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

共 184 条记录，以下是41-50 订阅

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Exploiting multiple detections to learn robust brightness transfer functions in re-identification systems

Exploiting multiple detections to learn robust brightness tr...

引用

IEEE International Conference on Image Processing

作者： Amran Bhuiyan Alessandro Perina Vittorio Murino Pattern Analysis and Computer Vision (PAVIS) Istituto Italiano di Tecnologia Genova Italy

ISBN: (纸本)9781479983407

Re-identification systems aim at recognizing the same individuals in multiple cameras and one of the most relevant problems is that the appearance of same individual varies across cameras due to illumination and viewpoint changes. This paper proposes the use of Cumulative Weighted Brightness Transfer Functions to model this appearance variations. It is multiple frame-based learning approach which leverages consecutive detections of each individual to transfer the appearance, rather than learning brightness transfer function from pairs of images. We tested our approach on standard multi-camera surveillance datasets showing consistent and significant improvements over existing methods on three different datasets without any other additional cost. Our approach is general and can be applied to any appearance-based method.

关键词： Cameras Transfer functions Brightness Histograms Bismuth Robustness Image color analysis

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Multimodal segmentation of medical images with heavily missing data

Multimodal segmentation of medical images with heavily missi...

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2021 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2021

作者： Akbar, Muhammad Usman Murino, Vittorio Sona, Diego Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Genova Italy Department of Electrical Electronics and Telecommunication Engineering degli Studi di Genova Genova Italy Huawei Technologies Ltd. Ireland Research Center Dublin Ireland Department of Computer Science Università di Verona Verona Italy Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Genova Italy Data Science for Health Center for Digital Health & Wellbeing Fondazione Bruno Kessler Trento Italy

ISBN: (纸本)9781665403580

An important aim of research in medical imaging is the development of computer aided diagnosis (CAD) systems. A fundamental step in these systems is the image segmentation and convolutional neural networks (CNNs) are becoming the most commonly used approach to solve this task. However, despite their great power, in this domain CNNs are limited in their potential performance by the usually small amount of data [1]. Computed tomography (CT) and magnetic resonance imaging (MRI) scans are often used to examine the internal structure of human body and have their own unique properties and limitations. As a common practice, the investigations are usually done on a single modality, nonetheless, the simultaneous analysis of multiple modalities can significantly boost the segmentation accuracy. However, obtaining multiple imaging modalities for the same subject is very unlikely. In this paper we investigate the possibility of generating a multimodal CT-MRI representation for a segmentation task starting from a single modality, either CT or MRI. We considered this as a missing data problem, hence, we designed a pipeline where a CycleGAN was used to generate the missing modality. The synthetic modality was then paired with the real one to perform the required segmentation taking advantage of the multimodal representation and the augmented training dataset. To test the system we used two unrelated labeled datasets, one with CT data and the other one with MRI data. Results show that data enrichment with synthetic modalities improves the segmentation performance. © 2021 IEEE

关键词： Magnetic resonance imaging

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Is It Time to Rethink Motion Artifacts? Temporal Relationships between Electrodermal Activity and Body Movements in Real-Life Conditions

Is It Time to Rethink Motion Artifacts? Temporal Relationshi...

引用

International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII

作者： Ryad Chellali Shannon Hennig Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia (IIT) Genova Italy

This work investigates the temporal dynamics between electro dermal activity (EDA) and gestures during real-life public speaking. Wrist movements and EDA were recorded from speakers during a high-stakes public speaking event. The extreme values theory, more typically used for detecting relatively rare events in financial markets or flood calculations, was adapted and combined with continuous wavelet transform to automatically detect significant temporal events from the derivatives of the movement and EDA time series. The literature describing motion artifacts in EDA predicts that detected movement events should precede or co-occur with EDA. In contrast, we also observed a second case in which EDA changes preceded movement (approximately 40% of cases). These results suggest that humans may move or gesture in response to affective changes, which has clear implications for anyone studying arousal and gesture 'in the wild.

关键词： Time series analysis Shape Skin Continuous wavelet transforms Sensors

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SC3K: Self-supervised and Coherent 3D Keypoints Estimation from Rotated, Noisy, and Decimated Point Cloud Data

SC3K: Self-supervised and Coherent 3D Keypoints Estimation f...

