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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Nikoloas Bakalos Eftychios Protopapadakis Anastasios Doulamis Nikolaos Doulamis National Technical University of Athens Signal Processing Pattern Analysis Computer Vision and Photogrammetry Laboratory Zografou Athens Greece

ISBN: (纸本)9781538675199

In this paper, we introduce a new framework for find out the dance postures from dance sequences. The data exploited are from 3D point joints as being estimated by the Kinect-II sensor. The analysis, instead of other traditional methods, assigns the correct label to each frame of the Kinect-derived sequence with respect to the actual posture involved. On the contrary, the conventional dance classification methods categorize the dances to their styles, type of derived from tempo and music rhythm. Seven classifiers are tested to assess the performance in posture classification. The selected classifiers are either probabilistic or use linear or non-linear kernels such as the Support Vector Machines. We have also included ensemble classification schemes to improve the analysis. Experiments are given to real-world dances focusing on Greek folkloric ones. The results are objectively assessed under the precision and recall metrics and provide a comparative framework.

关键词： Sensors Legged locomotion IP networks Three-dimensional displays Cultural differences Skeleton Classification algorithms

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Performance Evaluation of Subspace-based Algorithm in Selecting Differentially Expressed Genes and Classification of Tissue Types from Microarray Data

Performance Evaluation of Subspace-based Algorithm in Select...

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International Joint Conference on Neural Networks (IJCNN)

作者： J.S. Shaik M. Yeasin Computational Vision Pattern and Image Analysis Laboratory Department of Electrical and Computer Engineering University of Memphis Memphis TN USA

This paper presents the implementation and evaluation of subspace-based clustering algorithm for robust selection of differentially expressed genes as well as the classification of tissue types from microarray data. The performance of the proposed algorithm is compared against other well known clustering algorithms and the quality of clusters is evaluated using a number of cluster validation indices. Empirical analyses on a number of synthetic and real microarray data sets suggest that the proposed subspace-based algorithm is robust in selecting differentially expressed genes and performs significantly better compared to popular clustering algorithms in selecting differentially expressed genes and classifying different tissue types.

关键词： Clustering algorithms Robustness Algorithm design and analysis Data analysis Partitioning algorithms computer vision Image analysis Performance analysis Biomedical imaging Diseases

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Kernel-based classification for brain connectivity graphs on the Riemannian manifold of positive definite matrices

Kernel-based classification for brain connectivity graphs on...

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IEEE International Symposium on Biomedical Imaging

作者： Luca Dodero Hà Quang Minh Marco San Biagio Vittorio Murino Diego Sona Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Genova Italy NeuroInformatics Laboratory Fondazione Bruno Kessler Trento Italy

An important task in connectomics studies is the classification of connectivity graphs coming from healthy and pathological subjects. In this paper, we propose a mathematical framework based on Riemannian geometry and kernel methods that can be applied to connectivity matrices for the classification task. We tested our approach using different real datasets of functional and structural connectivity, evaluating different metrics to describe the similarity between graphs. The empirical results obtained clearly show the superior performance of our approach compared with baseline methods, demonstrating the advantages of our manifold framework and its potential for other applications.

关键词： Manifolds Kernel Measurement Support vector machines Autism Symmetric matrices Laplace equations

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Using Image Morphing for Memory-Efficient Impostor Rendering on GPU

Using Image Morphing for Memory-Efficient Impostor Rendering...

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International Conference on Cyberworlds

作者： Kamer Ali Yuksel Aytul Ercil Alp Yucebilgin Selim Balcisoy Computer Vision and Pattern Analysis Laboratory Faculty of Engineering and Natural Sciences Sabanci University Istanbul Turkey Faculty of Engineering and Natural Sciences Computer Graphics Laboratory Sabanci University Istanbul Turkey

Real-time rendering of large animated crowds consisting thousands of virtual humans is important for several applications including simulations, games and interactive walkthroughs, but cannot be performed using complex polygonal models at interactive frame rates. For that reason, several methods using large numbers of pre-computed image-based representations, which are called as impostors, have been proposed. These methods take the advantage of existing programmable graphics hardware to compensate the computational expense while maintaining the visual fidelity. Making the number of different virtual humans, which can be rendered in real-time, not restricted anymore by the required computational power but by the texture memory consumed for the variety and discretization of their animations. In this work, we proposed an alternative method that reduces the memory consumption by generating compelling intermediate textures using image-morphing techniques. In order to demonstrate the preserved perceptual quality of animations, where half of the key-frames were rendered using the proposed methodology, we have implemented the system using the graphical processing unit and obtained promising results at interactive frame rates.

