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Multi-Scale Dynamic Feature Encoding Network for Image Demoiréing

Multi-Scale Dynamic Feature Encoding Network for Image Demoi...

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international Conference on computer Vision workshops (ICCV workshops)

作者： Xi Cheng Zhenyong Fu Jian Yang PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9781728150239

ISBN: (纸本)9781728150246

The prevalence of digital sensors, such as digital cameras and mobile phones, simplifies the acquisition of photos. Digital sensors, however, suffer from producing Moire when photographing objects having complex textures, which deteriorates the quality of photos. Moire spreads across various frequency bands of images and is a dynamic texture with varying colors and shapes, which pose two main challenges in demoireing - an important task in image restoration. In this paper, towards addressing the first challenge, we design a multi-scale network to process images at different spatial resolutions, obtaining features in different frequency bands, and thus our method can jointly remove moire in different frequency bands. Towards solving the second challenge, we propose a dynamic feature encoding module (DFE), embedded in each scale, for dynamic texture. Moire pattern can be eliminated more effectively via *** proposed method, termed Multi-scale convolutional network with Dynamic feature encoding for image DeMoireing (MDDM), can outperform the state of the arts in fidelity as well as perceptual on benchmarks.

关键词： Spatial resolution Task analysis Encoding Image restoration Image coding Visualization

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Anomalous human behavior detection using a network of RGB-D sensors 2nd

Anomalous human behavior detection using a network of RGB-D ...

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2nd international workshop on Understanding Human Activities Through 3D Sensors, UHA3DS 2016 Held in Conjunction with the 23rd international Conference on pattern Recognition, ICPR 2016

作者： Mosca, Nicola Renò, Vito Marani, Roberto Nitti, Massimiliano Martino, Fabio D’Orazio, Tiziana Stella, Ettore National Research Council of Italy Institute of Intelligent Systems for Automation Via Amendola 122/DO Bari70126 Italy

ISBN: (纸本)9783319918624

The detection of anomalous behaviors of people in indoor environments is an important topic in surveillance applications, especially when low cost solutions are necessary in contexts such as long corridors of public buildings, where standard cameras with long camera view would be either ineffective or costly to implement. This paper proposes a network of low cost RGB-D sensors with no overlapping fields-of-view, capable of identifying anomalous behaviors with respect a pre-learned normal one. A 3D trajectory analysis is carried out by comparing three different classifiers (SVM, neural networks and k-nearest neighbors). The results on real experiments prove the effectiveness of the proposed approach both in terms of performances and of real time application. © 2018, Springer international Publishing AG, part of Springer Nature.

关键词： Cameras

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intelligent speech features mining for robust synthesis system evaluation 7th

Intelligent speech features mining for robust synthesis syst...

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7th Language and Technology Conference: Human Language Technologies as a Challenge for computer Science and Linguistics, LTC 2015

作者： Ekpenyong, Moses E. Inyang, Udoinyang G. Ekong, Victor E. Department of Computer Science University of Uyo P.M.B. 1017 Uyo520003 Nigeria

ISBN: (纸本)9783319937816

Speech synthesis evaluation involves the analytical description of useful features, sufficient to assess the performance of a speech synthesis system. Its primary focus is to determine the degree of semblance of synthetic voice to a natural or human voice. The task of evaluation is usually driven by two methods: the subjective and objective methods, which have indeed become a regular standard for evaluating voice quality, but are mostly challenged by high speech variability as well as human discernment errors. Machine learning (ML) techniques have proven to be successful in the determination and enhancement of speech quality. Hence, this contribution utilizes both supervised and unsupervised ML tools to recognize and classify speech quality classes. Data were collected from a listening test (experiment) and the speech quality assessed by domain experts for naturalness, intelligibility, comprehensibility, as well as, tone, vowel and consonant correctness. During the pre-processing stage, a Principal Component analysis (PCA) identified 4 principal components (intelligibility, naturalness, comprehensibility and tone) – accounting for 76.79% variability in the dataset. An unsupervised visualization using self organizing map (SOM), then discovered five distinct target clusters with high densities of instances, and showed modest correlation between significant input factors. A pattern recognition using deep neural network (DNN), produced a confusion matrix with an overall performance accuracy of 93.1%, thus signifying an excellent classification system. © 2018, Springer international Publishing AG, part of Springer Nature.

关键词： Deep neural networks

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Knowledge acquisition during software development: Modeling with anti-patterns 1st

Knowledge acquisition during software development: Modeling ...

