Loops on Dataflow Ac...

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Embedded Systems Week / Int Conf on Compilers, Architecture, and Synthesis for Embedded Systems (CASES) / International conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS) / Int Conf on Embedded Software (EMSOFT)

作者： Dave, Shail Kim, Youngbin Avancha, Sasikanth Lee, Kyoungwoo Shrivastava, Aviral Arizona State Univ Compiler Microarchitecture Lab Sch Comp Informat & Decis Syst Engn Tempe AZ 85281 USA Yonsei Univ Seoul South Korea Intel Labs Parallel Comp Lab Bangalore Karnataka India

Dataflow accelerators feature simplicity, programmability, and energy-efficiency and are visualized as a promising architecture for accelerating perfectly nested loops that dominate several important applications, including image and media processing and deep learning. Although numerous accelerator designs are being proposed, how to discover the most efficient way to execute the perfectly nested loop of an application onto computational and memory resources of a given dataflow accelerator (execution method) remains an essential and yet unsolved challenge. In this paper, we propose dMazeRunner - to efficiently and accurately explore the vast space of the different ways to spatiotemporally execute a perfectly nested loop on dataflow accelerators (execution methods). The novelty of dMazeRunner framework is in: i) a holistic representation of the loop nests, that can succinctly capture the various execution methods, ii) accurate energy and performance models that explicitly capture the computation and communication patterns, data movement, and data buffering of the different execution methods, and iii) drastic pruning of the vast search space by discarding invalid solutions and the solutions that lead to the same cost. Our experiments on various convolution layers (perfectly nested loops) of popular deep learning applications demonstrate that the solutions discovered by dMazeRunner are on average 9.16x better in Energy-Delay-Product (EDP) and 5.83x better in execution time, as compared to prior approaches. With additional pruning heuristics, dMazeRunner reduces the search time from days to seconds with a mere 2.56% increase in EDP, as compared to the optimal solution.

关键词： Coarse-grained reconfigurable array dataflow deep neural networks loop optimization energy-efficiency systolic arrays mapping analytical model design space exploration

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Appraisal of Pre-processing Techniques for Automated Detection of Diabetic Retinopathy 5

Appraisal of Pre-processing Techniques for Automated Detecti...

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5th IEEE International conference on parallel, distributed and Grid Computing (PDGC)

作者： Bhardwaj, Charu Jain, Shruti Sood, Meenakshi Jaypee Univ Informat Technol Dept ECE Solan HP India

ISBN: (纸本)9781728106465

Retinopathy is a retinal vascular disease caused by abnormal blood flow that damages the retina and it accounts for 5% of blindness worldwide. There are different causes of Retinopathy as retinopathy of prematurity, diabetic retinopathy and hypertensive retinopathy;this paper mainly focuses on preprocessing techniques employed for the detection of diabetic retinopathy. In this work, literatures reporting different methods for automated diabetic retinopathy detection in color images of retina are analyzed for their strength and shortcoming. Extensive techniques for retinal image screening have been published so far thus this paper presents the evaluation of these methods on a common database of image screening. Different image pre-processing techniques are implemented on fundus images and their performance is compared using performance metrics like mean square error, peak signal to noise ratio and root mean square error. Pre-processing steps used in this work can also be applied to other images of interest to identify and recognize the objects in an image.

关键词： Computer aided screening diabetic retinopathy hypertensive retinopathy retinopathy of prematurity

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A MULTICORE CONVEX OPTIMIZATION ALGORITHM WITH APPLICATIONS TO VIDEO RESTORATION 25

A MULTICORE CONVEX OPTIMIZATION ALGORITHM WITH APPLICATIONS ...

