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检索条件"机构=Pattern Analysis and Computer Vision"

共 184 条记录，以下是61-70 订阅

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Two-way Clustering using Fuzzy ASI for Knowledge Discovery in Microarrays

Two-way Clustering using Fuzzy ASI for Knowledge Discovery i...

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IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)

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

This paper presents two-way clustering of microarray data using fuzzy adaptive subspace iteration (ASI) based algorithm for knowledge discovery in microarrays. It is widely believed that each gene is involved in more than one cellular function or biological process. The proposed fuzzy ASI assigns a relevance value to each gene associated with each cluster. These functional categories are ranked based on their potential in providing maximal separation between the two tissues classes; which is an indication of differentially expressed genes (DEGs). Empirical analyses on simulated, 100 artificial microarray datasets are used to quantify the results obtained using the fuzzy-ASI algorithm. Further analyses on different microarray cancer datasets revealed several important genes that are relevant with various cancers.

关键词： Clustering algorithms Biological processes computer vision Algorithm design and analysis Cancer Computational intelligence Bioinformatics Computational biology Image analysis Biology computing

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Analyzing Tracklets for the Detection of Abnormal Crowd Behavior

Analyzing Tracklets for the Detection of Abnormal Crowd Beha...

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IEEE Workshop on Applications of computer vision (WACV)

作者： Hossein Mousavi Sadegh Mohammadi Alessandro Perina Ryad Chellali Vittorio Murino Pattern Analysis and Computer Vision Department (PAVIS) Istituto Italiano di Tecnologia Genova Italy

This paper presents a novel video descriptor, referred to as Histogram of Oriented Tracklets, for recognizing abnormal situation in crowded scenes. Unlike standard approaches that use optical flow, which estimates motion vectors only from two successive frames, we built our descriptor over long-range motion trajectories which is called tracklets in the literature. Following the standard procedure, we divided video sequences in spatio-temporal cuboids within which we collected statistics on the tracklets passing through them. In particular, we quantized orientation and magnitude in a 2-dimensional histogram which encodes the motion patterns expected in each cuboid. We classify frames as normal and abnormal by using Latent Dirichlet Allocation and Support Vector Machines. We evaluated the effectiveness of the proposed descriptors on three datasets: UCSD, Violence in Crowds and UMN. The experiments demonstrated (i) very promising results in abnormality detection, (ii) setting new state-of-the-art on two of them, and (iii) outperforming former descriptors based on the optical flow, dense trajectories and the social force model.

关键词： Tracking Histograms Standards Training Trajectory Dynamics Computational modeling

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Piecewise single view Photometric Stereo with multi-view constraints

Piecewise single view Photometric Stereo with multi-view con...

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IEEE International Conference on Image Processing

作者： Reza Sabzevari Alessio Del Bue Vittorio Murino PAVIS - Pattern Analysis & Computer Vision Istituto Italiano di Tecnologia (IIT) Genova Italy

This paper presents a novel Photometric Stereo approach for static views that recasts the problem into a piecewise formulation. The proposed algorithm, called Piecewise Photometric Stereo (PPS), entails several advantages in respect to previous global approaches. It is intrinsically more efficient, since reconstructing the surface in patches is computationally faster than reconstructing the global surface. Each patch has been associated an individual photometric model rather than a single global model as used in classical approaches. In this way, the piecewise formulation may grasp more complex lighting effects. Finally, the global metric properties of the shape is preserved using the multi-view constraints. In this pipeline, structure from motion is exploited to define such set of constraints and to compose a 3D mesh representing the metric structure of the object. Real results with ground truth show the positive performance of our algorithm compared with a classical global approach for Photometric Stereo.

关键词： Surface reconstruction Shape Lighting Image reconstruction Stereo vision Measurement Pipelines

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A distributed multi robot SLAM system for environment learning

A distributed multi robot SLAM system for environment learni...

