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International Joint Conference on Neural Networks (IJCNN)

作者： Suli Ji Baochang Zhang Dandan Du Biao He Jianzhuang Liu Science and Technology on Aircraft Control Labority Beihang University Beijing China Pattern Analysis and Computer Vision (PAVIS) Istituto Italiano di Tecnologia (IIT) Italy Chinese Department of Information Engineering Huawei Technologies Co. Ltd

ISBN: (纸本)9781479914821

This paper presents a non-linear pre-processing method based on Similarity Michaelis-Menten law (SMML) for face recognition. Similarity Michaelis-Menten law can be used to explain visual sensitivity in the vertebrate retina. We preprocess input images using SMML, and then employ Local Binary pattern (LBP) for face feature extraction. Advantages of SMML include improvement of light adaption, noise effect, detection right rate, robustness and efficiency, which inspire us exploit it for face pre-processing descriptor for the first time in the field of face recognition. And the parameters of SMML are spatiotemporally and locally estimated by the input image itself employing Sobel, which shows its advantages for face recognition. Extensive experiments clearly demonstrate the superiority of our method over the ones which only use LBP on FERET database in many aspects including the robustness against different facial expressions, lighting and aging of the subjects.

关键词： Face recognition Retina Photoreceptors Adaptation models Face Mathematical model Histograms

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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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Artificial Intelligence tools to predict the level of defectiveness of existing bridges

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Procedia Structural Integrity 2023年 44卷 2020-2027页

作者： Agnese Natali Milind G. Padalkar Vincenzo Messina Walter Salvatore Pietro Morerio Alessio Del Bue Carlos Beltrán-González Department of Industrial and Civil Engineering University of Pisa Largo Lucio Lazzarino 1 56122 Pisa Italy Pattern Analysis and Computer Vision (PAVIS) Fondazione Istituto Italiano di Tecnologia (IIT) 16163 Genova Italy Consorzio Fabre Largo Lucio Lazzarino 1 56122 Pisa Italy

The “Italian guidelines for the maintenance of bridges” propose a qualitative method for the classification of structural, seismic, hydraulic and geotechnical risk of infrastructures. Focusing on the structural risk, one of the key parameters that significantly drives the evaluation of the vulnerability class is the level of defectiveness. The level of defectiveness can be determined only after the execution of the visual inspections, which are necessary to point out the type of damages that affect the structure, their intensity, size and position in each structural component of the bridge. Given the high number of structures to be checked and the time that is necessary to execute the visual inspections of all these bridges, an instrument to have a starting idea of the conservation status of the structure could be helpful to establish an order of priority for the bridges to be investigated. With such purpose in mind, this paper presents an ongoing activity based on the use of Artificial Intelligence to develop a smart tool that recognizes the different elements that compose the bridge, the defects, their intensity, size and position. This tool could be applied to an automatic image collection process, e.g. using a drone that, with minimal user interaction, captures images of the structure and provides quick feedback to the operators.

关键词： Bridges visual inspections Damage detection Artificial Intelligence Deep Learning

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MsEDNet: Multi-Scale Deep Saliency Learning for Moving Object Detection

MsEDNet: Multi-Scale Deep Saliency Learning for Moving Objec...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Prashant W. Patil Subrahmanyam Murala Abhinav Dhall Sachin Chaudhary Indian Institute of Technology Delhi New Delhi Delhi IN Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar INDIA Learning Afffect and Semantic Image AnalysIs (LASII) Group Indian Institute of Technology Ropar INDIA

Moving object detection (foreground and background) is an important problem in computer vision. Most of the works in this problem are based on background subtraction. However, these approaches are not able to handle scenarios with infrequent motion of object, illumination changes, shadow, camouflage etc. To overcome these, here a two stage robust and compact method for moving object detection (MOD) is proposed. In first stage, to generate the saliency map, background image is estimated using a temporal histogram technique with the help of several input frames. In the second stage, multiscale encoder-decoder network is used to learn multiscale semantic feature of estimated saliency for foreground extraction. The encoder is used to extract multi-scale features from multi-scale saliency map. The decoder part is designed to learn the mapping of low resolution multi-scale features into high resolution output frame. To observe the efficacy of proposed MsEDNet, experiments are conducted on two benchmark datasets (change detection (CDnet-2014) and Wallflower) for MOD. The precision, recall and F-measure are used as performance parameter for comparison with the existing state-of-the-art methods. Experimental results show a significant improvement in detection accuracy and decrement in execution time as compared to the state-of-the-art methods for MOD.

关键词： Feature extraction Estimation Decoding Object detection Histograms Lighting Semantics

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Bose Einstein condensate as nonlinear block of a Machine Learning pipeline

arXiv

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arXiv 2023年

作者： Hans, Maurus Kath, Elinor Sparn, Marius Liebster, Nikolas Strobel, Helmut Oberthaler, Markus K. Draxler, Felix Schnörr, Christoph Kirchhoff-Institut für Physik Universität Heidelberg Im Neuenheimer Feld 227 Germany Computer Vision and Learning Lab Universität Heidelberg Germany Image and Pattern Analysis Group Universität Heidelberg Germany

Physical systems can be used as an information processing substrate and with that extend traditional computing architectures. For such an application the experimental platform must guarantee pristine control of the initial state, the temporal evolution and readout. All these ingredients are provided by modern experimental realizations of atomic Bose Einstein condensates. By embedding the nonlinear evolution of a quantum gas in a Machine Learning pipeline, one can represent nonlinear functions while only linear operations on classical computing of the pipeline are necessary. We demonstrate successful regression and interpolation of a nonlinear function using a quasi one-dimensional cloud of potassium atoms and characterize the performance of our system. © 2023, CC BY.