关键词： Rendering (computer graphics) Animation Graphics processing unit Memory management Cameras Face Humans

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Organ Segmentation with Recursive Data Augmentation for Deep Models 25th

Organ Segmentation with Recursive Data Augmentation for Deep...

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25th International Conference on pattern Recognition Workshops, ICPR 2020

作者： Akbar, Muhammad Usman Yamin, Muhammad Abubakar Murino, Vittorio Sona, Diego Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Genova Italy Department of Electrical Electronics and Telecommunication Engineering Università di Genova Genova Italy Huawei Technologies Ltd. Ireland Research Center Dublin Ireland Department of Computer Science Università di Verona Verona Italy Neuroinformatics Laboratory Fondazione Bruno Kessler Trento Italy

ISBN: (纸本)9783030687625

The precise segmentation of organs from computed tomography is a fundamental and pivotal task for correct diagnosis and proper treatment of diseases. Neural network models are widely explored for their promising performance in the segmentation of medical images. However, the small dimension of available datasets is affecting the biomedical imaging domain significantly and has a huge impact in training of deep learning models. In this paper we try to address this issue by iteratively augmenting the dataset with auxiliary task-based information. This is obtained by introducing a recursive training approach, where a new set of segmented images is generated at each iteration and then concatenated with the original input data as organ attention maps. In the experimental evaluation two different datasets were tested and the results produced from the proposed approach have shown significant improvements in organ segmentation as compared to a standard non-recursive approach. © 2021, Springer Nature Switzerland AG.

关键词： Deep learning

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Laser only feature based multi robot SLAM

Laser only feature based multi robot SLAM

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International Conference on Control, Automation, Robotics and vision (ICARCV)

作者： Syed Riaz un Nabi Jafri Zhao Li Aftab Ahmed Chandio Ryad Chellali Pattern Analysis and Computer Vision (PAVIS) Laboratory Istituto Italiano di Tecnologia Universita degli Studi di Genova Genova Italy Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9781467318716

This paper presents multi-robot simultaneous localization and mapping (SLAM) framework for a team of robots with unknown initial poses. The proposed solution is using feature based Rao-Blackwellised particle filter (RBPF) SLAM for each robot working in an unknown environment equipped only with 2D range sensor and communication module. To represent the environment in compact form, line and corner features (or point features) are used. By sharing and comparing distinct feature based maps of each robot, a global map with known poses is formed without any physical meeting among the robots. This approach can easily applicable to the distributed or centralized robotic systems with ease of data handling and reduced computational cost.

关键词： Robot kinematics Simultaneous localization and mapping Trajectory Feature extraction Green products

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Weakly supervised geodesic segmentation of egyptian mummy CT scans

arXiv

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arXiv 2020年

作者： Hati, Avik Bustreo, Matteo Sona, Diego Murino, Vittorio Del Bue, Alessio Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Genova Italy Neuroinformatics Laboratory Fondazione Bruno Kessler Trento Italy Huawei Technologies Ltd. Ireland Research Center Dublin Ireland Department of Computer Science Università di Verona Italy

In this paper, we tackle the task of automatically analyzing 3D volumetric scans obtained from computed tomography (CT) devices. In particular, we address a particular task for which data is very limited: the segmentation of ancient Egyptian mummies CT scans. We aim at digitally unwrapping the mummy and identify different segments such as body, bandages and jewelry. The problem is complex because of the lack of annotated data for the different semantic regions to segment, thus discouraging the use of strongly supervised approaches. We, therefore, propose a weakly supervised and efficient interactive segmentation method to solve this challenging problem. After segmenting the wrapped mummy from its exterior region using histogram analysis and template matching, we first design a voxel distance measure to find an approximate solution for the body and bandage segments. Here, we use geodesic distances since voxel features as well as spatial relationship among voxels is incorporated in this measure. Next, we refine the solution using a GrabCut based segmentation together with a tracking method on the slices of the scan that assigns labels to different regions in the volume, using limited supervision in the form of scribbles drawn by the user. The efficiency of the proposed method is demonstrated using visualizations and validated through quantitative measures and qualitative unwrapping of the mummy. Copyright © 2020, The Authors. All rights reserved.

