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22nd World Congress of the International Federation of Automatic Control (IFAC)

作者： Reith-Braun, Marcel Bauer, Albert Staab, Maximilian Pfaff, Florian Maier, Georg Gruna, Robin Laengle, Thomas Beyerer, Juergen Kruggel-Emden, Harald Hanebeck, Uwe D. Karlsruhe Inst Technol Intelligent Sensor Actuator Syst Lab D-76131 Karlsruhe Germany Tech Univ Berlin Chair Mech Proc Engn & Solids Proc D-10587 Berlin Germany Fraunhofer Inst Optron Syst Technol & Image Explo D-76131 Karlsruhe Germany

ISBN: (纸本)9781713872344

Optical sorters separate particles of different classes by first detecting them while they are transported, e.g., on a conveyor belt, and subsequently bursting out particles of undesired classes using compressed air nozzles. Currently, the most promising results are achieved by predictive tracking, a multitarget tracking approach based on extracted midpoints from area-scan camera images that analyzes the particles' motion and activates the nozzles accordingly. However, predictive tracking requires expert knowledge for setup and preceding object detection. Moreover, particle shapes are only considered implicitly, and the need to solve an association problem rises the computational complexity of the algorithm. In this paper, we present GridSort, an image-based approach that forecasts the scene at the nozzle array using a convolutional long short-term memory neural network and subsequently extracts nozzle activations, thus circumventing the aforementioned weaknesses. We show how GridSort can be trained in an unsupervised fashion and evaluate it using a coupled discrete element-computational fluid dynamics simulation of an optical sorter. We compare our method with predictive tracking in terms of sorting accuracy and demonstrate that it is an easy-to-apply alternative while achieving state-of-the-art results. Copyright (c) 2023 The Authors.

关键词： Monitoring of product quality and control performance Neural networks in process control Machine learning methods and applications Artificial intelligence in mining minerals and metals Process monitoring and fault diagnosis

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Perception Understanding Action: Adding Understanding to the Perception Action Cycle With Spiking Segmentation

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FRONTIERS IN NEUROROBOTICS 2020年 14卷 568319页

作者： Kirkland, Paul Di Caterina, Gaetano Soraghan, John Matich, George Univ Strathclyde Ctr Image & Signal Proc Elect & Elect Engn Neuromorph Sensor Signal Proc Lab Glasgow Lanark Scotland Leonardo MW Ltd London England

Traditionally the Perception Action cycle is the first stage of building an autonomous robotic system and a practical way to implement a low latency reactive system within a low Size, Weight and Power (SWaP) package. However, within complex scenarios, this method can lack contextual understanding about the scene, such as object recognition-based tracking or system attention. Object detection, identification and tracking along with semantic segmentation and attention are all modern computer vision tasks in which Convolutional Neural Networks (CNN) have shown significant success, although such networks often have a large computational overhead and power requirements, which are not ideal in smaller robotics tasks. Furthermore, cloud computing and massively parallel processing like in Graphic Processing Units (GPUs) are outside the specification of many tasks due to their respective latency and SWaP constraints. In response to this, Spiking Convolutional Neural Networks (SCNNs) look to provide the feature extraction benefits of CNNs, while maintaining low latency and power overhead thanks to their asynchronous spiking event-based processing. A novel Neuromorphic Perception Understanding Action (PUA) system is presented, that aims to combine the feature extraction benefits of CNNs with low latency processing of SCNNs. The PUA utilizes a Neuromorphic Vision sensor for Perception that facilitates asynchronous processing within a Spiking fully Convolutional Neural Network (SpikeCNN) to provide semantic segmentation and Understanding of the scene. The output is fed to a spiking control system providing Actions. With this approach, the aim is to bring features of deep learning into the lower levels of autonomous robotics, while maintaining a biologically plausible STDP rule throughout the learned encoding part of the network. The network will be shown to provide a more robust and predictable management of spiking activity with an improved thresholding response. The reported e

关键词： spiking convolution segmentation tracking STDP neuromorphic neural network asynchronous

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Experimental Evaluation of a Novel sensor-Based Sorting Approach Featuring Predictive Real-Time Multiobject Tracking