引用

International Conference on computer vision (ICCV)

作者： Mohammad Zohaib Alessio Del Bue Pattern Analysis & Computer Vision (PAVIS) Italian Institute of Technology (IIT) Genoa Italy

This paper proposes a new method to infer keypoints from arbitrary object categories in practical scenarios where point cloud data (PCD) are noisy, down-sampled and arbitrarily rotated. Our proposed model adheres to the following principles: i) keypoints inference is fully unsupervised (no annotation given), ii) keypoints position error should be low and resilient to PCD perturbations (robustness), iii) keypoints should not change their indexes for the intra-class objects (semantic coherence), iv) keypoints should be close to or proximal to PCD surface (compactness). We achieve these desiderata by proposing a new self-supervised training strategy for keypoints estimation that does not assume any a priori knowledge of the object class, and a model architecture with coupled auxiliary losses that promotes the desired keypoints properties. We compare the keypoints estimated by the proposed approach with those of the state-of-the-art unsupervised approaches. The experiments show that our approach outperforms by estimating keypoints with improved coverage (+9.41%) while being semantically consistent (+4.66%) that best characterize the object’s 3D shape for downstream tasks. Code and data are available at: https://***/IIT-PAVIS/SC3K

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UNSUPERVISED DETECTION OF WHITE MATTER FIBER BUNDLES WITH STOCHASTIC NEURAL NETWORKS

UNSUPERVISED DETECTION OF WHITE MATTER FIBER BUNDLES WITH ST...

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IEEE International Conference on Image Processing

作者： M. Dayan V.-M. Katsageorgiou L. Dodero V. Murino D. Sona Pattern Analysis and Computer Vision (PAVIS) Istituto Italiano di Tecnologia Genova Italy

Exploring the human structural connectome often involves dealing with millions of white matter tracts reconstructed from diffusion MRI. Reducing the dimensionality of such data by grouping tracts into bundles can prove essential for subsequent analyses. Many unsupervised clustering algorithms aim at providing such bundles but often require the choice of a distance metric and suffer from memory storage issues relating to the size of the datasets. We propose for the first time a neural network approach for the unsupervised clustering of white matter tracts. It has the main properties of learning automatically the tract features and scaling well with the data size. As a proof of concept, we compare both quantitatively and qualitatively the computed tract clusters with a commonly used clustering method. The proposed approach shows results similar to the reference approach while not using any distance matrix or similarity metric.

关键词： Neural networks Data models Magnetic resonance imaging Clustering algorithms White matter Measurement Training

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Multi-view Photometric Stereo Using Semi-isometric Mappings

Multi-view Photometric Stereo Using Semi-isometric Mappings

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International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT)

作者： Reza Sabzevari Alessio Del Bue Vittorio Murino Pattern Analysis and Computer Vision (PAVIS) Istituto Italiano di Tecnologia Genoa Italy

Classical uncalibrated Photometric Stereo approaches are mostly constrained to the static view assumption that enforces the camera to be fixed in front of an object illuminated by different light sources. Attempts to extend PS to multi-views has delivered methods that can only be robust to images taken with short camera baselines. In this paper, we present a new uncalibrated Multi-View Photometric Stereo (MVPS) method that can obtain a dense 3D reconstruction from views subject to strong baseline variations and extreme changes in illumination conditions. This approach is intrinsically photo geometric obtaining robust results using a combination of multi-view geometry and photometry. At the core of the algorithm, there is an efficient planar embedding and local image pixel registration among views that renders the problem tractable and computationally solvable. In the experiments, the results are evaluated and compared with the existing methods as well as the ground truth and shows the method is able to deal with the most complex objects and lighting conditions.

关键词： Lighting Transmission line matrix methods Shape Cameras Three dimensional displays Surface reconstruction Image reconstruction

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Adaptive and safe mobile manipulator for human robot interaction

Adaptive and safe mobile manipulator for human robot interac...

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IEEE Workshop on Robotic Intelligence in Informationally Structured Space, RIISS

作者： Amal Meddahi Ryad Chellali Pattern Analysis and Computer Vision Laboratory Istituto Italiano di Tecnologia Università degli Studi di Genova Genova Italy Pattern Analysis and Computer Vision Laboratory Istituto Italiano di Tecnologia Genova Italy

Mobile robotic manipulators will be more and more present in our daily life. Many applications use already these platforms such as for homecare, industrial logistics, etc. and most of the time with in vicinity of humans, within dynamic and unknown environments. This poses the problem of adaptability and safety: robots should adapt to support a wide range of human users behaviors, with a strong guaranty of users' safety. In addition, such functions should take place with environments that are dynamic and not known a priori. This contribution reports on the mobile manipulator we are developing. We focus on solving the safe path planning problem for both the robot base and the robot manipulator in the presence of humans and dynamic obstacles: a safety distance should be strictly maintained by the robot with regard to the person involved in the interactive task as well as with any other person or moving object potentially colliding with the robot while performing the assigned task. These two requirements are achieved in real time by a multi-objective optimization that controls the base and the manipulator based on the task definition and the sensing data provided by the embedded sensors. Simulated scenarios have been tested and the obtained results are presented and discussed.