关键词： Pipelines

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Leveraging Next-Active Objects for Context-Aware Anticipation in Egocentric Videos

Leveraging Next-Active Objects for Context-Aware Anticipatio...

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IEEE Workshop on Applications of computer vision (WACV)

作者： Sanket Thakur Cigdem Beyan Pietro Morerio Vittorio Murino Alessio Del Bue Pattern Analysis and Computer Vision (PAVIS) Istituto Italiano di Tecnologia (IIT) Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN) University of Genoa Italy Department of Management Information and Production Engineering University of Bergamo Dalmine Italy Department of Computer Science University of Verona Italy

Objects are crucial for understanding human-object interactions. By identifying the relevant objects, one can also predict potential future interactions or actions that may occur with these objects. In this paper, we study the problem of Short-Term Object interaction anticipation (STA) and propose NAOGAT (Next-Active-Object Guided Anticipation Transformer), a multi-modal end-to-end transformer network, that attends to objects in observed frames in order to anticipate the next-active-object (NAO) and, eventually, to guide the model to predict context-aware future actions. The task is challenging since it requires anticipating future action along with the object with which the action occurs and the time after which the interaction will begin, a.k.a. the time to contact (TTC). Compared to existing video modeling architectures for action anticipation, NAOGAT captures the relationship between objects and the global scene context in order to predict detections for the next active object and anticipate relevant future actions given these detections, leveraging the objects’ dynamics to improve accuracy. One of the key strengths of our approach, in fact, is its ability to exploit the motion dynamics of objects within a given clip , which is often ignored by other models, and separately decoding the object-centric and motion-centric information. Through our experiments, we show that our model outperforms existing methods on two separate datasets, Ego4D and EpicKitchens-100 ("Unseen Set"), as measured by several additional metrics, such as time to contact, and next-active-object localization. The code can be found on project page : ***/wacv24

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Kernelized covariance for action recognition

Kernelized covariance for action recognition

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International Conference on pattern Recognition

作者： Jacopo Cavazza Andrea Zunino Marco San Biagio Vittorio Murino Pattern Analysis & Computer Vision Istituto Italiano di Tecnologia Genova Italy Dipartimento di Ingegneria Navale Elettrica Elettronica e delle Telecomunicazioni Università degli Studi di Genova Genova Italy Istituto Italiano di Tecnologia Genova Liguria IT

ISBN: (纸本)9781509048489

In this paper we aim at increasing the descriptive power of the covariance matrix, limited in capturing linear mutual dependencies between variables only. We present a rigorous and principled mathematical pipeline to recover the kernel trick for computing the covariance matrix, enhancing it to model more complex, non-linear relationships conveyed by the raw data. To this end, we propose Kernelized-COV, which generalizes the original covariance representation without compromising the efficiency of the computation. In the experiments, we validate the proposed framework against many previous approaches in the literature, scoring on par or superior with respect to the state of the art on benchmark datasets for 3D action recognition.

关键词： Kernel Covariance matrices Hidden Markov models Data models Three-dimensional displays Activity recognition

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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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A Versatile Crack Inspection Portable System based on Classifier Ensemble and Controlled Illumination

A Versatile Crack Inspection Portable System based on Classi...

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International Conference on pattern Recognition

作者： Milind G. Padalkar Carlos Beltrán-González Matteo Bustreo Alessio Del Bue Vittorio Murino Pattern Analysis and Computer Vision (PAVIS) Istituto Italiano di Tecnologia Genova Italy Elettrica Elettronica e delle Telecomunicazioni University of Genova Italy Visual Geometry and Modelling (VGM) Istituto Italiano di Tecnologia Genova Italy University of Verona Verona Italy

ISBN: (纸本)9781728188089;9781728188096

This paper presents a novel setup for automatic visual inspection of cracks in ceramic tile as well as studies the effect of various classifiers and height-varying illumination conditions for this task. The intuition behind this setup is that cracks can be better visualized under specific lighting conditions than others. Our setup, which is designed for field work with constraints in its maximum dimensions, can acquire images for crack detection with multiple lighting conditions using the illumination sources placed at multiple heights. Crack detection is then performed by classifying patches extracted from the acquired images in a sliding window fashion. We study the effect of lights placed at various heights by training classifiers both on customized as well as state-of-the-art architectures and evaluate their performance both at patch-level and image-level, demonstrating the effectiveness of our setup. More importantly, ours is the first study that demonstrates how height-varying illumination conditions can affect crack detection with the use of existing state-of-the-art classifiers. We provide an insight about the illumination conditions that can help in improving crack detection in a challenging real-world industrial environment.

关键词： Training Visualization Tiles Lighting Inspection pattern recognition Ceramics

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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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