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2021年第2期68卷 1548-1559页

作者： Maier, Georg Pfaff, Florian Pieper, Christoph Gruna, Robin Noack, Benjamin Kruggel-Emden, Harald Langle, Thomas Hanebeck, Uwe D. Wirtz, Siegmar Scherer, Viktor Beyerer, Jurgen Fraunhofer IOSB D-76131 Karlsruhe Germany Inst Optron Syst Technol & Image Exploitat D-76131 Karlsruhe Germany Karlsruhe Inst Technol Intelligent Sensor Actuator Syst Lab D-76131 Karlsruhe Germany Ruhr Univ Bochum Dept Energy Plant Technol D-44801 Bochum Germany TU Berlin Mech Proc Engn & Solids Proc D-10623 Berlin Germany

sensor-based sorting is a machine vision application that has found industrial application in various fields. An accept-or-reject task is executed by separating a material stream into two fractions. Current systems use line-scanning sensors, which is convenient as the material is perceived during transportation. However, line-scanning sensors yield a single observation of each object and no information about their movement. Due to a delay between localization and separation, assumptions regarding the location and point in time for separation need to be made based on the prior localization. Hence, it is necessary to ensure that all objects are transported at uniform velocities. This is often a complex and costly solution. In this article, we propose a new method for reliably separating particles at nonuniform velocities. The problem is transferred from a mechanical to an algorithmic level. Our novel advanced image processing approach includes equipping the sorter with an area-scan camera in combination with a real-time multiobject tracking system, which enables predictions of the location of individual objects for separation. For the experimental validation of our approach, we present a modular sorting system, which allows comparing sorting results using a line-scan and area-scan camera. Results show that our approach performs reliable separation and hence increases sorting efficiency.

关键词： Automated visual inspection machine vision real-time multiobject tracking sensor-based sorting

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Radiation Hardening of Digital Color CMOS Camera-on-a-Chip Building Blocks for Multi-MGy Total Ionizing Dose Environments

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IEEE TRANSACTIONS ON NUCLEAR SCIENCE 2017年第1期64卷 45-53页

作者： Goiffon, Vincent Rolando, Sebastien Corbiere, Franck Rizzolo, Serena Chabane, Aziouz Girard, Sylvain Baer, Jeremy Estribeau, Magali Magnan, Pierre Paillet, Philippe Van Uffelen, Marco Mont Casellas, Laura Scott, Robin Gaillardin, Marc Marcandella, Claude Marcelot, Olivier Allanche, Timothe Univ Toulouse Image Sensor Res Team ISAE SUPAERO F-31055 Toulouse France Univ St Etienne Lab Hubert Curien UMR CNRS 5516 St Etienne France CEA DAM DIF F-91297 Arpajon France Fus Energy F4E Barcelona 08019 Spain OTL Abingdon OX14 1RL Oxon England

The Total Ionizing Dose (TID) hardness of digital color Camera-on-a-Chip (CoC) building blocks is explored in the Multi-MGy range using Co-60 gamma-ray irradiations. The performances of the following CoC subcomponents are studied: radiation hardened (RH) pixel and photodiode designs, RH readout chain, Color Filter Arrays (CFA) and column RH Analog-to-Digital Converters (ADC). Several radiation hardness improvements are reported (on the readout chain and on dark current). CFAs and ADCs degradations appear to be very weak at the maximum TID of 6 MGy(SiO2), 600 Mrad. In the end, this study demonstrates the feasibility of a MGy rad-hard CMOS color digital camera-on-a-chip, illustrated by a color image captured after 6 MGy(SiO2) with no obvious degradation. An original dark current reduction mechanism in irradiated CMOS image sensors is also reported and discussed.

关键词： Active pixel sensors (APS) CIS CMOS CMOS image sensors dark current deep submicron process (DSM) enclosed layout transistors (ELT) gamma gigarad image sensors integrated circuit interface states MGy monolithic active pixel sensor (MAPS) quantum efficiency rad hard radiation damage radiation effects radiation hard radiation hardening radiation tolerant radiation-hardened-by-design (RHBD) shallow trench isolation (STI) total ionizing dose (TID) trapped charge X-rays

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Extended Global-Local Representation Learning for Video Person Re-Identification

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IEEE ACCESS 2019年 7卷 122684-122696页

作者： Song, Wanru Wu, Yahong Zheng, Jieying Chen, Changhong Liu, Feng Nanjing Univ Posts & Telecommun Jiangsu Key Lab Image Proc & Image Commun Nanjing 210003 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Minist Educ Key Lab Broadband Wireless Commun & Sensor Networ Nanjing 210003 Jiangsu Peoples R China