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1st workshop on Knowledge Engineering and Software Engineering, KESE 2005, Co-Located with 28th German Conference on Artificial Intelligence, KI-2005

作者： Smiari, Paraskevi Bibi, Stamatia Stamelos, Ioannis Department of Informatics & Telecommunications Engineering University of Western Macedonia Kozani Greece Department of Computer Science Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (纸本)9783319641607

Knowledge is a strategic resource;that should be timely acquired and exploited to manage and control software development. Software development is a knowledge intensive process characterized by increased uncertainty, presenting large variations among different development environments. Project uncertainty and volatility confounds the traditional knowledge-based processes since at any time traditional software project management techniques and patterns may be considered out of scope. In this chapter a dynamic and constantly adaptive knowledge encapsulation framework is presented. This framework analytically describes (a) metric collection methods along with metrics that attribute to knowledge creation regarding successful software development (b) representation mechanisms of the knowledge created in the form of anti-patterns (c) Bayesian Network analysis technique for converting the data to knowledge allowing inference mechanisms for testing the applicability of the anti-pattern. The presented approach is demonstrated into a case study showing both its feasibility and applicability. © 2018, Springer international Publishing AG.

关键词： Bayesian networks

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CEUR workshop Proceedings

CEUR Workshop Proceedings

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13th international Scientific Conference on Optoelectronic Equipment and Devices in Systems of pattern Recognition, Image and Symbol Information Processing, RECOGN 2017

The proceedings contain 12 papers. The topics discussed include: identification of spatial objects on digital maps;developing an algorithm for identifying bovine latent mastitis based on data complexing from livestock enterprise sensors;development of an algorithm for determining the movement of products between racks based on data from their radio frequency tags;a soft decoding for high rate Reed-Solomon codes;trace-analysis of images of the differential chronogram of the combustion wave for recognition of transitional modes of SHS;synthesis of the general structure and algorithm of controlling the functions of a mobile photoophthalmoscope;mathematical model of a 'tail' computation in a desktop grid;stochastic modelling of large-scale distributed computer systems functioning with group restorations;recognition of flat objects based on dimensionless marks based of their contours;on some features of symmetric diagonal Latin squares;the system for determining the human head position and orientation for vehicle simulators;and data protection in the organization of numerical experiments in distributed computing.

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Early Experiences with Crowdsourcing Airway Annotations in Chest CT 1st

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2nd international workshop on Deep Learning in Medical Image analysis (DLMIA) / 1st international workshop on Large-Scale Annotation of Biomedical Data and Expert Label synthesis (LABELS)

作者： Cheplygina, Veronika Perez-Rovira, Adria Kuo, Wieying Tiddens, Harm A. W. M. de Bruijne, Marleen Erasmus MC Biomed Imaging Grp Rotterdam Dept Med Informat Rotterdam Netherlands Erasmus MC Dept Radiol Rotterdam Netherlands Delft Univ Technol Pattern Recognit Lab Delft Netherlands Sophia Childrens Univ Hosp Erasmus Med Ctr Dept Pediat Pulmonol & Allergol Rotterdam Netherlands Erasmus MC Dept Radiol Rotterdam Netherlands Univ Copenhagen Dept Comp Sci Image Sect Copenhagen Denmark

ISBN: (纸本)9783319469768;9783319469751

Measuring airways in chest computed tomography (CT) images is important for characterizing diseases such as cystic fibrosis, yet very time-consuming to perform manually. Machine learning algorithms offer an alternative, but need large sets of annotated data to perform well. We investigate whether crowdsourcing can be used to gather airway annotations which can serve directly for measuring the airways, or as training data for the algorithms. We generate image slices at known locations of airways and request untrained crowd workers to outline the airway lumen and airway wall. Our results show that the workers are able to interpret the images, but that the instructions are too complex, leading to many unusable annotations. After excluding unusable annotations, quantitative results show medium to high correlations with expert measurements of the airways. Based on this positive experience, we describe a number of further research directions and provide insight into the challenges of crowdsourcing in medical images from the perspective of first-time users.

关键词： Learning systems

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Effective brain connectivity through a constrained autoregressive model 1

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1st international workshop on Simulation and synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th international Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2016

作者： Crimi, Alessandro Dodero, Luca Murino, Vittorio Sona, Diego Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Genoa Italy Department of Computer Science University of Verona Verona Italy Neuroinformatics Laboratory Fondazione Bruno Kessler Trento Italy

ISBN: (数字)9783319467207

ISBN: (纸本)9783319467191

Integration of functional and structural brain connectivity is a topic receiving growing attention in the research community. Their fusion can,in fact,shed new light on brain functions. Targeting this issue,the manuscript proposes a constrained autoregressive model allowing to generate an "effective" connectivity matrix that model the structural connectivity integrating the functional activity. In practice,an initial structural connectivity representation is altered according to functional data,by minimizing the reconstruction error of an autoregressive model constrained by the structural prior. The proposed model has been tested in a community detection framework,where the brain is partitioned using the effective network across multiple subjects. Results showed that using the effective connectivity the resulting clusters better describe the functional interactions of different regions while maintaining the structural organization. © Springer international Publishing AG 2016.