关键词： Geodesy

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Weakly Supervised Geodesic Segmentation of Egyptian Mummy CT Scans

Weakly Supervised Geodesic Segmentation of Egyptian Mummy CT...

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International Conference on pattern Recognition

作者： Avik Hati Matteo Bustreo Diego Sona Vittorio Murino Alessio Del Bue Pattern Analysis and Computer Vision (PAVIS) Istituto Italiano di Tecnologia Genova Italy University of Genova Italy Neuroinformatics Laboratory Fondazione Bruno Kessler Trento Italy Ireland Research Center Huawei Technologies Co. Ltd. Dublin Ireland

关键词： Three-dimensional displays Computed tomography Semantics Pipelines Metals Transforms pattern recognition

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Sleep-stage scoring in mice: The influence of data pre-processing on a system's performance

Sleep-stage scoring in mice: The influence of data pre-proce...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Vasiliki-Maria Katsageorgiou Glenda Lassi Valter Tucci Vittorio Murino Diego Sona Department of Pattern Analysis and Computer Vision (PAVIS) Istituto Italiano di Tecnologia Genova Italy Department of Neuroscience and Brain Technologies (NBT) Istituto Italiano di Tecnologia Genova Italy Department of Computer Science University of Verona Verona Italy Neuroinformatics Laboratory (NILab) Fondazione Bruno Kessler (FBK) Trento Italy

Sleep-stage analysis in mice and rats has received growing attention in recent years, due to the fact that mice display electrical activity during sleep which has underlying similarities with that of human sleep. Both conventional manual and automatic sleep-wakefulness scoring are rule based tasks which use brain waves measured by Electroencephalogram (EEG) and activity detected by Electromyography (EMG) of skeletal muscles. Several works have been conducted trying to provide an automatic sleep-scoring system on the basis of machine learning methods. In this study we try to understand the reasons behind the complexity of this problem and we emphasize the importance of normalization procedure that leads to a better stage discrimination comparing different classification methods.

关键词： Sleep Electroencephalography Electromyography Mice Niobium Support vector machines

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Traces of human functional activity: Moment-to-moment fluctuations in fMRI data

Traces of human functional activity: Moment-to-moment fluctu...

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IEEE International Symposium on Biomedical Imaging

作者： Luca Dodero Diego Sona Djalel E. Meskaldji Vittorio Murino Dimitri Van De Ville Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Genova Italy NeuroInformatics Laboratory Fondazione Bruno Kessler Trento Italy Department of Radiology and Medical Informatics University of Geneva Switzerland Institute of Bioengineering Ecole Polytechnique Fédérale de Lausanne Switzerland Dept. of Computer Science University of Verona Italy

ISBN: (纸本)9781479923519

Dynamic functional connectivity (dFC) measured by functional magnetic resonance imaging (fMRI) shows evidence of large-scale networks with highly dynamic (re)configurations. We propose a novel approach to extract traces of human brain function by the construction of a trajectory in a meaningful low-dimensional space. This allows studying dFC in more detail and identify possible meaningful brain states from the moment-to-moment fluctuations of the brain signals during resting state or naturalistic conditions such as passive movie watching. Specifically, we explored dynamic organization of sub-networks derived from the time-dependent graph Laplacian in combination with Riemannian manifold distance to measure dissimilarity over time of dFC and to subsequently build the trajectory of brain activity. As a proof-of-principle, we show results for an fMRI dataset containing both rest and movie epochs in 15 healthy participants. The movie condition varied (i.e., fearful, joyful, and neutral movie excerpts) and clearly influenced the subsequent resting-state period in terms of FC brain state.

关键词： Motion pictures Laplace equations Trajectory Organizations Euclidean distance Manifolds Stress

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