Recently, person re-identification has become one of the research hotspots in the field of computer vision and has received extensive attention in the academic community. Inspired by the part-based research of image ReID, this paper presents a novel feature learning and extraction framework for video-based person re-identification, namely, the extended global-local representation learning network (EGLRN). Given a video sequence of a pedestrian, the holistic and local features are simultaneously extracted using the E-GLRN network. Specifically, for the global feature learning, we adopt the channel attention convolutional neural network (CNN) and the bidirectional long short-term memory (Bi-LSTM) networks, which are responsible for introducing a CNN-LSTM module to learn the features of consecutive frames. The local feature learning module relies on the key local information extraction, which is based on the Bi-LSTM networks. In order to obtain the local feature more effectively, our work defines a concept of "the main image group'' by selecting three representative frames. The local feature representation of a video is obtained by exploiting the spatial contextual and appearance information of this group. The local and global features extracted in this paper are complementary and further combined into a discriminative and robust feature representation of the video sequence. Extensive experiments are conducted on three video-based ReID datasets, including the iLIDS-VID, PRID2011 and MARS datasets. The experimental results demonstrate that the proposed method outperforms state-of-the-art video-based re-identification approaches.

关键词： Bi-directional LSTM feature extraction global-local feature representation person re-identification video

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Sign Language Recognition using Spiking Neural Networks 2

Sign Language Recognition using Spiking Neural Networks

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2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Chaudhari, Pranav Vicente-Sola, Alex Basu, Amlan Manna, Davide L. Kirkland, Paul Caterina, Gaetano Di Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India Neuromorphic Sensor Signal Processing Lab Centre for Image and Signal Processing Electrical and Electronic Engineering Department University of Strathclyde Glasgow United Kingdom

In recent years, research in automatic Sign Language Recognition (SLR) has undergone significant progress, serving as a founda-tional base for developing applications that aim to promote the integration of deaf individuals into society. Most of this progress is owed to the recent developments in deep learning. However, the deployment of conventional Artificial Neural Networks (ANNs) can be hindered by their requirements in terms of computational power and energy consumption. Therefore, to improve the ef-ciency of current SLR systems, in this work, we propose the use of the increasingly popular Spiking Neural Networks (SNNs), which, on the one hand, provide more energy-efficient computations than conventional ANNs and, on the other hand, are able to process temporal sequences with simpler architectures thanks to their temporal dynamics. To evaluate our method, we utilize WLASL300, the 300-word (300 classes of signs) dataset from Word-Level American Sign Language, and achieve an improvement in accuracy with the SNN (+2.70%) over the previous state-of-the-art, when working with energy-efficient spiking neurons. Furthermore, we construct a non-spiking version of the same network and evaluate it in a similar manner. Our results demonstrate how the SNN has sparser activations (25% less), thanks to the use of spiking neurons, and therefore can be implemented with a lower power requirement than an ANN version of the same architecture. This work thus demonstrates the possibility of performing SLR in a very effective and efficient way, thus opening up the development of applications that span from the automatic real-time translation of dynamic signs to remote control utilizing sign languages. © 2024 Elsevier B.V.. All rights reserved.

关键词： Neural networks

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Improving Multitarget Tracking Using Orientation Estimates for Sorting Bulk Materials 13

Improving Multitarget Tracking Using Orientation Estimates f...

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IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

作者： Pfaff, Florian Kurz, Gerhard Pieper, Christoph Maier, Georg Noack, Benjamin Kruggel-Emden, Harald Gruna, Robin Hanebeck, Uwe D. Wirtz, Siegmar Scherer, Viktor Laengle, Thomas Beyerer, Juergen Karlsruhe Inst Technol Intelligent Sensor Actuator Syst Lab ISAS Inst Anthropomat & Robot Karlsruhe Germany Ruhr Univ Bochum Dept Energy Plant Technol LEAT Bochum Germany Fraunhofer Inst Optron Syst Technol & Image Explo Karlsruhe Germany Tech Univ Berlin Mech Proc Engn & Solids Proc Lab MVTA Berlin Germany

ISBN: (纸本)9781509060641

Optical belt sorters can be used to sort a large variety of bulk materials. By the use of sophisticated algorithms, the performance of the complex machinery can be further improved. Recently, we have proposed an extension to industrial optical belt sorters that involves tracking the individual particles on the belt using an area scan camera. If the estimated behavior of the particles matches the true behavior, the reliability of the separation process can be improved. The approach relies on multitarget tracking using hard association decisions between the tracks and the measurements. In this paper, we propose to include the orientation in the assessment of the compatibility of a track and a measurement. This allows us to achieve more reliable associations, facilitating a higher accuracy of the tracking results.

