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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Marco Crocco Alessio Del Bue Pattern Analysis and Computer Vision (PAVIS) Istituto Italiano di Tecnologia (IIT) Via Morego 30 16163 Genova Italy

ISBN: (纸本)9781479999897

Estimation of Time Difference of Arrivals (TDOAs) corresponding to early room reflections can be formulated as a Blind Channel Identification (BCI) problem, exploiting signals acquired by a set of microphones, given an unknown transmitted source. To cope with the intrinsic noise sensitivity and ill-posedness of the problem, sparsity and non-negativity priors on the Acoustic Impulse Response (AIR) of the room can be exploited. Here we propose a novel iterative method, resulting in a sequence of convex problems relying on a weighted l_1 constraint. The proposed method allows to outperform current state of the art on speech and non-speech real signals, while lowering the number of parameters to tune and the sensitivity of solution given the few free parameters.

关键词： Acoustic Impulse Response Blind Channel Identification Sparsity Non-negative Priors TDOA Estimation channel identification Reflection Time difference of arrival Rooms iterative methods Microphone Device Component LU1 tuning current status

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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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Fast 6D pose estimation for texture-less objects from a single RGB image

Fast 6D pose estimation for texture-less objects from a sing...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： E. Muñoz Y. Konishi V. Murino A. Del Bue Pattern Analysis and Computer Vision (PAVIS) Department Istituto Italiano di Tecnologia (IIT) Genova Italy OMRON corporation Kyoto Japan

A fundamental step to solve bin-picking and grasping problems is the accurate estimation of an object 3D pose. Such visual task usually rely on profusely textured objects: standard procedures such as detection of interest points or computation of appearance-based descriptors are favoured by using a highly informative surface. However, texture-less objects or their parts (i.e., those whose surface texture is poorly conditioned) are common in any environment but still challenging to deal with. This is due the fact that the distribution of surface brightness makes difficult to compute interest points or appearance-based descriptors. In this paper, we propose a method to estimate the 3D pose for texture-less objects given a coarse initialization: the pose is estimated using using edge correspondences, where the similarity measure is encoded using a pre-computed linear regression matrix. Furthermore, we also propose a method to increase the robustness of the estimated pose against background and object clutter. We validate both methods by using synthetic and real image sequences with objects with known ground truth.

关键词： Image edge detection Solid modeling Three-dimensional displays Computational modeling Robustness Training

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Fast 6D pose from a single RGB image using Cascaded Forests Templates

Fast 6D pose from a single RGB image using Cascaded Forests ...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： E. Munoz Y. Konishi C. Beltran V. Murino A. Del Bue Pattern Analysis and Computer Vision (PAVIS) Department Istituto Italiano di Tecnologia (IIT) Genova Italy OMRON corporation Kyoto Japan

ISBN: (纸本)9781509037636

This paper presents a method for 6D pose estimation from a single RGB image for complex texture-less objects. This class of objects are common in any environment but still challenging to deal with. This is due to the fact that the distribution of surface brightness makes difficult to compute interest points or appearance-based descriptors. Here we propose a novel part-based method using an efficient template matching approach where each template independently encodes the similarity function using a Forest trained over the templates. Moreover, accuracy is even more incremented by using a cascade of the learned forest. These templates forests together with the simplicity of the computed image features allow a quick estimate of the pose achieving real-time performance. Performance are demonstrated both on synthetic and real images with known ground truth.

关键词： jigs(templates) cascades ground truth Forest red-green-blue colour model surface brightness image feature images Imagery (Psychotherapy) Image SYNTHETICS

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Visual coverage using autonomous mobile robots for search and rescue applications

Visual coverage using autonomous mobile robots for search an...

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IEEE International Workshop on Safety, Security, and Rescue Robotics (SSRR)

作者： A. Del Bue M. Tamassia F. Signorini V. Murino A. Farinelli Pattern Analysis and Computer Vision Department (PAVIS) Istituto Italiano di Tecnologia (IIT) Genova Italy Department of Computer Science University of Verona Italy

This paper focuses on visual sensing of 3D large-scale environments. Specifically, we consider a setting where a group of robots equipped with a camera must fully cover a surrounding area. To address this problem we propose a novel descriptor for visual coverage that aims at measuring visual information of an area based on a regular discretization of the environment in voxels. Moreover, we propose an autonomous cooperative exploration approach which controls the robot movements so to maximize information accuracy (defined based on our visual coverage descriptor) and minimizing movement costs. Finally, we define a simulation scenario based on real visual data and on widely used robotic tools (such as ROS and Stage) to empirically evaluate our approach. Experimental results show that the proposed method outperforms a baseline random approach and an uncoordinated one, thus being a valid solution for visual coverage in large scale outdoor scenarios.