关键词： Brain mapping

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10th Multi-Disciplinary international workshop on Artificial Intelligence, MIWAI 2016

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10th Multi-Disciplinary international workshop on Artificial Intelligence, MIWAI 2016

ISBN: (纸本)9783319493961

The proceedings contain 27 papers. The special focus in this conference is on Multi-disciplinary Trends in Artificial Intelligence. The topics include: A hybrid text representation model for improving text categorization based on ***;an entailment procedure for Kleene answer set programs;an efficient Gaussian kernel based fuzzy-rough set approach for feature selection;an improvement of pattern-based information extraction using intuitionistic fuzzy sets;a hierarchical learning approach for finding multiple vehicle number plate under cluttered background;finding risk-averse shortest path with time-dependent stochastic costs;identification of relevant and redundant automatic metrics for MT evaluation;bankruptcy prediction using memetic algorithm;crowd simulation in 3D virtual environments;resolving the manufacturing cell design problem using the flower pollination algorithm;a new multiple objective cuckoo search for university course timetabling problem;from preference-based to multiobjective sequential decision-making;evolutionary analysis and computing of the financial safety net;learning to navigate in a 3d environment;computing sentiment scores of adjective phrases for Vietnamese;sentiment analysis using anaphoric coreference resolution and ontology inference and a GA-SSO based intelligent channel assignment approach for MR-MC wireless sensors networks.

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Three-Dimensional CT Image Segmentation by Combining 2D Fully Convolutional Network with 3D Majority Voting 1st

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2nd international workshop on Deep Learning in Medical Image analysis (DLMIA) / 1st international workshop on Large-Scale Annotation of Biomedical Data and Expert Label synthesis (LABELS)

作者： Zhou, Xiangrong Ito, Takaaki Takayama, Ryosuke Wang, Song Hara, Takeshi Fujita, Hiroshi Gifu Univ Grad Sch Med Dept Intelligent Image Informat Gifu 5011194 Japan Univ South Carolina Dept Comp Sci & Engn Columbia SC 29208 USA

ISBN: (纸本)9783319469768;9783319469751

We propose a novel approach for automatic segmentation of anatomical structures on 3D CT images by voting from a fully convolutional network (FCN), which accomplishes an end-to-end, voxel-wise multiple-class classification to map each voxel in a CT image directly to an anatomical label. The proposed method simplifies the segmentation of the anatomical structures (including multiple organs) in a CT image (generally in 3D) to majority voting for the semantic segmentation of multiple 2D slices drawn from different viewpoints with redundancy. An FCN consisting of "convolution" and "de-convolution" parts is trained and re-used for the 2D semantic image segmentation of different slices of CT scans. All of the procedures are integrated into a simple and compact all-in-one network, which can segment complicated structures on differently sized CT images that cover arbitrary CT scan regions without any adjustment. We applied the proposed method to segment a wide range of anatomical structures that consisted of 19 types of targets in the human torso, including all the major organs. A database consisting of 240 3D CT scans and a humanly annotated ground truth was used for training and testing. The results showed that the target regions for the entire set of CT test scans were segmented with acceptable accuracies (89 % of total voxels were labeled correctly) against the human annotations. The experimental results showed better efficiency, generality, and flexibility of this end-to-end learning approach on CT image segmentations comparing to conventional methods guided by human expertise.

关键词： CT images Anatomical structure segmentation Fully convolutional network (FCN) 3D majority voting End-to-end learning

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Image pre-processing detection: Evaluation of Benford's law, spatial and frequency domain feature performance

Image pre-processing detection: Evaluation of Benford's law,...

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international workshop on Sensing, Processing and Learning for intelligent Machines (SPLINE)

作者： Tom Neubert Mario Hildebrandt Jana Dittmann Dept. of Computer Science Otto-von-Guericke University of Magdeburg Magdeburg Germany

ISBN: (纸本)9781467389181

This Paper proposes a novel method for the blind detection of image pre-processing techniques by means of statistical pattern recognition in image forensics. The technique is intended to detect sensor intrinsic pre-processing steps as well as manually applied filters. We have exemplary chosen 6 pre-processing filters with different parameter settings. The concept utilizes 29 image features which are supposed to allow for a reliable model creation during supervised learning. The evaluation of the trained models indicates average accuracies between 82.50 and 94.53%. The investigation of image data from 8 sensors leads to the detection of credible pre-processing filters. Those results adumbrate that our method might be suitable to prove the authenticity of the data origin and the integrity of image data based on the detected preprocessing techniques. The preliminary evaluation for manually applied filters yields recognition accuracies between 39.09% (14 classes) and 53.33% (7 classes).

关键词： Feature extraction pattern recognition Transform coding Frequency-domain analysis Data models computer science

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