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IEEE Workshop on Robotic Intelligence in Informationally Structured Space, RIISS

作者： Syed Riaz un Nabi Jafri Ryad Chellali Pattern Analysis and Computer Vision (PAVIS) Laboratory Istituto Italiano di Tecnologia Universita degli Studi di Genova Genova Italy Pattern Analysis and Computer Vision (PAVIS) Laboratory Istituto Italiano di Tecnologia Genova Italy

This paper presents a multi mobile robot simultaneous localization and mapping (SLAM) system for feature based environment learning by using team of exploring robots. The environmental information is measured through the dynamic sensor network in the shape of moving robots with unknown initial poses. Each robot is equipped with 2D laser scanner and a webcam and it serves as a moving sensor node to perceive horizontal and vertical line features respectively. All the moving nodes are responsible to build the informational structured space. The proposed system is using a unified Extended Kalman Filter (EKF) based SLAM framework for each robot which eventually builds a line feature based partial 3D model of the environment. Each moving robotic sensor node then shares its feature based map model to other moving nodes which are in communication range. A global map model is then transformed after getting mutual pose estimation of the robots by matching mutual common map features in addition by taking visual confirmation of other robot. The proposed system has been tested in an indoor environment and results are shown in the paper.

关键词： Simultaneous localization and mapping Cameras Feature extraction Lasers Robot vision systems

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Predicting Gaze from Egocentric Social Interaction Videos and IMU Data 21

Predicting Gaze from Egocentric Social Interaction Videos an...

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23rd ACM International Conference on Multimodal Interaction, ICMI 2021

作者： Thakur, Sanket Kumar Beyan, Cigdem Morerio, Pietro Del Bue, Alessio Visual Geometry and Modelling Istituto Italiano di Tecnologia Genoa Italy Dept. of Information Engineering and Computer Science University of Trento Trento Italy Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Genoa Italy Visual Geometry and Modelling Italy

ISBN: (纸本)9781450384810

Gaze prediction in egocentric videos is a fairly new research topic, which might have several applications for assistive technology (e.g., supporting blind people in their daily interactions), security (e.g., attention tracking in risky work environments), education (e.g., augmented / mixed reality training simulators, immersive games) and so forth. Egocentric gaze is typically estimated from video while few works attempt to use inertial measurement unit (IMU) data, a sensor modality often available in wearable devices (e.g., augmented reality headsets). Instead, in this paper, we examine whether joint learning of egocentric video and corresponding IMU data can improve the first-person gaze prediction compared to using these modalities separately. In this respect, we propose a multimodal network and evaluate it on several unconstrained social interaction scenarios captured by a first-person perspective. The proposed multimodal network achieves better results compared to unimodal methods as well as several (multimodal) baselines, showing that using egocentric video together with IMU data can boost the first-person gaze estimation performance. © 2021 ACM.

关键词： Augmented reality

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Approximate Log-Hilbert-Schmidt Distances between Covariance Operators for Image Classification

Approximate Log-Hilbert-Schmidt Distances between Covariance...

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IEEE Conference on computer vision and pattern Recognition

作者： Ha Quang Minh Marco San Biagio Loris Bazzani Vittorio Murino Pattern Analysis and Computer Vision (PAVIS) Istituto Italiano di Tecnologia (IIT) Department of Computer Science Dartmouth College

ISBN: (纸本)9781467388528

This paper presents a novel framework for visual object recognition using infinite-dimensional covariance operators of input features, in the paradigm of kernel methods on infinite-dimensional Riemannian manifolds. Our formulation provides a rich representation of image features by exploiting their non-linear correlations, using the power of kernel methods and Riemannian geometry. Theoretically, we provide an approximate formulation for the Log-Hilbert-Schmidt distance between covariance operators that is efficient to compute and scalable to large datasets. Empirically, we apply our framework to the task of image classification on eight different, challenging datasets. In almost all cases, the results obtained outperform other state of the art methods, demonstrating the competitiveness and potential of our framework.

关键词： Image classification state of the art Covariance object recognition Operon Frameworks Riemannian geometry kernel method Operators OPERATORS Approximate Competitiveness Scalability Dataset

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Encoding structural similarity by cross-covariance tensors for image classification

Encoding structural similarity by cross-covariance tensors f...