关键词： Robot kinematics Mobile communication Robot sensing systems Manipulator dynamics Mobile robots

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A Multi-View Learning Approach to Deception Detection

A Multi-View Learning Approach to Deception Detection

引用

International Conference on Automatic Face and Gesture Recognition

作者： Nicolo Carissimi Cigdem Beyan Vittorio Murino Pattern Analysis and Computer Vision (PAVIS) Istituto Italiano di Tecnologia Genova Italy

Recently, automatic deception detection has gained momentum thanks to advances in computer vision, computational linguistics and machine learning research fields. The majority of the work in this area focused on written deception and analysis of verbal features. However, according to psychology, people display various nonverbal behavioral cues, in addition to verbal ones, while lying. Therefore, it is important to utilize additional modalities such as video and audio to detect deception accurately. When multi-modal data was used for deception detection, previous studies concatenated all verbal and nonverbal features into a single vector. This concatenation might not be meaningful, because different feature groups can have different statistical properties, leading to lower classification accuracy. Following this intuition, we apply, for the first time in deception detection, a multi-view learning (MVL) approach, where each view corresponds to a feature group. This results in improved classification results over the state of the art methods. Additionally, we show that the optimized parameters of the MVL algorithm can give insights into the contribution of each feature group to the final results, thus revealing the importance of each feature and eliminating the need of performing feature selection as well. Finally, we focus on analyzing face-based low level, not hand crafted features, which are extracted using various pre-trained Deep Neural Networks (DNNs), showing that face is the most important nonverbal cue for the detection of deception.

关键词： Feature extraction Face Support vector machines Encoding Machine learning Physiology Syntactics

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Image foreground extraction — A supervised framework based on region transfer

Image foreground extraction — A supervised framework based ...

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International Conference on Signal and Information Processing (IConSIP)

作者： Biplab Banerjee Sudipan Saha Shabbir N. Merchant Pattern Analysis and Computer Vision Group Istituto Italiano Di Tecnologia Genoa Italy

ISBN: (纸本)9781509015238

We propose a simple technique for extracting dominant foreground objects from images in this paper. Image foreground segmentation is a challenging task given problems due to complex background, clutter and occlusion, spectral variability in object appearance etc. Most of the techniques in this respect are semi-automatic in nature which require prior assumptions regarding the object location in the image in the form of rough bounding boxes. On the contrary, we propose a patch transfer based approach which propagates rough segmentation masks from the training to the test image patches. Further, these segmentation masks are combined and updated using a Markov random field model to generate the final foreground segmentation. The proposed technique is simple, robust and can work with very few labeled samples. Results obtained on the Weizmann Horse and the Leeds Butterfly dataset exhibit the superiority of the proposed approach with respect to existing methods from the literature.

关键词： Image segmentation Proposals Training Semantics Active contours Hidden Markov models Object recognition

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Weighted bag of visual words for object recognition

Weighted bag of visual words for object recognition

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作者： Biagio, Marco San Bazzani, Loris Cristani, Marco Murino, Vittorio Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Via Morego 30 Genova16163 Italy Departimento di Informatica Università di Verona Strada le Grazie 15 Verona37134 Italy

ISBN: (纸本)9781479957514

Bag of Visual words (BoV) is one of the most successful strategy for object recognition, used to represent an image as a vector of counts using a learned vocabulary. This strategy assumes that the representation is built using patches that are either densely extracted or sampled from the images using feature detectors. However, the dense strategy captures also the noisy background information, whereas the feature detection strategy can lose important parts of the objects. In this paper we propose a solution in-between these two strategies, by densely extracting patches from the image, and weighting them accordingly to their salience. Intuitively, highly salient patches have an important role in describing an object, while those with low saliency are still taken with low emphasis, instead of discarding them. We embed this idea in the word encoding mechanism adopted in the BoV approaches. The technique is successfully applied to vector quantization and Fisher vector, on Caltech-101 and Caltech-256. © 2014 IEEE.

关键词： Object recognition

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