关键词： Three-dimensional displays Cameras Visualization Robot vision systems

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Adversarial feature augmentation for unsupervised domain adaptation

arXiv

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arXiv 2017年

作者： Volpi, Riccardo Morerio, Pietro Savarese, Silvio Murino, Vittorio Pattern Analysis & Computer Vision Istituto Italiano di Tecnologia Stanford Vision and Learning Lab Stanford University Computer Science Department Università di Verona

Recent works showed that Generative Adversarial Networks (GANs) can be successfully applied in unsupervised domain adaptation, where, given a labeled source dataset and an unlabeled target dataset, the goal is to train powerful classifiers for the target samples. In particular, it was shown that a GAN objective function can be used to learn target features indistinguishable from the source ones. In this work, we extend this framework by (i) forcing the learned feature extractor to be domain-invariant, and (ii) training it through data augmentation in the feature space, namely performing feature augmentation. While data augmentation in the image space is a well established technique in deep learning, feature augmentation has not yet received the same level of attention. We accomplish it by means of a feature generator trained by playing the GAN minimax game against source features. Results show that both enforcing domain-invariance and performing feature augmentation lead to superior or comparable performance to state-of-the-art results in several unsupervised domain adaptation benchmarks. Copyright © 2017, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Passive underwater imaging through optimized planar arrays of hydrophones

Passive underwater imaging through optimized planar arrays o...

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OCEANS

作者： Andrea Trucco Samuele Martelli Marco Crocco Dept. of Electrical University of Genoa Genova Italy Pattern Analysis & Computer Vision (PAVIS) Istituto Italiano di Tecnologia (IIT) Genova Italy

Planar arrays of acoustic sensors represent a critical component for a 3-D wideband passive imaging system. Recently, a method has been proposed for the design of planar microphone arrays that are superdirective (to provide good performance at low frequencies) and aperiodic (to avoid grating lobes at high frequencies). Robustness against dispersion in the sensor characteristics is achieved by exploiting the probability density functions of such errors. Here, such a method is used to demonstrate that 36 low-cost poorly-matched hydrophones are sufficient to populate a square aperture with a side length of 60 cm used as sensor array for an underwater passive system working from 1.5 kHz to 12 kHz.

关键词： Array signal processing Planar arrays Sonar equipment Finite impulse response filters Transducers Layout Robustness

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Are Multiple Cross-Correlation Identities better than just Two? Improving the Estimate of Time Differences-of-Arrivals from Blind Audio Signals

Are Multiple Cross-Correlation Identities better than just T...

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International Conference on pattern Recognition

作者： Danilo Greco Jacopo Cavazza Alessio Del Bue Pattern Analysis and Computer Vision (PAVIS) Istituto Italiano di Tecnologia (IIT) Genova Italy Visual Geometry and Modelling (VGM) Istituto Italiano di Tecnologia (IIT) Genova Italy

Given an unknown audio source, the estimation of time differences-of-arrivals (TDOAs) can be efficiently and robustly solved using blind channel identification and exploiting the cross-correlation identity (CCI). Prior “blind” works have improved the estimate of TDOAs by means of different algorithmic solutions and optimization strategies, while always sticking to the case N=2 microphones. But what if we can obtain a direct improvement in performance by just increasing N? In this paper we try to investigate this direction, showing that, despite the arguable simplicity, this is capable of (sharply) improving upon state-of-the-art blind channel identification methods based on CCI, without modifying the computational pipeline. Inspired by our results, we seek to warm up the community and the practitioners by paving the way (with two concrete, yet preliminary, examples) towards joint approaches in which advances in the optimization are combined with an increased number of microphones, in order to achieve further improvements.

关键词： Measurement Deconvolution Pipelines Estimation Channel estimation Switches Market research

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CANDID: Robust change dynamics and deterministic update policy for dynamic background subtraction

arXiv

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arXiv 2018年

作者： Mandal, Murari Saxena, Prafulla Vipparthi, Santosh Kumar Murala, Subrahmanyam Department of Computer Science & Engineering MNIT Jaipur India Computer Vision and Pattern Recognition Lab Department of Electrical Engineering IIT Ropar India

Background subtraction in video provides the preliminary information which is essential for many computer vision applications. In this paper, we propose a sequence of approaches named CANDID to handle the change detection problem in challenging video scenarios. The CANDID adaptively initializes the pixel-level distance threshold and update rate. These parameters are updated by computing the change dynamics at a location. Further, the background model is maintained by formulating a deterministic update policy. The performance of the proposed method is evaluated over various challenging scenarios such as dynamic background and extreme weather conditions. The qualitative and quantitative measures of the proposed method outperform the existing state-of-the-art approaches. Copyright © 2018, The Authors. All rights reserved.

关键词： Machine learning

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Robust Superdirective End-Fire Arrays

Robust Superdirective End-Fire Arrays

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Oceans 2013 - Norway

作者： A. Trucco F. Traverso M. Crocco Dept. of Electrical Electronic Telecommunications Engineering and Naval Architecture (DITEN) University of Genoa Pattern Analysis & Computer Vision Istituto Italiano di Tecnologia (IIT)

ISBN: (纸本)9781479900022

Oversteering is a technique to achieving robust superdirective performance by end-fire arrays whose transducers are spaced less than half wavelength. To reduce the beamwidth, the main-lobe is pushed past end-fire by the introduction of additional delays. The reduction of the main-lobe sensitivity produces, in turn, an increase of the side-lobe level and a fall of the white noise gain. This paper proposes a method to compute the real-valued weight coefficients that yield the maximum directivity for a given oversteering amount and assure a white noise gain above a given threshold. If this optimization is carried out in parallel with the exhaustive search for the best oversteering amount, the optimum oversteering performance can be reached. Finally, it is shown that for a linear array composed of 8 transducers, the oversteering technique yields a directivity which is very close to that provided by the optimum complex-valued weight coefficients, requiring a simpler processing system and assuring the same robustness (i.e., the two solutions show the same white noise gain).

关键词： End-fire arrays Superdirective beamforming Oversteering Maximum directivity White noise gain

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