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25th IEEE International conference on image processing (ICIP)

作者： Abboud, Feriel Chouzenoux, Emilie Pesquet, Jean-Christophe Talbot, Hugues WITBE France Puteaux La Defense France Univ Paris Saclay Cent Supelec INRIA Saclay CVN Paris France Univ Paris Est Marne La Vallee ESIEE CNRS LIGMUMR 8049 Champs Sur Marne France

ISBN: (纸本)9781479970612

In this paper, we present a new distributed algorithm for minimizing a sum of non-necessarily differentiable convex functions composed with arbitrary linear operators. The overall cost function is assumed strongly convex. Each involved function is associated with a node of a hypergraph having the ability to communicate with neighboring nodes sharing the same hyperedge. Our algorithm relies on a primal-dual splitting strategy with established convergence guarantees. We show how it can be efficiently implemented to take full advantage of a multicore architecture. The good numerical performance of the proposed approach is illustrated in a problem of video sequence denoising, where a significant speedup is achieved.

关键词： convex optimization distributed algorithms proximal methods video processing parallel programming

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DEEP image CLUSTERING USING CONVOLUTIONAL AUTOENCODER EMBEDDING WITH INCEPTION-LIKE BLOCK 25

DEEP IMAGE CLUSTERING USING CONVOLUTIONAL AUTOENCODER EMBEDD...

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25th IEEE International conference on image processing (ICIP)

作者： Wang, Qiang Xu, Jiaqing Li, Rongchun Qiao, Peng Yang, Ke Li, Shijie Dou, Yong Natl Univ Def Technol Natl Lab Parallel & Distributed Proc Changsha Hunan Peoples R China Natl Univ Def Technol Sch Comp Changsha Hunan Peoples R China

ISBN: (纸本)9781479970612

image clustering is one of the challenging tasks in machine learning, and has been extensively used in various applications. Recently, various deep clustering methods has been proposed. These methods take a two-stage approach, feature learning and clustering, sequentially or jointly. We observe that these works usually focus on the combination of reconstruction loss and clustering loss, relatively little work has focused on improving the learning representation of the neural network for clustering. In this paper, we propose a deep convolutional embedded clustering algorithm with inception-like block (DCECI). Specifically, an inception-like block with different type of convolution filters are introduced in the symmetric deep convolutional network to preserve the local structure of convolution layers. We simultaneously minimize the reconstruction loss of the convolutional autoencoders with inception-like block and the clustering loss. Experimental results on multiple image datasets exhibit the promising performance of our proposed algorithm compared with other competitive methods.

关键词： Deep Clustering Convolutional Autoencoders Kullback-Leibler divergence

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Fish Detection and Tracking in Pisciculture Environment using Deep Instance Segmentation

Fish Detection and Tracking in Pisciculture Environment usin...

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IEEE Region 10 International conference TENCON

作者： C.S. Arvind R Prajwal Prithvi Narayana Bhat A Sreedevi K N Prabhudeva RV College of Engineering Bengaluru India Karnataka Veterinary Animal and Fisheries Sciences University Bidar India

This study presents a novel approach in detecting and tracking of fish in pisciculture. Pisciculture in general involves challenging tasks of counting and monitoring fish in natural or nature like, man-made habitats such as inland fisheries for breeding, feeding and sorting purposes. These are presently achieved using conventional methods that are inefficient when implemented in large-scale commercial productions. To overcome such difficulties and improve the efficiency of the processes, images of fish and fish seeds are captured in natural murky water habitats through a vision sensor on board an unmanned aerial vehicle (UAV). In this research paper, a deep instance segmentation algorithm called Mask R-CNN along with GOTURN tracking algorithm is employed for real time fish detection and tracking. A comparison study is also carried out (i) fish detection on high resolution images (ii) fish detection on high resolution image multi-region parallel processing (iii) fish detection on high resolution image multi-region parallel processing with tracking. The results are found to be accurate with image multi-region parallel processing along with tracking, with an F1 score of 0.91 at 16 frames per seconds on in-land fishes environment.

关键词： Fish Aquaculture Unmanned aerial vehicles Training Machine learning image segmentation Real-time systems

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