关键词： Belts Target tracking Sorting Cameras Optical imaging Atmospheric measurements

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Fast encoding method using CU depth for quality scalable HEVC

Fast encoding method using CU depth for quality scalable HEV...

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IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA)

作者： Qingyang Ge Dong Hu Education Ministry's Key Lab of Broadband Wireless Communication and Sensor Network Technology Jiangsu Province's Key Lab of Image Procession and Image Communications Nanjing University of Posts and Telecommunications Nanjing CN

In this paper, a new fast encoding scheme using CU depth information for quality scalable HEVC was proposed. The proposed scheme based on the correlation between the base layer and enhancement layer, as well as the correlation between current frame and reference frame, which obtained by experiment. The main idea of the algorithm is that when each LCU is encoded in enhancement layer, it's corresponding encoded LCU's best coding depth in base layer is used as the new maximum encoding depth in enhancement layer, thereby, it can reduce the unnecessary CU division process then reduce the computational complexity. On the other hand, for base layer, when each LCU encoding in current frame, the corresponding LCU's depth in already coded reference frame is utilized to skip parts process of mode selection before reach this reference depth, so that the computational complexity can be reduced again. Experimental results show that compared with the original SHM5.1, our proposed method saves about 59.45% and 67.71% encoding time in whole encoding layers and enhancement layer respectively, with a negligible decrease in PSNR of 0.014 dB and bitrate increase of 0.46% in EL, as well as PSNR of 0.05 dB and bitrate increase of 1.15% in BL averagely.

关键词： Encoding Scalability Computational complexity Correlation PSNR Bit rate

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From Visual Spectrum to Millimeter Wave: A Broad Spectrum of Solutions for Food Inspection

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IEEE ANTENNAS AND PROPAGATION MAGAZINE 2020年第5期62卷 55-63页

作者： Becker, Florian Schwaebig, Christopher Krause, Julius Leuchs, Sven Krebs, Christian Gruna, Robin Kueter, Andries Laengle, Thomas Nuessler, Dirk Beyerer, Juergen Karlsruhe Inst Technol Vis & Fus Lab Karlsruhe Germany Fraunhofer Inst High Frequency Phys & Radar Tech Wachtberg Germany Koblenz Tech Univ Koblenz Germany Fraunhofer Inst Optron Syst Technol & Image Explo Karlsruhe Germany Karlsruhe Inst Technol Karlsruhe Germany Fraunhofer Inst Optron Syst Technol & Image Explo Vis Based Inspect Syst Business Unit Karlsruhe Germany Fraunhofer Inst High Frequency Phys & Radar Tech Dept Integrated Circuits & Sensor Syst Wachtberg Germany Karlsruhe Inst Technol Inst Anthropomat & Robot Karlsruhe Germany

The consequences of food adulteration can be far reaching. In the past, inexpensive adulterants were used to inflate different products, leading to severe health issues. Contamination of food has many causes and can be physical (plant stems in tea), chemical (melamine in infant formula), or biological (bacterial contamination). Employing suitable sensor systems along the production process is a requirement for food safety. In this article, different approaches to food inspection are illustrated, and exemplary scenarios outline the potential of different sensor systems along the spectrum.

关键词： Optical imaging Detectors Inspection Cameras Sorting Indium gallium arsenide

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A new inter prediction mode decision method based on temporal correlation for HEVC encoder

A new inter prediction mode decision method based on tempora...

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IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA)

作者： Qiuping Xu Dong Hu Education Ministry's Key Lab of Broadband Wireless Communication and Sensor Network Technology Jiangsu Province's Key Lab of Image Procession and Image Communications Nanjing University of Posts and Telecommunications Nanjing CN

To alleviate the encoder computation load in inter-frame coding of the High Efficiency Video Coding (HEVC), a new fast inter-frame prediction mode decision method is proposed by reducing the number of execution of the advanced motion vector prediction (AMVP) mode. Based on the correlation analysis between the corresponding located CU (Coding Unit) in the previous frame and the current CU, we first select the best one inter prediction mode by just performing merge mode decision process and then only perform the AMVP process on the selected one. Experimental results show that the proposed scheme provides 33.04% encoder time savings in Random Access (RA) configuration and 38.05% time savings in Low Delay (LD) configuration on average, and its increment in bit rate is 0.87% in RA and 1.29% in LD compared to the default encoding scheme in HM10.0.

关键词： Encoding Correlation Video coding Vectors Bit rate Algorithm design and analysis Copper

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