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作者： Biagio, Marco San Martelli, Samuele Crocco, Marco Cristani, Marco Murino, Vittorio Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Via Morego 30 Genova16163 Italy Universitá degli Studi di Verona Dipartimento di Informatica Strada le Grazie 15 Verona37134 Italy

In computer vision, an object can be modeled in two main ways: by explicitly measuring its characteristics in terms of feature vectors, and by capturing the relations which link an object with some exemplars, that is, in terms of similarities. In this paper, we propose a new similaritybased descriptor, dubbed structural similarity cross-covariance tensor (SS-CCT), where self-similarities come into play: Here the entity to be measured and the exemplar are regions of the same object, and their similarities are encoded in terms of cross-covariance matrices. These matrices are computed from a set of low-level feature vectors extracted from pairs of regions that cover the entire image. SS-CCT shares some similarities with the widely used covariance matrix descriptor, but extends its power focusing on structural similarities across multiple parts of an image, instead of capturing local similarities in a single region. The effectiveness of SSCCT is tested on many diverse classification scenarios, considering objects and scenes on widely known benchmarks (Caltech-101, Caltech-256, PASCAL VOC 2007 and SenseCam). In all the cases, the results obtained demonstrate the superiority of our new descriptor against diverse competitors. Furthermore, we also reported an analysis on the reduced computational burden achieved by using and efficient implementation that takes advantage from the integral image representation. © World Scientific Publishing Company.

关键词： Covariance matrix

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A Progressive Framework for Two-Way Clustering Using Adaptive Subspace Iteration for Functionally Classifying Genes

A Progressive Framework for Two-Way Clustering Using Adaptiv...

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International Joint Conference on Neural Networks (IJCNN)

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

This paper presents an adaptive subspace based two-way clustering of microarray data. To analyze the data at various scales a "Progressive" framework is introduced. The goals are to functionally classify genes and also to find differentially expressed genes in microarray expression profiles. Empirical analysis on Colon Cancer dataset shows that ASI performs favorably in grouping genes with similar functions and finding genes that may have been involved in the formation of colon cancer. It was also observed that the proposed algorithm is robust against ordering of samples and yield results consistent with ground truth information.

关键词： Clustering algorithms Cancer Colon Data analysis Robustness Biological tissues Proteins computer vision Image analysis Performance analysis

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Audio-Visual Inpainting: Reconstructing Missing Visual Information with Sound

Audio-Visual Inpainting: Reconstructing Missing Visual Infor...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Valentina Sanguineti Sanket Thakur Pietro Morerio Alessio Del Bue Vittorio Murino Pattern Analysis and Computer Vision (PAVIS) - Istituto Italiano di Tecnologia University of Genova University of Verona

We tackle audio-visual inpainting, the problem of completing an image in such a way to be consistent with the sound associated to the scene. To this end, we propose a multimodal, audio-visual inpainting method (AVIN), and show how to leverage sound to reconstruct semantically consistent images. AVIN is a 2-stage algorithm, which first learns the scene semantics and reconstructs low resolution images based on a conditional probability distribution of pixels in the space conditioned to audio, and then refines such result with a GAN-based network to increase the resolution of the reconstructed image. We show that AVIN is able to recover the original content, especially in the hard cases where the missing area heavily degrades the scene semantics: it can perform cross-modal generation whenever no visual context is observed at all, reconstructing visual data from sound only. Code will be made available upon acceptance.

关键词： Visualization Image resolution Semantics Signal processing algorithms Signal processing Probability distribution Task analysis

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Trusting Skype: Learning the Way People Chat for Fast User Recognition and Verification

Trusting Skype: Learning the Way People Chat for Fast User R...

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International Conference on computer vision Workshops (ICCV Workshops)

作者： Giorgio Roffo Marco Cristani Loris Bazzani Hà Quang Minh Vittorio Murino Department of Computer Science University of Verona Verona Pattern Analysis & Computer Vision Istituto Italiano di Tecnologia Genova

Identity safekeeping on chats has recently become an important problem on social networks. One of the most important issues is identity theft, where impostors steal the identity of a person, substituting her in the chats, in order to have access to private information. In the literature, the problem has been addressed by designing sets of features which capture the way a person interacts through the chats. However, such approaches perform well only on the long term, after a long conversation has been performed, this is a problem, since in the early turns of a conversation, much important information can be stolen. This paper focuses on this issue, presenting a learning approach which boosts the performance of user recognition and verification, allowing to recognize a subject with considerable accuracy. The proposed method is based on a recent framework of one-shot multi-class multi-view learning, based on Reproducing Kernel Hilbert Spaces (RKHS) theory. Our technique reaches a recognition rate of 76.9% in terms of AUC of the Cumulative Matching Characteristic curve, with only 10 conversational turns considered, on a total of 78 subjects. This sets the new best performances on a public conversation benchmark.

关键词： Kernel Probes Feature extraction Vectors Training Testing Accuracy

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